The AI job boom continues: Build the skills that move business forward
Discover new AI powered business Certifications to validate the skills that matter most. Gretchen LaBelle: Copilot + Agents Learning Portfolio Manager, Global Skilling Tarek Saleh Eldin: Content Publishing Manager, Global Skilling In Part 1 of this series, The AI job boom is here. Are you ready to showcase your skills?, we explored how Microsoft Certifications across AI, cloud, and security are evolving to keep pace with a rapidly changing job market. AI is no longer a niche capability. It’s becoming foundational across roles, reshaping how work gets done, and redefining how professionals create impact. This post picks up that thread. As organizations move from experimenting with AI to operationalizing it at scale, big changes are happening in business solutions roles. These shifts demand the ability to apply AI in real business contexts, redesign processes, build intelligent apps and agents, and lead transformation responsibly across the organization. Earlier this year, Microsoft introduced four new AI business solutions Certifications: Microsoft Certified: Agentic AI Business Solutions Architect (Exam AB‑100) Microsoft 365 Certified: Copilot and Agent Administration Fundamentals (Exam AB‑900) Microsoft Certified: AI Business Professional (Exam AB‑730) Microsoft Certified: AI Transformation Leader (Exam AB‑731) Together, these Certifications map to the critical roles in an AI‑driven workplace, from business practitioners and IT administrators to solution architects and transformation leaders. Building on that foundation, we’re launching new Certifications to help amplify human skills for AI-powered roles in the business landscape. New AI business solutions credentials: April 2026 and beyond Additional new Certification exams begin rolling out in beta starting in April 2026, with more releases over the following months, and going live later this year. The Microsoft Certified: AI Agent Builder Associate Certification is designed for tech pros, including developers, AI engineers, and architects, who are pushing AI agents beyond out‑of‑the‑box scenarios to implement production‑ready Microsoft Copilot Studio agents and multi‑agent solutions capable of sophisticated processes, workflow automation, and enterprise integration. Exam AB-620 beta and training available in April 2026; exam expected to go live in June 2026. The Microsoft Certified: Dynamics 365 Contact Center AI Engineer Associate Certification is designed for contact center engineers and solutions pros who design and run modern contact center as a service (CCaaS) solutions with Dynamics 365 Contact Center and service‑oriented autonomous agents. This Certification goes beyond routing and channels, focusing on how AI, Microsoft Copilot, and agents deliver scalable, always‑on service across voice and digital channels. Exam AB-250 beta and training available in June 2026; exam expected to go live in August 2026. The Microsoft Certified: Dynamics 365 Sales AI Consultant Associate Certification is for modern sellers who design and operationalize AI‑powered sales solutions across the lead‑to‑cash lifecycle, emphasizing Copilot-driven productivity and insights, AI-powered opportunity research and qualification, agent configuration and lifecycle management, and secure, scalable automation aligned with governance and responsible AI. Exam AB‑210 beta and training available in May 2026; exam expected to go live in June 2026. The Microsoft Certified: Intelligent Applications Builder Associate Certification equips Microsoft Power Platform pros to build for an AI-first world, where apps, agents, automation, and models work as one. It validates the skills to use Copilot and natural language to design intelligent solutions, embed agents across experiences, and ship responsibly with strong governance and application lifecycle management. Exam AB-410 beta and training available in April 2026; exam expected to go live in June 2026. The Microsoft Applied Skills: Build an agent-first app credential validates learners’ ability to build an app that surfaces a Copilot Studio agent and to craft prompts that make the agent genuinely effective, not just functional. This credential is part of the broader Microsoft Certified: Intelligent Applications Builder Associate (Exam AB-410) Certification journey, serving as a fast, accessible entry point for those looking to get started with agent-first development before pursuing the full Certification. Credential and training expected to go live in June 2026. Refresh: The Microsoft Certified: Power Platform