GPT-6.1 Terra now generally available in Microsoft Foundry: build agents that see and act
AI agents that read dense charts, navigate desktop apps, and process scanned documents are only as capable as the model behind them. OpenAI’s GPT-6.1 Terra, now generally available in Microsoft Foundry, delivers significant gains in visual reasoning and computer-use speed, so the apps and agents you build can see more and act faster. AI projects often slow down on identity, networking, governance, data handling, evaluation, and compliance. Microsoft Foundry brings these fundamentals together in Azure, so you can take GPT-6.1 Terra from prototype to production with speed and trust. What's new in GPT-6.1 Terra GPT-6.1 Terra is a quick, efficient model built for day-to-day professional tasks, with four core strengths: Stronger vision. Improved visual reasoning helps the model pick out the details that matter in product photos, technical drawings, floor plans, and application screens. Faster, more precise computer use. The model pairs what it sees with quick, accurate actions across browsers, desktop software, and apps, so agents can go from reviewing information to finishing the task. Clear writing. Summarizes what it finds in plain, approachable language and drafts natural-sounding emails and proposals for everyday work. GPT-6.1 Terra also provides: Large context window. Up to 1M input tokens. Multimodal input. Accepts text and images. Developer controls. Tune reasoning effort, including a none setting for latency-sensitive calls, and adjust verbosity to match response length to the task. Interim updates. When your orchestration layer supports it, the model can send interim messages to the user while it continues working toward a final response, keeping long-running tasks responsive. Where GPT-6.1 Terra fits GPT-6.1 Terra is built for work that starts with visual analysis and ends with action inside software: Forms, email, and proposals. Complete forms and draft emails and proposals as part of routine administrative work. Design and technical visuals. Review campaign assets for cropping and text placement and turn issues into a clear list of fixes or read technical drawings and update floor plans in the right tools. Visual analysis to action. Review what appears in images or on application screens and use those insights to drive the next step in a software workflow. Retail catalog upkeep. Match supplier product images to existing catalog entries, spot gaps in product details, and update listings in the browser. Video workflows. Find a specific moment in a video and make edits through video applications. GPT-6.1 Terra complements GPT-6 Astra. Astra reasons through open-ended, multi-step work and plans the job. Terra handles the high-volume visual and screen-based tasks inside it, at a lower price point. Built into the Foundry platform GPT-6.1 Terra works with the Foundry APIs and tools you already use. It’s available through the Responses API with no new SDK requirements or schema changes. From day one, GPT-6.1 Terra is available through Foundry Models in Global Standard deployments across all supported regions, and in Data Zone Standard deployments in US, EU and APAC. You can adopt the latest frontier model where you’ve already built and scaled your applications, without trading off performance for data residency. Watch the demo with Naomi Moneypenny for a platform-wide walkthrough of model selection, evaluation on your own data, and approaches to cost optimization. GPT-6.1 Terra Pricing (USD/1M tokens) Model Deployment Context Length Pricing (USD $/million tokens) Input Cached Input Cached Writes Output GPT-6.1 Terra Global Standard Short context $1.00 $0.05 $1.25 $5.00 Long context $2.00 $0.10 $2.50 $7.50 Data Zone Standard (US) Short context $1.10 $0.055 $1.375 $5.50 Long context $2.20 $0.11 $2.75 $8.25 Data Zone Standard (EU) Short context $1.20 $0.06 $1.50 $6.00 Long context $2.40 $0.12 $3.00 $9.00 Data Zone Standard (APAC) Short context $1.20 $0.06 $1.50 $6.00 Long context $2.40 $0.12 $3.00 $9.00 Learn more pricing details in here. Put GPT-6.1 Terra to work on your next AI app Deploy GPT-6.1 Terra in Microsoft Foundry today to build apps and agents that read complex visuals and act on screen faster. Some steps in your app need a fast, structured answer instead of generated text, such as classifying content or routing a request. For those, use the Decisions API. Last week, we announced the general availability of OpenAI’s Decisions API with GPT-6 Luna in Microsoft Foundry. Terra handles the visual and on-screen work, and the Decisions API makes the quick call on what happens next. Whichever you start with, you get development, evaluation, and governance on one platform, so you can move from prototype to production with confidence.
Building a Self-Updating RAG System: Handling Document Uploads, Updates and Deletions
The Hidden Challenge of Production RAG Imagine your organization has deployed an AI assistant to answer questions about employee policies. An employee asks, “How many days of annual leave am I entitled to?” The assistant responds confidently: “15 days.” There is just one problem. The company updated its policy last week. Employees are now entitled to 20 days. The updated PDF was uploaded successfully. The storage service is healthy. The search index is responding. The language model is generating answers without errors. Yet the answer is wrong. This is a different kind of production failure. Nothing appears to be broken at the infrastructure level. The failure lies in the relationship between the source document and the information the AI retrieves. Retrieval-Augmented Generation (RAG) systems depend on external knowledge to ground their responses. But that knowledge is not static. Policies change, contracts get revised, product documentation evolves, and outdated documents are removed. When the underlying documents change, the associated chunks, embeddings, metadata, and search-index entries must be managed accordingly. Otherwise, the system can continue generating plausible answers from information that is no longer valid. Building a RAG pipeline is a retrieval problem. Keeping it correct as data changes is a data lifecycle engineering problem. In this article, we will design a self-updating RAG system that handles document creation, updates, and deletion while addressing asynchronous processing, version consistency, failed jobs, and stale retrieval results. The goal is not simply to index documents automatically. It is to make sure that the right version of the right document is available to the right user when a query is executed. Understand the Problem Before Designing the Architecture A basic RAG pipeline is relatively straightforward: Load a document. Extract its text. Split the text into smaller chunks. Generate an embedding for each chunk. Store the chunks and embeddings in a searchable index. Retrieve relevant chunks when a user asks a question. Pass the retrieved context to a language model to generate an answer. This works well when the document collection is relatively static. The difficulty begins when the source data changes after indexing. Consider three common scenarios. Scenario A: A document is uploaded A new policy is added to the knowledge base. The system must process it and make its content searchable. Until indexing finishes, the document may not be available to the chatbot. Scenario B: A document is updated The original policy allows 15 days of annual leave. A revised policy allows 20 days. If the new version is indexed without correctly deactivating the old version, both versions may appear in search results. The language model may then select the wrong passage or combine conflicting information. Scenario C: A document is deleted An administrator removes a document from the application. However, deleting the original PDF from storage does not automatically delete the corresponding records from Azure AI Search. The content may remain searchable until its indexed chunks are removed. Azure AI Search document deletion itself can also take time to be reflected in index statistics. Microsoft Learn: Delete documents in Azure AI Search These scenarios reveal an important design principle: The source document, its metadata, the background processing jobs, and the search index must be treated as parts of one logical lifecycle—even though they are separate systems. We need a design that can handle failures between these systems without exposing outdated or unauthorized content. Design the System Around Clear Responsibilities Rather than making the upload API responsible for every operation, we can separate the system into a document management path, an asynchronous processing path, and a query path. The Proposed Architecture The design uses the following components: Component Responsibility Azure Blob Storage Stores the original document files Azure Cosmos DB Maintains document metadata, versions, and lifecycle state Azure Service Bus Queues ingestion and deletion jobs Background worker Extracts text, creates chunks, generates embeddings, and updates the index Microsoft Foundry Provides the models used for embedding generation and response generation Azure AI Search Stores searchable document chunks, metadata, and vectors FastAPI Exposes document management and chat APIs Microsoft Agent Framework Supports agentic query workflows when multi-step reasoning or tool use is required The background worker can be implemented as a separate Python service or another suitable compute component. It does not have to be part of the FastAPI process. Why separate these responsibilities? Suppose a user uploads a 100-page contract. The application may need to extract text, split thousands of characters into chunks, generate embeddings, and send multiple indexing requests. Performing all of that synchronously inside the upload request increases latency and makes the API vulnerable to timeouts. Instead, FastAPI accepts the request, persists the document and its metadata, and arranges for the work to be processed asynchronously. The worker performs the expensive operations independently. This allows ingestion to scale separately from query traffic and makes retries easier to manage. The query path remains separate: it retrieves eligible, relevant chunks and uses them to ground the model's response. Establish a source of truth Azure Cosmos DB should hold the authoritative lifecycle metadata in this design. Azure AI Search is the retrieval index, not the authority on whether a document is currently active. For example, a document record might look like this: { "id": "policy-123", "documentId": "policy-123", "currentVersion": 2, "indexedVersion": 1, "status": "PROCESSING", "blobPath": "policies/leave-policy.pdf" } Here, version 2 is the desired