Fundamentals Certification (Exam PL-900) is being updated with a streamlined, one-day instructor-led course and a new training program. These improvements are designed to align with the AI-powered Microsoft Power Platform and to make it easier than ever for learners to start building confidently. Training and courseware updates are scheduled for June 2026, with minor exam changes planned at the same time to reflect these enhancements. Retiring Certifications: What you need to know We’re committed to keeping our Certifications portfolio aligned with latest technology. As we launch new Certifications, we also retire some older credentials to keep the portfolio mapped to evolving roles. The following table itemizes what’s changing and provides key dates for Certification and training retirements in 2026. If your Certification is eligible for renewal, please renew it before the retirement date. Retiring Microsoft Credential Credential and exam retirement date Planned training retirement date Related new Credential Microsoft Certified: Dynamics 365 Customer Experience Analyst Associate (Exam MB-280) July 31, 2026 July 31, 2026 Microsoft Certified: Dynamics 365 Sales AI Consultant Associate (Exam AB-210) Microsoft Certified: Power Platform Functional Consultant Associate (Exam PL-200) August 31, 2026 August 31, 2026 Microsoft Certified: Intelligent Applications Builder Associate (Exam AB-410) Microsoft Certified: Dynamics 365: Finance and Operations Apps Solution Architect Expert (Exam MB-700) June 30, 2026 June 30, 2026 No new Certification is planned. To stay up to date with these technologies please refer to the Microsoft technical documentation. Microsoft Certified: Power Platform Solution Architect Expert (Exam PL-600) June 30, 2026 June 30, 2026 Microsoft Certified: Dynamics 365 Supply Chain Management Functional Consultant Expert (Exam MB-335) June 30, 2026 June 30, 2026 Microsoft Certified: Dynamics 365 Field Service Functional Consultant Associate (Exam MB-240) June 30, 2026 June 30, 2026 Microsoft Certified: Power Automate RPA Developer Associate (Exam PL-500) June 30, 2026 June 30, 2026 Microsoft Applied Skills: Create and manage model-driven apps with Power Apps and Dataverse June 30, 2026 June 30, 2026 Microsoft Applied Skills: Build an agent-first app Note: The recently released Microsoft Certified: Agentic AI Business Solutions Architect Certification (Exam AB‑100), although not a direct replacement for the retiring Certifications listed here, is the flagship expert‑level Certification, spanning a significantly broader scope and covering agentic architectures, AI‑driven solution design, and end‑to‑end business impact. If you currently hold one of the retiring expert-level Certifications (associated with Exam MB‑700, Exam PL‑600, Exam MB-335, Exam MB-240, or Exam PL-500), consider pursuing the Microsoft Certified: Agentic AI Business Solutions Architect Certification (Exam AB‑100) as your next step. Navigating the transition: FAQs The following questions and answers can help you determine how these retirements and expanded portfolio could impact your learning journey: Q. Why is Microsoft implementing these updates? A. Microsoft Credentials are valuable, as is the time you spend earning them. With the ongoing evolutions in technology, it’s essential that we keep the credentials up to date so we can help you stay aligned with latest skills and trends. We’re implementing these updates to provide a valuable path forward to keep up with the latest skills. Q. I’ve already earned one of the retiring Certifications. What happens now? A. If you’ve already earned any of the retiring Certifications, your credential remains valid until it expires. Retirement does not revoke or invalidate Certifications that were earned while the exam was active. They show your continued dedication to staying up to date and learning new skills in this ever-changing technical landscape. Q. What if a Certification that’s retiring is part of the prerequisites for an expert-level Certification? A. If a retiring Certification is required for an expert-level Certification, the requirements for that expert-level Certification will be updated as needed. The retiring Certification will be removed from the requirements and replaced (as appropriate) with a new associate-level Certification. If you’ve earned an expert-level Certification by earning an associate-level Certification that’s now retiring, you’ll continue to hold the expert-level Certification as long as you renew the expert-level Certification when it’s eligible. After you’ve earned a Certification, and if you renew it when it’s