version, but version 1 is still the last successfully indexed version. The application must not assume that the latest upload has already become searchable simply because the metadata record has been updated. This distinction between the desired version and the indexed version is central to reliable lifecycle management. Implementing Document Uploads A reliable upload operation involves more than storing a file and sending a message to a queue. It must establish a durable record of the document, identify the version being processed, and ensure that background processing can be retried safely. Step 1: Accept and persist the document When a client uploads a document, FastAPI validates the request, checks the user's permissions, and stores the original file in Azure Blob Storage. The application then creates or updates the document metadata in Cosmos DB. At this stage, the document can be marked as PROCESSING until the ingestion pipeline completes. Step 2: Schedule background processing The system creates an ingestion job containing enough information for the worker to locate the source and determine exactly which version to process. For example: { "operationId": "op-789", "operation": "INGEST", "documentId": "policy-123", "documentVersion": 2, "blobPath": "policies/leave-policy-v2.pdf" } The worker should process the specified version rather than blindly reading whatever file happens to exist at a mutable storage path. For systems that overwrite the same blob path, use blob versioning, an immutable version-specific path, or another mechanism that ensures the worker reads the intended content. Step 3: Extract, chunk, and embed The worker downloads the appropriate document and extracts its text. The extracted text is divided into chunks. Each chunk should carry identifiers that connect it to its parent document and version. For example: { "id": "policy-123-v2-chunk-001", "documentId": "policy-123", "documentVersion": 2, "chunkNumber": 1, "content": "Employees are entitled to 20 days of annual leave." } The worker generates an embedding for each chunk and writes the chunk, its metadata, and its vector to Azure AI Search. The exact index schema depends on the retrieval strategy, embedding dimensions, filtering requirements, and whether hybrid search is used. Step 4: Mark the version as indexed After the worker confirms that all required indexing operations succeeded, it updates the document metadata to record the indexed version and processing status. For example: { "id": "policy-123", "currentVersion": 2, "indexedVersion": 2, "status": "ACTIVE" } This update must be conditional on the document still being at version 2. If a newer version has already been uploaded, the older job must not mark itself as the current version. Why idempotency matters Distributed systems can deliver the same job more than once. A worker may finish indexing but fail before acknowledging the queue message. The message can then be delivered again. If every attempt creates new chunk IDs, duplicate content may accumulate in the index. A practical solution is to derive chunk IDs deterministically from the document ID, version, and chunk identifier. Reprocessing the same version then targets the same index records instead of creating a new set each time. This does not eliminate every failure mode, but it makes repeated indexing much safer. Azure Service Bus supports duplicate detection for eligible tiers, but queue-level duplicate detection is not a substitute for idempotent worker logic. Microsoft Learn: Duplicate detection Updating Documents Without Serving Stale Content Document updates are the most interesting part of the lifecycle because they introduce a consistency problem. Imagine the current leave policy is version 1, allowing 15 days. An administrator uploads version 2, allowing 20 days. The worker starts processing version 2, but embedding generation takes time. Meanwhile, users continue querying the application. What should those users see? There are two common strategies. The right choice depends on whether the system prioritizes availability or strict freshness. Strategy A: Make the new version active after indexing succeeds In this approach, the existing active version remains available while the new version is processed. The workflow is: Create version 2 and mark it as processing. Extract text and generate embeddings for version 2. Index all required version 2 chunks. Verify indexing success. Atomically update the authoritative metadata to make version 2 active. Clean up version 1's indexed chunks asynchronously. This minimizes the time during which the document is unavailable. However, it means that version 1 can remain available while version 2 is being processed. That may be acceptable for some knowledge bases but inappropriate for policies that must become effective immediately. Strategy B: Disable the old version as soon as an update is accepted For high-sensitivity documents, the application can immediately mark the old version as ineligible for retrieval. It then processes the new version and activates it only after successful indexing. The trade-off is that the document may temporarily have no eligible version. The application should return a clear message or use an approved fallback rather than silently serving obsolete information. Prevent old jobs from overwriting new versions Consider this sequence: Version 2 starts processing. Version 3 is uploaded before version 2 finishes. Version 3 finishes indexing first. Version 2 finishes later and attempts to update the metadata. Without version checks, the late version 2 job could incorrectly reactivate stale content. The worker must therefore use conditional updates or optimistic concurrency checks against the authoritative document version. A job may complete its indexing work, but it must not activate its version unless it is still eligible to do so. Handle the lack of a cross-service transaction Cosmos DB and Azure AI Search do not participate in a single distributed transaction in this architecture. A metadata update can succeed while an indexing request fails. An indexing request can succeed while the subsequent metadata update fails. The system should account for these partial failures using: Explicit lifecycle states. Conditional version updates. Retryable and idempotent operations. Periodic reconciliation between metadata and indexed records. The important engineering principle is to make incomplete operations detectable and recoverable, rather than assuming every multi-step workflow succeeds atomically. Deleting Documents Safely Deletion requires special treatment because removing a document from the source does not automatically remove its indexed content. More importantly, waiting for physical index cleanup before preventing retrieval creates a window in which content that should no longer be available may still be returned. Step 1: Make the document ineligible for retrieval When an authorized deletion request arrives, the application updates the authoritative metadata to indicate that the document is being deleted or has been logically deleted. The retrieval path must check that status before using retrieved content. For sensitive documents, this check must happen before the content is added to the LLM context. Merely hiding a document in the user interface is insufficient. Step 2: Schedule physical cleanup The application enqueues a deletion job that identifies the document and all indexed records associated with it. The worker queries or otherwise identifies the corresponding chunk keys and deletes those records from Azure AI Search. The deletion operation should be idempotent. Retrying a deletion for an already-removed chunk should not cause the workflow to fail unnecessarily. Step 3: Verify cleanup The worker checks whether the targeted records have been removed and records the cleanup result. If an operation fails, the job should be retried according to a defined policy. Jobs that continue failing should be sent to a dead-letter queue for investigation and controlled recovery. Azure AI Search supports deleting indexed documents by their document keys. Its documentation also describes how to verify deletion and why index statistics may not update immediately. Microsoft Learn: Delete documents A simplified FastAPI deletion endpoint The following example illustrates the API boundary. The helper functions represent application-specific implementations. from fastapi import FastAPI, HTTPException app = FastAPI() @app.delete("/documents/{document_id}") async def delete_document(document_id: str): document = await get_document_metadata(document_id) if document is None: raise HTTPException( status_code=404, detail="Document not found" ) # Must enforce authorization and use a # concurrency-safe lifecycle transition. await mark_document_deleting(document_id) # The job must be published reliably. await enqueue_deletion_job( document_id=document_id, document_version=document["currentVersion"] ) return { "documentId": document_id, "status": "DELETING" } This is an illustrative example, not a complete production implementation. In particular, the metadata transition and message publication must be coordinated reliably. One approach is the transactional outbox pattern: write the lifecycle change and an outbox event in the same Cosmos DB transactional batch when the data model and partitioning allow it, then publish the event asynchronously. The publisher can retry without losing the intent to schedule cleanup. Deletion must also account for in-flight queries Suppose a query retrieves a document's chunks just before the deletion request is processed. The query might still hold those chunks in memory even after the document is marked deleted. For stricter deletion guarantees, the application needs a defined policy for in-flight requests, cache invalidation, and authorization checks immediately before content is sent to the model. If the requirement is that no response may contain content after a deletion takes effect, that guarantee must explicitly cover requests already in progress. Physical cleanup and retrieval eligibility are separate responsibilities. Both matter. Coordinate Lifecycle Changes Reliably Once ingestion and deletion run asynchronously, the queue becomes part of the system's correctness model. A production worker should not assume that each message is delivered exactly once or that messages for a particular document always finish in order. Design for at-least-once processing A practical worker lifecycle is: Receive a message using a lock-based delivery mode. Validate the document ID, version, and operation. Check whether the operation is still eligible to run. Perform idempotent indexing or deletion. Update lifecycle metadata conditionally. Acknowledge the message only after successful processing. If the worker fails before acknowledging the message, the message may be delivered again. That is expected behavior and should be handled by the design. Add retry and dead-letter policies Not all failures should be treated equally. Transient network errors or throttling may succeed after a delay. Invalid documents, unsupported file formats, and persistent schema mismatches may require manual investigation. The system should use bounded retries with appropriate backoff, record meaningful failure details, and route repeatedly failing messages to a dead-letter queue. Use reconciliation as a safety net Even with retries, the system needs a way to detect inconsistencies that escaped the normal workflow. A reconciliation job can compare authoritative metadata against indexed document versions and lifecycle states. It can identify cases such as: A document marked active whose current version is not fully indexed. Chunks belonging to an obsolete version. Search records for a logically deleted document. An operation that remained in a processing state beyond its expected duration. Reconciliation should repair only states it can safely determine. It should not reactivate a version simply because its chunks exist in the index. For applications using Azure Cosmos DB change feed, document metadata changes can also drive incremental background processing. The change feed captures inserts and updates in its standard latest-version mode; capturing deletions requires selecting and configuring an appropriate supported mode or using explicit lifecycle events. Microsoft Learn: Cosmos DB change feed Where Microsoft Agent Framework Fits A common mistake is to assume that every part of an agentic RAG application should be implemented as an AI agent. Document uploads, version transitions, embedding generation, and index deletion are predictable operations with explicit success and failure conditions. These are generally best implemented with conventional application logic and background workers. Microsoft Agent Framework becomes useful when the query itself requires multi-step reasoning, tool selection, or orchestration. For example, a user might ask: “Compare the current leave policy with the previous version and explain the changes that affect employees.” An agent could use approved tools to retrieve the current policy and the permitted historical version, compare the relevant passages, and generate an evidence-based explanation. The application must still control which documents and versions are eligible for retrieval. The agent should not independently decide whether a deleted document is authorized for use. This distinction is important: deterministic workflows protect data integrity; agentic workflows help solve tasks that benefit from reasoning and tool use. Microsoft Agent Framework provides both agent and workflow capabilities, allowing developers to choose an appropriate orchestration model for the task. Microsoft Agent Framework documentation Production Readiness: What Should We Measure? A pipeline that successfully indexes a document once is not necessarily production-ready. The system needs observable lifecycle states and measurable recovery behavior. Area What to validate Ingestion Can a failed upload or indexing job be retried safely? Idempotency Does processing the same document version twice create duplicate records? Version consistency Can a delayed job reactivate an older version? Deletion Can logically deleted content enter the LLM context? Authorization Are tenant and document-level permissions enforced during retrieval? Recovery Can reconciliation detect and repair partial failures? Observability Can operators identify stuck jobs, indexing failures, and cleanup delays? Performance How long does it take for an accepted update to become searchable? Useful operational metrics include ingestion success rate, queue age, indexing latency, retry counts, dead-letter queue depth, time spent in each lifecycle state, and deletion cleanup latency. Also test race conditions deliberately. Upload a new version while an earlier job is still running. Delete a document while a query is in flight. Simulate an indexing failure after metadata has been updated. Restart a worker after it has indexed content but before it acknowledges the message. These tests expose the problems that a happy-path demonstration will not reveal. Bringing It All Together Keeping a RAG system accurate is not simply a matter of re-running embeddings whenever a file changes. It requires a deliberate lifecycle design that connects source documents, versioned metadata, asynchronous processing, search-index operations, and retrieval-time authorization. Azure Blob Storage can preserve the source documents, Cosmos DB can track authoritative lifecycle state, Service Bus can distribute background work, and Azure AI Search can provide retrieval. Microsoft Foundry supplies the models, while Microsoft Agent Framework can support the parts of the application that genuinely benefit from agentic orchestration. The architecture becomes reliable when it assumes that failures will happen: messages may be repeated, jobs may finish out of order, index operations may partially succeed, and cleanup may take time. Version checks, idempotent operations, explicit lifecycle states, reliable event publication, and reconciliation turn those failure modes into manageable engineering problems. The objective is not merely to keep the index populated. It is to ensure that every answer is grounded in information that is current, eligible, and authorized for the user who asked the question. Further Reading Azure AI Search: Delete documents Azure Cosmos DB: Change feed Azure Service Bus: Duplicate detection Microsoft Agent Framework I'd be interested to hear from engineers building production RAG systems: how do you coordinate document updates and deletions across your source of truth and search index?Azure OpenAI Decisions API: Now generally available in Microsoft Foundry
AI applications and agents do more than generate content. They constantly make decisions about what should happen next. Which model should handle this request? Is a customer issue urgent? Did an agent’s output meet the required criteria? Should a workflow continue, try another approach, or involve a person? As agents become more sophisticated, these decisions can happen hundreds or even thousands of times across a workflow, and not every one of them requires a full generative response. On October 22, we brought the OpenAI Decisions API to Microsoft Foundry, giving developers a purpose-built way to make fast, focused decisions across AI applications and agentic workflows. The Decisions API evaluates text, images, and structured inputs and turns them into actionable choices, probabilities, and scores. It brings intelligence directly into the control flow of an application, helping developers determine what should happen next without relying on traditional generative inference for every step. This represents an important evolution in how models can be used within AI systems. Sometimes you need a model to create. Sometimes you need it to reason deeply across a complex problem. And sometimes, the most valuable thing a model can do is make a focused decision that helps the rest of the system move forward. Decision-making is becoming a core part of agentic applications Consider an AI customer service agent handling an incoming request. Before it generates a response, the application may need to understand what the customer is asking about, determine how urgent the issue is, identify which model or tool is best suited to handle it, and decide whether specialized expertise is required. Once a response has been generated, the application may need to evaluate it again, determining whether it adequately addressed the customer's issue, met the application's requirements, or should be escalated for human review. For an agentic workflow, that distinction can be significant. Before work begins, an application can evaluate an incoming request and route it to the right model or tool. During execution, an agent can assess possible actions and choose the appropriate next step. After another model completes a task, the application can evaluate the output against defined criteria and decide whether to continue, retry, take a different path, or bring a person into the workflow. Before work begins: Evaluate an incoming request and route it to the right model or tool. During execution: Assess possible actions and choose the appropriate next step. After a task completes: Evaluate the output against defined criteria and decide whether to continue, retry, take a different path, or bring a person into the workflow. These decisions may be small individually, but collectively they shape how an agent behaves. Giving developers a model designed specifically for these moments creates an opportunity to build systems that can make these choices efficiently while reserving more computationally intensive reasoning and generation for the parts of the experience that need them. Turn inputs into decisions your application can use The Decisions API gives developers a dedicated endpoint for evaluating inputs against questions they define. Each question supports one of three focused outcomes: a probability, a choice from a predefined set of options, or a score against a defined rubric. Instead of parsing generated text to determine what a model meant, application code can work with defined values designed to inform what happens next. Probability: Determine whether a condition is true and return a confidence value. Multiple choice: Select from a predefined set of options. Rubric scoring: Score an input against defined criteria. Instead of parsing generated text to determine what a model meant, application code can work with defined values designed to inform what happens next. Use the right model for the right moment What makes this especially powerful is the impact on real-world agentic applications. As agents become more sophisticated, they make hundreds or even thousands of decisions across workflows. The latency and cost of each decision can quickly compound. With the Decisions API, developers can bring intelligence directly into these critical decision points, from dynamically routing requests and prioritizing customer issues to evaluating agent outputs and deciding when human intervention is needed. This is an important shift from using AI primarily to generate content toward using AI to drive responsive, efficient, and intelligent systems. For developers, the opportunity isn't simply to build agents capable of doing more. It's to make those agents faster, more cost-efficient, and practical to operate at enterprise scale. That could mean routing a request to the appropriate model rather than