eligible, you hold it until it expires. If you’ve earned an associate-level Certification that’s a requirement for an expert-level Certification and that associate-level Certification hasn't expired, it can still satisfy the expert-level requirement. Be sure to meet all the requirements for the expert-level Certification before the associate-level Certification expires. Expired Certifications cannot be used to meet the requirements for an expert-level Certification. Q. Can I renew a soon-to-retire Certification? A. Yes, as long as it’s eligible for renewal and you renew it before the Certification officially retires, you can renew a soon-to-retire Certification. Please note that Fundamentals Certifications don’t expire. Q. Is there a direct transition path from a retiring Certification that I’ve already earned to the related new Certification, or do I need to pass the new exam? A. To earn the new Certification, you need to pass the new exam, since the new exam and the old one don’t measure the same skill sets. Q. I’m preparing for an exam that’s retiring. What should I do? A. The time you spend preparing for an exam and earning a Certification never goes to waste. If you’re actively preparing for an exam that’s retiring and a replacement exam has been announced: If you’ve already registered for the exam, you can continue preparing for it and take it while it’s still available. Keep in mind that after the exam retires, you won’t be able to retake it if you don’t pass, and you won’t be able to renew it. Exam registration ends on the same day that the exam retires. If you haven’t registered for the exam and there’s a related new exam, we strongly recommend that you prepare for and take the new exam instead, as noted in the following table. If you’re not close to testing for this exam Prepare for and take this exam instead Learning path and instructor-led training expected to be available in Exam MB-280 Exam AB-210 April 2026 Exam PL-200 Exam AB-410 April 2026 If you’re preparing for Exam PL-900 to earn the Microsoft Certified: Power Platform Fundamentals Certification, the new Course PL-900 will be available at the end of June 2026. If you’re preparing for Exam MB-335 to earn the Microsoft Certified: Dynamics 365 Supply Chain Management Functional Consultant Associate Certification, Exam MB-700 to earn the Microsoft Certified: Dynamics 365: Finance and Operations Apps Solution Architect Expert Certification, or Exam PL-600 to earn the Microsoft Certified: Power Platform Solution Architect Expert Certification, consider preparing for and taking Exam AB-100 to earn the Microsoft Certified: Agentic AI Business Solutions Architect Certification, the new flagship expert‑level Certification for solutions architects, available now. Note that to earn the Certification, you must pass Exam AB-100 and you must also have a current associate-level Certification. Q. How might these updates impact partner competency requirements? A. To track whether and how these updates might impact partner competency requirements, go to Solutions Partner for Business Applications in the Partner Center. The bigger picture AI is transforming not only what technology can do but also who does the work and how. Whether you’re building agents, designing intelligent apps, transforming sales, or leading enterprise AI strategy, there’s now a Certification that reflects the real skills your role demands. These Certifications can help ensure that you’re not only ready for AI-driven work but you’re also leading it. We’ll share updates for the new AI business solutions Certifications, including beta exams and go-live dates, on The Skills Hub Blog. Stay tuned! Explore more Microsoft Credentials on AI Skills Navigator.Why Collecting User Feedback on Your AI Agent Actually Matters
Hi everyone, I see many of us experimenting with AI agents in Copilot Studio and other platforms. Spinning up an agent is now the easy part but making sure it actually helps users is much harder. In a short blog, I shared why listening to users should be part of your AI design, not an afterthought. I talk about: Using thumbs up/down, comments, and simple surveys Turning feedback into a backlog of improvements Why this feedback loop is essential for making AI agents truly useful If you’re building or maintaining AI agents, I’d love your thoughts and experiences. 🔗 Read the blog: Why Collecting User Feedback on Your AI Agent Actually Matters https://medium.com/@sajeda27/why-collecting-user-feedback-on-your-ai-agent-actually-matters-54deea4fee7bJoin us at Microsoft 365 Copilot Live Expo and Discovery event in Huntsville, AL!