automatically using a larger model, prioritizing customer issues before an agent begins working on them, or evaluating an agent's output before deciding whether a workflow can continue autonomously. In each of these cases, the model isn't responsible for producing the final experience. It's helping the application intelligently decide how that experience should unfold. Building AI systems that can decide what comes next The broader shift here is from thinking about AI primarily as something that generates an answer to thinking about intelligence as something that can be embedded throughout an application. Generative models remain essential for creating, reasoning, and interacting with users, but increasingly sophisticated agents also need intelligence between those moments to continuously determine what to do next. Developers can use it alongside other models to create workflows where reasoning, generation, evaluation, routing, and decision-making each happen where they make the most sense. As those workflows become longer and more autonomous, having a focused way to make the many smaller decisions along the way can become just as important as the models performing the largest tasks. Get started with the Decisions API For developers, the Decisions API is available through a dedicated /openai/v1/decisions endpoint on an Azure OpenAI resource and is available with GPT-6 Luna. Developers can call the endpoint using OpenAI SDKs or REST, pass their GPT-6 Luna deployment name in the model field, and authenticate using an API key or Microsoft Entra ID. Learn more about the Decisions API → Learn how it works here. Try the Decisions API in Microsoft Foundry → https://ai.azure.com As we continue expanding the models and capabilities available through Microsoft Foundry, I'm particularly excited about what this unlocks for developers building the next generation of AI applications. The opportunity isn't simply to give agents access to increasingly capable models. It's to build systems that can apply the right intelligence at the right moment, from the complex reasoning and generation that users see to the many decisions happening behind the scenes. The next generation of AI applications won't just be defined by how well they generate. They'll also be defined by how intelligently and efficiently they decide what to do next. Pricing (per 1M tokens) Decision API supported model Input Output Cached input 30-minute cached write GPT-6 Luna $0.10 N/A $0.01 $0.125 Start building with the Azure OpenAI Decisions API in Microsoft Foundry today to bring reliable, structured decision-making to your apps and agents.From Business Brief to Production Agent: Building in Three Stages, Not One
The problem with "AI can help my business" Every non-technical founder has heard some version of "you should put an AI agent on this." Far fewer have seen what it actually costs, in time and money, to find out whether that's true for their specific business — before committing to it. The honest answer is: it shouldn't cost much at all, if you build it in the right order. Most of the expensive AI project failures aren't failures of the model — they're failures of sequencing. Someone commits to real infrastructure, real hosting, and real integration work before anyone has confirmed the underlying idea even holds up. This post walks through a small, realistic project — an autonomous sales-development agent for a fictitious construction-tech company — built in three deliberate stages, each one cheap enough to abandon and each one answering a different question before the next stage spends any more money or time. The point isn't the specific agent. It's the order of operations: what to validate first, what to validate second, and what you only pay for once everything before it has already been proven. The Brief Hi, I'm looking for someone who can help me grow FieldForge, a SaaS platform specifically for general contractors, remodelers, and design-build companies. I'm not looking for generic lead lists. I need someone who can identify qualified construction companies, reach the owner/decision-maker, start conversations, and book demos that can turn into paying SaaS customers. Your experience with B2B lead generation and appointment setting looks relevant. I'd love to hear how you would approach generating qualified leads and growing a construction-focused SaaS. Translated into engineering terms, that's four capabilities: pull in new leads from a data feed (ingest_permits), decide whether each one is worth pursuing (qualify_lead), draft a personalized first-touch email for the ones that qualify (generate_pitch_email), and hand qualified leads off to an outbound sequence (dispatch_sequence). Nothing about that list requires committing to any infrastructure yet — which is exactly the point of Stage 1. Stage 1: Mock prototype — no AI, no backend, no cost The first stage has no real agent in it at all. It's a Vite + React console with the full intended UI — a strategy pane, a live-feed pane, a lead-dossier pane — wired to hand-written mock data and a fake setTimeout standing in for "the AI is thinking." No model calls, no cloud resources, no backend of any kind. It runs entirely in a browser tab: This is deliberately the cheapest possible way to answer the first real question: does this even make sense as a product, before a single token of inference is spent proving it? Is a 3-pane command console the right way to expose "an agent found and qualified these leads for you"? Is a permit-triggered pitch angle a good hook, or does it look contrived once it's actually on screen? Does a business owner look at this and understand what they're being offered? None of those questions need a real agent to answer — they need a UI a person can click through and react to. Running this stage costs essentially nothing beyond development time: no API calls, no cloud spend, nothing to tear down if the idea doesn't land. Where the AI pair programmer actually did the work. This entire stage — the layout, the state management, the mock data shapes, the interaction flow — was built by describing the product in plain language to an AI coding assistant (GitHub Copilot, Claude) and iterating on the generated code directly, rather than dragging components around in a low-code builder. That distinction matters more than it sounds like it should. A low-code prototype is fast to show, but it's still just a prototype — once a real agent needs to sit behind it, most of that first version gets rebuilt in real code anyway. A pro-code prototype, even a fake one, is a keepable asset: the exact same React components, the exact same UI shell, carry forward unchanged into Stage 2 and Stage 3. Nothing here gets rebuilt later — it gets connected to something real. What this stage proves, and what it deliberately doesn't. By the end of Stage 1, you know whether the product concept and the UX hold up — whether the thing you're describing to a non-technical stakeholder actually makes sense once it's on a screen in front of them. What it tells you nothing about: whether a real model can actually do the reasoning this UI is pretending to show. That's a completely different risk, and it's wrong to pay for cloud infrastructure to find out — it just needs a real agent, still running on a laptop: Stage 2: A real agent, still running on a laptop — the convincing part This is the stage that actually matters most, and it's also the cheapest place to find out if the whole idea works. The fakery gets ripped out and replaced with a genuine agent: a Microsoft Agent Framework Agent, backed by a real model deployment on Microsoft Foundry, given the same four tools from the brief — ingest_permits, qualify_lead, generate_pitch_email, dispatch_sequence — as real Python functions it can actually call. Its reasoning streams live into the same terminal-style pane the mock prototype already had, so nothing about the UI needs to change; only what's driving it does. Crucially, the frontend stays on a local dev server and the agent stays a local process on the same machine. There's no cloud hosting, no production auth, no deployment pipeline — the UI just talks to a locally running agent server over a simple streaming protocol. That's the deliberate design of this stage: the only thing worth testing here is whether the agent is actually good, and every question that matters — does it call tools in a sensible order, does it correctly decide which leads qualify, does its drafted email actually reflect the lead's specific trigger event rather than reading like a template — can be answered without touching a cloud resource. The qualify_lead tool is where this gets concrete: it's handed a target definition — commercial/residential remodelers in a specific revenue band, an active permit filing, still running manual/legacy tooling — and has to decide, per lead, whether it's a fit. You'll see this called an ICP (Ideal Customer Profile) constantly in sales and GTM circles right now, to the point of being a bit of a buzzword. Stripped of the jargon it's just this: a precise, written-down definition of a good-fit customer, specific enough that a model can apply it as consistently as a trained human rep would. The reason it matters isn't the term — it's that the agent's usefulness is bounded entirely by how precisely that definition is written, and this stage is exactly where you find out if yours is precise enough. This is also where model choice gets decided, cheaply. Swapping which Foundry model deployment sits behind the agent, or rewriting the system instructions, costs a restart and a few cents of inference — not a redeploy, not a new pipeline run. If gpt-4o isn't giving good enough qualification judgment, or the reasoning is inconsistent between runs, you find out here, in seconds per iteration, for the price of a handful of API calls. Compare that to discovering the same problem after cloud hosting, authentication, and a production pipeline are already built around it. That's why this stage — not Stage 1 — is the genuinely convincing one. Anyone can mock up a UI that looks like an agent found and qualified a lead. Stage 2 is where you can put a real, unscripted permit feed in front of a real model and watch it reason its way to a defensible qualify/reject decision and a genuinely personalized email, live, for the cost of a few model calls. That's the moment a skeptical stakeholder stops asking "does this actually work" and starts asking "when can we have it." What this stage proves, and what it deliberately doesn't. By the end of it, you know the agent itself is sound — right tools, right order, correct ICP logic, real personalization — for a cost barely above Stage 1's. What it hasn't touched: what happens when a real user hits this over the public internet, how auth works outside your own machine, whether the hosting model you eventually pick can actually serve this reliably. That's a different kind of work, and it starts now that there's finally something worth doing that work for. Stage 3: Quasi-production — translating a proven agent into a running service Stage 2 answers "does the agent work." Stage 3 answers a different question entirely: "can this be handed to someone who isn't on my laptop." That's not a continuation of Stage 2's work — it's a translation of it. The agent's logic, tools, and instructions don't change at all going into this stage; what changes is everything around them, as the same proven Agent gets repackaged into something that can actually run as a service instead of a local process. The agent itself deploys as a Foundry Hosted Agent via azd — no hand-written Dockerfile, no Container App config, no Bicep authored from scratch; Foundry's managed hosting owns the runtime, scaling, and session continuity (via previous_response_id chaining on the Responses protocol), and assigns the agent its own managed identity. The frontend and its API layer become an Azure Static Web App: the React build as static assets, plus a small same-origin Azure Function that reverse-proxies requests to the hosted agent. That proxy exists for a concrete reason — Foundry's hosted-agent endpoint has no CORS support, so a browser calling it directly would simply be refused; routing through a same-origin Function sidesteps that entirely, and keeps the real agent URL and credentials server-side instead of sitting in a public JS bundle. None of that is agent work. It's the operational work of turning a validated prototype into a real, addressable, network-reachable thing — the productionization step every agent eventually needs regardless of how it was built, and the part that's easy to underestimate if you haven't budgeted for it as its own phase. Framed plainly, this stage is where the project stops being "a demo that runs on my machine" and starts being "a small SaaS": something with a real URL, a real hosting bill, real auth, and a real deploy pipeline — everything that separates a working prototype from a thing you could actually put a customer in front of. To be clear about the boundary of "quasi": this is not a fully hardened production system. There's no custom domain, no persistent session store beyond what Foundry provides out of the box, and no load testing. What it is: proof that the exact agent validated in Stage 2 can be deployed, reached over a real network, and driven from a real hosted frontend without silently breaking — the last class of risk that only shows up once something leaves your own machine. What this stage proves, and what it deliberately doesn't. By the end of it, you know the whole pipeline holds together end to end, from a hosted UI through a same-origin proxy to a Foundry-managed agent and back, streaming, over a real deployment. What it doesn't prove: that it holds up under real production load, real customer data governance, or a real incident on-call rotation. Those are legitimate next questions — just not the ones this stage was built to answer, and not ones worth paying for before the first two questions were settled. The pattern, not just the project Line the three stages up and the shape becomes obvious: each one is deliberately the cheapest possible way to eliminate one specific class of risk before the next stage spends real money finding out the hard way. Stage 1 kills product/UX risk for the cost of dev time and zero infrastructure. Stage 2 kills agent-quality risk — the right tools, the right model, correct decisions — for the cost of a few model calls, and it's the stage that actually earns anyone's confidence that this is real. Stage 3 kills operational risk — real hosting, real network, real auth — by translating an already-proven agent into a running service, rather than building all of that around an agent nobody has validated yet. Collapse those stages — build the hosting and the agent and the UI all at once, betting everything up front — and you don't save time. You just move all three classes of failure into the same debugging session, at the point where they're most expensive to disentangle and hardest to tell apart. Staged this way, each phase is cheap enough to walk away from if it fails, and each one only gets built on top of a phase that already earned its place. There's a second half to this story that's deliberately not in this post: the data. Everything here ran on synthetic permit feeds and mock company data — a real deployment lives or dies on where its actual leads come from and how well they're structured for a model to reason over. That's its own story, and it gets a dedicated write-up of its own. Project reference Source code: https://github.com/olivierb123/lead-agent-demo Live demo: https://victorious-glacier-03340ff0f.3.azurestaticapps.netHow to make AI responses faster on Microsoft Foundry: lessons from 2,040 measurements
A reproducible Microsoft Foundry performance study covering prompt caching, multimodal input, tool orchestration, MCP lifecycle, Toolbox tool search, and Priority Processing - with correctness and reliability measured alongside latency.Choosing a real-time voice architecture on Microsoft Foundry: three enterprise patterns
A practical comparison of three real-time voice architectures on Microsoft Foundry, including implementation tradeoffs and four enterprise release gates for residency, networking, retrieval authorization, and tool credentials.Model Migration Process on Microsoft Foundry and Azure OpenAI
Every app built on an LLM will eventually move to a new model. The model you shipped may be retired, or a newer model may offer better quality, cost, or performance. Changing a model name in code from a retiring model such as gpt-4o to a newer one such as gpt-5.1 may take one line. That line hides a much larger migration. Model failures are often silent to the systems using the model and loud to users. Nothing crashes. Error rates stay flat. Every dashboard says the migration went fine. Meanwhile, responses change shape, summaries become longer and more hedged, JSON fields disappear, and tool calls fire in a different order. Users notice. Support queues grow. Downstream code that depended on the old behavior starts to break. A successful migration preserves the application's behavior or improves it in measurable ways. That requires a repeatable process to detect drift, adapt safely, and prove quality before broad rollout. The model migration process has six phases: Discover → Assess → Adapt → Validate → Roll out → Retire. This article explains what each phase looks like, which Microsoft Foundry tools support it today—including Azure OpenAI capabilities—and where teams still need to build around the platform. It then applies the process to a retail shopping assistant and points to additional resources in Go deeper at the end of this post! Why migrate now Every model has a retirement date. On Microsoft Foundry, generally available models typically ship with a retirement date about 18 months out, and older model families are actively replaced. For example, the model lifecycle and retirement schedule lists gpt-4o (2024-05-13) as retiring on October 1, 2026, with gpt-5.1 as its replacement. What happens at retirement depends on how you buy capacity: Standard, Global Standard, and Data Zone Standard (pay-as-you-go) deployments are auto-upgraded on a rolling, region-by-region schedule. You control the timing with versionUpgradeOption set to one of: OnceNewDefaultVersionAvailable, OnceCurrentVersionExpired, or NoAutoUpgrade. NoAutoUpgrade means the deployment stops working at retirement. Priority Processing follows the same path. Provisioned (PTU) deployments are not auto-upgraded. You migrate them yourself, either in-place (traffic moves over a 20–30 minute window with no downtime) or side-by-side (stand up the new deployment, test, shift traffic, delete the old one). Batch deployments follow the side-by-side path: deploy the new model, resubmit jobs, retire the old deployment. The developer problem is the same in every case: traffic eventually reaches a different model, but the platform cannot tell you whether the application still behaves as it did before. A responding endpoint does not prove that the app behaves correctly. A new model can change formatting, tone, tool-calling behavior, or JSON shape in ways that quietly break downstream code. When should I migrate? Start before the retirement date. Automatic upgrade handles the traffic transition for eligible deployments, but the team still owns behavioral validation. Provisioned deployments also require a manual migration. Microsoft typically makes a replacement available in Global Standard about 90 days before retirement, in provisioned regions about 30 days before retirement, and in standard regions about two weeks before retirement. That gives you time to evaluate the new model on your own terms. Retirement dates cannot be extended. You also do not need a deprecation notice to begin. If a newer model may improve quality, speed, or cost, run it through the process now. Waiting turns the switchover into a slow train wreck: responses drift, parsing becomes brittle, support tickets accumulate, and the team ends up debugging a model it did not choose on a date it did not pick. A deliberate migration makes the retirement date a formality and creates a process the team can reuse. Who this is for This process fits teams that own an LLM-powered feature inside a larger application and run migrations deliberately. It also applies to AI-native platform teams that centrally manage models for other application teams. The phases remain the same, though platform teams may run them faster and in parallel rather than in sequence. Fine-tuned workloads are out of scope here because they cannot be upgraded automatically, have separate training and deployment retirement schedules, and turn the Adapt phase into a distillation or retraining exercise rather than primarily prompt work. The six phases Phase Definition What success looks like Discover Learn that a model change is coming or needed. The team receives a timely, structured signal with the deprecation date, replacement model, and migration window. Assess Choose a target model and confirm that it is operationally available. The team understands the candidates and confirms capacity, region, and SKU before tuning starts. Adapt Replay the current workload on the new model, diagnose changes, and update prompts, parameters, tool definitions, output schemas, and calling code. The team runs side-by-side replay against real or representative traffic, can see the behavioral differences, and records every change. Validate Run the adapted workload against a quality rubric and decide whether it is safe to ship. The team has an evaluation suite that is affordable to run and trusted by application owners and reviewers. Roll out Promote the model through staged production exposure, monitor live behavior, and commit or roll back. Canary or weighted routing is in place, live quality is measured alongside latency and errors, and rollback remains possible. Retire Decommission the old deployment, free capacity, archive evaluation artifacts, and update internal documentation. The old SKU is gone, the deployment count falls, and the team carries what it learned into the next migration. Foundry tools at a glance Microsoft Foundry provide tools for each phase of the Model Migration Process. Phase Microsoft Foundry feature (including Azure OpenAI) Documentation Discover Model retirement schedule, lifecycle policy, Service Health alerts, and Models API lifecycleStatus Model retirement schedule Lifecycle policy Assess Model leaderboards and benchmarks for quality, safety, cost, throughput, and latency; trade-off charts; side-by-side comparison; suggested replacements Model leaderboards and benchmarks Side-by-side compare Adapt Prompt Optimizer in the Foundry Agent playground; agent optimization; simulator for synthetic data Prompt Optimizer Agent optimization Simulator Validate Azure AI Evaluation SDK with 30+ evaluators, LLM-as-judge, graders, and the portal evaluation wizard Azure AI Evaluation SDK Portal evaluation Roll out Automatic upgrade and versionUpgradeOption; provisioned in-place or side-by-side migration; continuous evaluation; Azure Monitor alerts Auto-upgrade with versionUpgradeOption Continuous evaluation Retire Models API to confirm 410 Gone; observability dashboard to track deployment count Models API Observability dashboard Breakdown of each phase 0. Prepare the test dataset Before starting the six phases, build a set of representative inputs, expected outputs, and agreed success criteria. This dataset gates the middle of the lifecycle: Adapt needs inputs for replay, and Validate needs ground truth and scoring criteria. Step 0 describes the workload rather than the candidate model, so it can begin during Discover, before the team selects a target. Build the dataset from captured production traffic or domain examples in .csv or .jsonl. If representative data is not available, use the simulator to generate synthetic inputs. Two practices determine whether this work pays off: Instrument capture before you need it. Production content capture is opt-in and never retroactive. Log prompts, responses, latency, and token counts now so the team has traffic to evaluate later. Freeze the dataset. Keep inputs, ground truths, and success criteria fixed throughout the migration. If they change, source and target results are no longer comparable. You also need an inventory of the model deployments your workload uses, including their deployment types (Standard, Provisioned, or Batch). For each source model, note its retirement date and suggested replacement from the Model retirement schedule. 1. Discover Discover begins when something forces the team to consider a model change: a deprecation notice, a new generally available model, a cost or latency problem, or a capability gap. The phase ends with a decision to begin migration or stay on the current model if it remains stable, performs well, and is not approaching retirement. Foundry tools. The model lifecycle and retirement schedule publishes retirement dates and suggested replacements. The Azure OpenAI model retirements documentation explains notification timing, including at least 60 days for generally available model retirements and at least 30 days for preview model retirements. It also explains how to configure Azure Service Health advisories and use the Models API for programmatic lifecycleStatus and deprecation checks. Those APIs provide the foundation for an internal discovery system. Where it breaks. Customers may learn about a retirement through email, a service health alert, or a production error. By the time the right team sees the signal, it may already be deep into the deprecation window and heading toward retirement. What your team provides. The schedule and Models API expose the data through a stable contract. Mature enterprises may add a thin notification layer that routes it to the right owners. 2. Assess The team chooses a candidate target model and confirms that it is usable: the correct region and SKU, enough quota, and availability alongside the current model so rollback remains possible. Assess also includes projecting monthly cost against historical traffic. Pricing structures change between model generations through reasoning tokens, cached input, structured-output overhead, and other factors. Those changes can move unit economics by 2x or more. For regulated workloads, compliance requirements such as BAA, FedRAMP, and regional Standard versus Global Standard availability may narrow the candidate list before quality testing begins. Foundry tools. Start with the replacement suggested in the retirement schedule, then build a shortlist with model benchmarks, which compare quality, safety, cost, throughput, and latency. Use trade-off charts such as quality versus cost and the side-by-side model comparison for up to three models. Compare context windows, feature support such as function calling, structured output, and vision, and available endpoints. Confirm SKU, region, quota, and upgrade mechanics in the model retirements documentation. Where it breaks. Teams face several plausible candidates, such as gpt-5.1, gpt-5.2, and a nano variant, without clear positioning between them. A selected model may be unavailable in the required region or SKU, a constraint that sometimes appears only after planning is underway. Historical traffic may also show that the new model costs substantially more, forcing an unplanned budget decision. What your team provides. Public benchmarks should filter the candidate list, not make the final decision. Confirm the shortlist against the team's own workload. Build the monthly cost view from token logs and current pricing. 3. Adapt Adapt is often the most time-consuming phase for embedded and product-facing workloads. Validate may take longer for regulated workloads. First, replay the existing workload on the new model without changing it. This isolates changes caused by the model. Diagnose shifts in verbosity, reasoning depth, structured-output adherence, tool-call shape, and latency. Then update the application until it recovers or improves on the previous behavior. Prompt editing is only one part of Adapt. A migration often changes four other surfaces: Parameters. temperature, top_p, max_tokens, and reasoning-effort controls may not map directly between generations. Some are unsupported by newer model families. Tool definitions. Argument names, descriptions, and required fields that reliably guided the old model may need clearer wording or tighter constraints. Output schemas. Structured-output behavior changes between models. A schema the old model followed loosely may need explicit constraints, or the new model may finally enforce it. Calling code. API and SDK differences, including Chat Completions versus Responses, streaming formats, and new or renamed request fields, can require code changes. Downstream parsers may also assume the old response shape. For agentic and workflow workloads, schema and tool-call changes can outweigh prompt changes. Foundry tools. Prompt Optimizer is available through the Optimize button below the system instructions field in the Agent playground. It restructures instructions, explains each change by paragraph, and supports iteration. For example, a team can add a constraint such as "keep the JSON schema exactly" and optimize again. It is a fast first pass for a prompt that would otherwise be rewritten by hand. For agent workloads, agent optimization tunes instructions, tools, and model selection together. Prompt Optimizer and agent optimization are available in Microsoft Foundry, not Azure OpenAI. When production data is unavailable, the simulator can generate synthetic and adversarial inputs. Where it breaks. Most migration time is spent in a manual diagnosis loop. Teams rerun prompts by hand, compare outputs by eye, and rarely record what changed or why. For agent builders, chat benchmarks may miss tool-call regressions such as extra fields, renamed arguments, or changed call sequences. Those problems appear only when the team replays real agent traces. Plan for three constraints: Start with the optimizers, then verify their output. They apply general practices in a single pass rather than fitting changes to the team's dataset. They tune instruction text, not tool definitions or output schemas. Copy the original prompt first because there is no version history, then evaluate the optimized prompt against the frozen dataset. Expect more manual work when moving between providers or model families. There is no "optimize for target model X" flow. Moving from one family to another, such as OpenAI to Claude, still requires deliberate prompt and schema translation. Record traffic before you need it. Replay is only as useful as the captured data. Existing traces are available as an evaluation source for agents today, while content capture is opt-in and never retroactive. Log prompts, responses, latency, and tokens now to prepare for the next Adapt phase. 4. Validate Run the adapted workload against a quality rubric on the frozen dataset. The rubric may combine rules, LLM-as-judge evaluation, human review, existing user-feedback signals, or a domain-specific scoring framework. Examples include a clinical summarization rubric for healthcare or a tool-call sequencing assertion for agents. Validation produces a pass-or-fail decision for production exposure. AI-native teams may run the same signal continuously on every commit rather than treating it as a one-time gate. The dataset is a dependency for both Adapt and Validate. Build and freeze it early, around Assess, even though its primary purpose belongs to this phase. Validation then has two touchpoints: Before Adapt, freeze the dataset and success criteria, then run the current model to establish the source baseline. After Adapt, run the target model against the same dataset and evaluators, compare it with the source baseline, and make the release decision. Prepare the evaluation runner early and apply the gate after Adapt. Both steps belong to Validate. Foundry tools. The Azure AI Evaluation SDK, installed with pip install azure-ai-evaluation, includes more than 30 evaluators. They cover grounding, relevance, retrieval, coherence, fluency, question answering, reference-based similarity, F1, BLEU, ROUGE, safety, agent behavior, and Azure OpenAI graders. Teams can also build custom LLM-as-judge evaluators for task-specific rubrics. The portal evaluation flow runs the same evaluators against model, agent, dataset, and trace targets. Run identical evaluators against source and target outputs on the frozen dataset so the results remain comparable. Measure the three dimensions used for sign-off: Quality: evaluator results Latency: leaderboard time to first token and throughput, plus operational latency from the workload Cost: (input tokens × input price) + (output tokens × output price) Where it breaks. Most teams do not have an evaluation suite. Teams that do often built it themselves and may not use platform evaluation tools. Regulated workloads add mandatory human review, which can become the bottleneck. For those teams, migrations often stall in Validate rather than Adapt. What your team provides. The evaluators are ready to run, but model workloads still require teams to curate a domain-relevant test set from production traffic. That is why Phase 0 pays for itself. 5. Roll out Promote the validated configuration in stages: non-production, then a canary or weighted percentage of production traffic, followed by broader exposure. Compare live latency, errors, and quality signals with the pre-migration baseline, then commit or roll back. Some workloads cannot expose a new model to customer traffic during testing, including flows involving protected health information or financial transactions. Use shadow or mirror mode instead: run the new model offline against production inputs and compare its outputs with the old model without affecting users. Foundry tools. Migration mechanics depend on the deployment SKU: Standard, Global Standard, and Data Zone Standard deployments upgrade automatically on a rolling schedule. Control timing with versionUpgradeOption: OnceNewDefaultVersionAvailable, OnceCurrentVersionExpired, or NoAutoUpgrade. Priority Processing follows the same path. Provisioned, Global Provisioned, and Data Zone Provisioned deployments migrate manually, either in place during a 20-to-30-minute Azure-managed traffic transition or through side-by-side deployments. Batch deployments migrate side by side. Deploy the new model, resubmit jobs, then retire the old deployment. Fine-tuned deployments do not upgrade automatically. They follow separate training and deployment retirement schedules, so plan retraining or distillation early. See the model retirements documentation for deployment-specific guidance. Use continuous evaluation to score a sample of production traffic in the Foundry Observability dashboard. Connect evaluation results to traces for root-cause analysis and configure Azure Monitor alerts for quality regressions. Where it breaks. Offline evaluation can miss production quality and latency regressions. Rollback decisions may also be forced by deprecation deadlines rather than evidence. What your team provides. Teams implement weighted routing between deployments in their application or gateway layer. They must also choose how long to keep the old deployment warm for rollback. Embedded copilot teams often target about 30 days. Design both mechanisms once and reuse them for future migrations. 6. Retire Retire is easy to forget. Decommission the old deployment, free its capacity, archive evaluation artifacts, update internal documentation, and communicate the change to downstream owners. That may include customer-facing documentation, marketing pages, support runbooks, and audit logs. Regulated workloads may need to retain artifacts for years. Retirement is also a governance step. Foundry tools. Use the Models API to confirm that the old version is retired through lifecycleStatus or 410 Gone. Use the observability dashboard to confirm that the active deployment count falls. Add useful production traces to the golden dataset so the next migration starts with better evidence. Where it breaks. Teams skip the phase. Zombie deployments accumulate, leaving teams with structural debris from migrations they never finished. What your team provides. The observability dashboard shows deployment count, but the team must decide which deployments still carry traffic. Create an explicit retirement ticket rather than relying on someone to remember. Embedded copilot teams also need to update public claims such as "powered by gpt-4o" after the model changes. Worked example: Zava's Shopping Assistant migrates from gpt-4o mini to gpt-5.x Zava is a fictional retailer used as a stand-in for a real customer story. The example reflects patterns observed in customer-facing embedded AI workloads. Zava's Shopping Assistant is one of the company's largest LLM workloads. It has two LLM stages: Per-review insight extraction identifies sentiment, attribute mentions, and defect signals across thousands of product reviews. Product-level summaries present those findings to shoppers on the product page. Together, the two stages account for a meaningful share of Zava's token volume. Discover Zava's central AI Platform team made gpt-5.x models available internally and notified feature teams. The Shopping Assistant team learned about the models through that channel and received a target migration window before gpt-4o mini's deprecation. Zava has an internal discovery layer built on the retirement schedule and Models API. Microsoft provides the underlying data, while Zava routes the signal to application owners. Assess The team compared gpt-5.4 nano, which offered lower latency and cost, with gpt-5.1 and gpt-5.2 using leaderboard trade-off charts. Selection remained difficult because of the rapid release cadence, unclear positioning between variants, and the lack of a behavioral benchmark for product question-and-answer workloads. Capacity planning required coordination with the AI Platform team. Both gpt-4o mini and the gpt-5.x candidate needed to remain available in the same regions so the team could roll back. Adapt The team ran its existing Shopping Assistant prompts against gpt-5.4 nano using a sanitized traffic sample. Customer queries were scrubbed of personally identifiable information before replay. The behavioral comparison found three problems: Summaries used more hedged language and sometimes contradicted the underlying review evidence, creating a shopper-trust risk. Insight counts varied across runs. The model sometimes extracted substantially more or fewer attribute mentions than gpt-4o mini, affecting downstream filtering. Latency varied more than expected on the synchronous product-page path. Prompt Optimizer helped restructure the summary prompt, but the team still diagnosed the differences manually. It built its own replay system and behavioral comparison on top of captured traffic. Reengineering took weeks and extended beyond prompts: parameters and downstream parsing for the insight-extraction output also changed. Validate The team scored outputs with Zava's Product Answer Quality (PAQ) rubric. Its nine criteria cover factual grounding, attribute accuracy, tone, and refusal behavior for questions outside the catalog. Zava implemented the rubric as custom evaluators in the Azure AI Evaluation SDK. Initial evaluations used unchanged prompts to isolate model behavior. The team reran them after each prompt change. Zava's QA team also completed a manual review, which the company requires for every new model used in a customer-facing workflow. The per-review insight extraction stage still has no automated evaluation, a gap the team has accepted for now. Roll out The validated configuration moved to non-production and then through staged exposure: employees first, followed by a small percentage of shoppers. The canary exposed latency regressions that offline evaluation had missed. The team rolled back the latency-sensitive synchronous product-page path while keeping the offline pregeneration path on the new model. Retire Retirement is not complete. The gpt-4o mini deployment remains warm for rollback. The team must resolve the synchronous-path latency regression before retiring it, which sends that code path back to Adapt. This split state is common. Retire can lag Roll out by weeks or months, and the old deployment remains visible in deployment-sprawl data. Lessons from the example Adapt consumed most of the schedule. Replay tooling, behavioral comparison, and prompt reengineering are the clearest opportunities for Microsoft to shorten migrations. Validate worked because Zava had invested in it. Most customers do not have an equivalent to PAQ. Making domain-specific evaluations cheaper to build would improve confidence in this phase. The migration is partially live and partially rolled back. The process must support split-state workloads rather than assuming a binary switch from old model to new. How to use this process The six phases can serve as a checklist and an interview script for teams creating or auditing a migration process. Documentation and process: Lead with the six phases. Most teams recognize them immediately. Investment priorities: Start with Adapt and Validate. Across customer stories, those phases consume the most time and confidence. Interviews and postmortems: For each phase, ask whether it happens, who owns it, which tool the team uses, where it failed last time, and what evidence would increase confidence. Metrics: Discover and Retire are the easiest phases to instrument through measures such as announcement reach and active deployment count. Adapt and Validate require purpose-built telemetry that most teams do not yet have. Go deeper Two companion resources turn the process into concrete implementation steps: Microsoft Learn guide: This article follows the six phases through identifying affected deployments, preparing a test dataset, adapting prompts, evaluating source and target models, and rolling out by deployment type. Foundry Models Accelerator: This community toolkit includes a deployment-inventory scanner, a feasibility and assessment playbook, code and API migration audit scripts, an A/B evaluation runner with golden datasets, and rollout guidance. It follows the same six-phase process. The Foundry Models Accelerator is a community-built toolkit provided as-is under the MIT License. It falls outside Microsoft Support. Always verify model availability and retirement dates against official documentation. Additionally, check out the Foundry Forgebook which hosts a plethora of recipes that walk through the required code changes to migrate from different source to target models, even across model families. The goal of a model migration is to change the model without changing your application’s behavior, or to change it measurably for the better. Lead with the six phases, invest first in Adapt and Validate phases, and treat the Retire phase as a governance step.
Making Azure AI Foundry Agents Explainable — Knowledge Graphs + Source Attribution
90% of Azure AI demos work on stage — most never ship. The gap is architecture, not the model. I wrote up a field guide on taking Azure AI Foundry agents from POC to production, with knowledge graphs (GraphRAG) doing the grounding and source attribution. What the post covers Grounding with a knowledge graph — multi-hop retrieval + inline citations so every answer is traceable to a source (critical for regulated/medical use cases). Externalized state — keeping agent memory and session state outside the model. Identity at the boundary — Entra ID / RBAC instead of trusting the prompt. Observability & compliance — tracing, evaluation, and auditability. The stack — Azure AI Foundry + Model Context Protocol (MCP) + Azure Functions (Flex Consumption) + Azure OpenAI, from a real build (VeritasGraph medical MCP server). 📖 Full write-up: https://bibinprathap.com/blog/azure-ai-proof-of-concept-to-production ▶️ 6-min walkthrough: https://youtu.be/z-CPS5WUvyw?si=fmpo1RN28Bh9KbIb Questions for the community How are you grounding Foundry agents today — vector RAG, GraphRAG, or hybrid? Anyone combining knowledge graphs with MCP tools in Foundry? What worked / broke? For regulated domains, how are you handling source attribution and audit trails? Curious to compare notes — happy to share more detail on the GraphRAG retrieval design if useful.Model router updates: new regions, a refreshed model pool, and understanding the hill climb
Across Microsoft, "hill climbing" has become shorthand for how real AI progress happens: not in one dramatic leap, but through a disciplined loop. Microsoft AI defines the hill climb as an organization that continuously improves, cycle after cycle, through more compute, better data, and sharper evaluation. Reinforcement fine-tuning in Foundry defines it as improving the deployable model package one measured step at a time across quality, latency, and cost. Different altitudes, same premise: progress is not a one-shot decision. It's a loop. For most teams, the decision of what model to use when is made manually or with custom routing tools. A developer picks a model based on benchmarks, familiarity, or the last launch that made headlines, ships it, and revisits the choice only when something breaks. In an ecosystem where the frontier moves monthly, that decision goes stale fast. Model router in Foundry Models brings the hill climb to the selection layer. What's new: a bigger pool, in more places This release expands where teams can deploy model router, broaden the supported model pool, and delivers updates through a stable endpoint. Together, these changes help teams run production workloads in more locations, match a wider range of tasks to suitable models, and adopt supported updates without changing the application integration. A refreshed model pool. The supported model list now includes Anthropic Claude Opus 4.8 — a high-capability model built for complex reasoning and long-form generation, for scenarios that demand depth, structure, and quality — and the GPT-5.6 family. Just as importantly, the pool is pruned: gpt-5-chat, gpt-5.2-chat, gpt-5.3-chat, Deepseek-V3.1 have been removed from the model router as models reach the end of their lifecycle and are deprecated in Foundry. New region availability. The model router is now available in 28 regions for global standard and 21 data zone regions. For many organizations, inference requests must stay within specific geographic boundaries for regulatory, governance, or customer-trust reasons — and intelligent routing shouldn't force a compromise on that. Find the full list of regions here. The most important detail is what you don't have to do: these updates occur automatically*. The endpoint remains stable as the supported model pool is refreshed, so teams do not need to redeploy the model router to receive the update. Applications can continue using the same integration while the model router evaluates requests against the current supported pool. Teams should continue monitoring routing traces and application outcomes to confirm that quality, cost, latency, and governance requirements are met. *Models from Anthropic still need to be deployed separately before they can be routed to through the model router. Interested in hearing more about what's new to the model router? Tune in for the next episode of Model Mondays with Sanjeev Jagtap and Lee Stott, where they talk all things model router from evaluations to hill climbing. Sign up here to watch live or view the replay: Model Mondays - Spotlight On Model router in Microsoft Foundry | Microsoft Reactor The selection-layer hill climb At the selection layer, a step is a routing decision. Each one is a micro-optimization against your objective, and each one is instrumented: every response from the model router includes a model field showing which underlying model was selected, so the climb leaves a complete, auditable trail. Model router supports three parts of the optimization loop: A/B testing to compare two router configurations to understand quality, cost, and latency tradeoffs; model decomposition to use routing results to decompose a single-model application into a multi-model or multi-agent design, and continuous routing to keep the router in production for continuous per-request selection. Each pattern turns model choice into a measured, repeatable process rather than a fixed decision. 1. A/B Testing Question: Which model or routing strategy should I use in production? A/B testing helps teams compare candidate models, model families, or router configurations against the same workload. Representative traffic is sent to competing deployments, and teams compare quality, cost, latency, and governance outcomes. The goal is to understand tradeoffs and identify the model or routing strategy that best meets workload requirements before promoting it to production. 2. Model Decomposition Question: What work is my application actually doing? Model decomposition uses model router as a diagnostic tool. By deploying the model router against a representative workload and examining routing telemetry, teams can see how requests naturally separate into different task classes. Simple retrieval, classification, and summarization requests may route to smaller models, while reasoning, planning, and agentic workflows may require more capable models. The goal is not to choose a winner, but to understand the structure of the workload and uncover opportunities for optimization, specialization, or architectural improvements. 3. Route continuously Question: Why choose a single model at all? Route continuously is the pattern model router was designed for but is not limited to. Rather than treating model selection as a one-time decision, teams leave the model router in production and allow the best-fit model to be selected for each request. As the supported model pool, regional availability, and platform capabilities evolve, teams can continue using the same endpoint while evaluating whether updates improve workload outcomes. Model selection becomes an ongoing optimization process rather than a project that must be repeated every time the model landscape changes. Together, these patterns illustrate a broader shift: the model router is more than a model. It is a tool for the optimization loop itself, helping teams evaluate tradeoffs, understand workload behavior, test hypotheses, and continuously refine model selection as requirements evolve. Whether used to compare candidate models, decompose applications into specialized tasks, or automate per-request routing in production, model router turns model selection into an observable, measurable, and repeatable process. As the model landscape continues to change, that optimization loop becomes a durable advantage. Getting Started Ready to start your own hill climb? Whether you're exploring the model router for the first time, evaluating routing strategies against your workload, or building a long-term optimization practice, these resources can help you move from experimentation to production with Microsoft Foundry. What's new in model router? Sign up for the next Model Mondays episode for a deep dive into new features, optimization patterns, and the latest model router updates. How do I build agents with model router? Check out the Model Router Agents Lab and build agent experiences with routing, retrieval, web search, tool calling, and multi-agent patterns. How do I evaluate model router? Compare model router against baseline models using your own prompts, then review quality, cost, latency, and routing decisions with the Auto Evaluation Toolkit. How do I optimize model router for my workload? Start your hill-climbing journey with the Model Mastery workshop, where you'll test one optimization lever at a time and measure how each change impacts workload outcomes. How do I build a model router optimization playbook? Explore the Model Releases repository to track new capabilities, understand the optimization question behind each release, and try focused notebooks that demonstrate one optimization lever at a time.