The Microsoft 365 Copilot Live Expo and Discovery event in Huntsville, AL features hands-on demos, expert sessions, and real-world use cases showcasing AI-driven productivity, Microsoft Copilot capabilities, and modern workplace innovation. The event takes place on May 19-21 at Redstone Arsenal, Huntsville, AL.
Designing a Governed RTO Compliance Agent Using Copilot Studio and Databricks Genie
Enterprise AI adoption in HR scenarios comes with a unique challenge: how do you deliver actionable insights without compromising privacy, trust, or policy boundaries? In this blog, I’ll share how we built an RTO (Return‑to‑Office) Compliance Agent using Microsoft Copilot Studio and Databricks Genie, focusing on governance‑first design, controlled data access, and real‑world enterprise constraints. This solution was developed as part of an HRLT proof‑of‑value initiative and is designed to support people managers with clear, aggregated compliance insights, delivered conversationally inside Microsoft Teams. The Problem We Were Solving As hybrid work models mature, organizations need a reliable way to answer questions such as: How compliant is my team with RTO expectations? Are there trends across regions or time periods? Traditional dashboards often fall short because they: Require manual interpretation Expose too much granular data Are difficult to govern at scale Our objective was to create an AI‑powered conversational interface that provides: Only manager‑authorized, aggregated insights Zero visibility into individual‑level behavior Built‑in enforcement of HR and privacy policies Architecture Overview The solution integrates Copilot Studio with Databricks Genie, backed by curated data sources. (Image: High-level Copilot Studio and Databricks Genie architecture) Key Components Copilot Studio – Conversational orchestration, policy enforcement, and Teams deployment Databricks Genie – Governed natural-language interface to curated datasets RokFusion Platform – Trusted HR and badge-swipe data This layered approach ensures governance is applied before data is ever queried. Controlled End-to-End Data Flow The interaction pattern follows a strict, auditable flow: A manager asks a question in Copilot Studio Copilot forwards the request to Genie with instruction constraints Genie executes logic only on curated, approved tables Calculations are performed at team or manager level only Copilot formats and returns compliant responses (text, tables, or charts) At no point are employee IDs, badge events, or individual metrics exposed. Using Genie as a Governance Layer, Not Just a Query Tool One of the most critical decisions was to treat Databricks Genie as a policy‑enforcement layer, not merely a natural‑language SQL generator. (Image: Genie instruction configuration enforcing compliance rules) What We Configured in Genie Synonyms and NL mappings for HR terminology Strict filtering logic for employee categories Population threshold enforcement (minimum count) Explicit rejection of sensitive attributes such as gender, race, religion, or age Prevention of formula or row‑level data exposure This approach ensured that even malformed or risky prompts could not bypass policy constraints. Compliance Scenarios Supported The agent supports multiple business‑aligned interpretations of RTO compliance: Hybrid Compliance Hybrid employees counted only on eligible hybrid days Onsite Compliance Onsite employees counted across standard working days All Employees View Weighted aggregation combining hybrid and onsite logic These scenarios are embedded into the agent’s instruction logic, not dynamically inferred at runtime—ensuring consistency and auditability. Why We Chose Conversational AI Over Dashboards A key insight early on was that managers don’t want spreadsheets—they want answers. Instead of navigating filters and charts, managers can ask: “What was my team’s compliance last week?” “Show me a comparison across regions.” When required, the agent can also render simple visual outputs. (Image: Sample Microsoft Teams output with compliance visualization) Importantly, visuals follow the same governance rules as text responses. Publishing and Validation in Microsoft Teams Once configured, the agent was published directly from Copilot Studio to Microsoft Teams, making adoption frictionless. (Image: Publishing Copilot Studio agent to Microsoft Teams) End‑to‑end testing validated: Authorization boundaries Population rules Safe handling of incomplete or ambiguous queries Key Engineering Learnings Governance must be instruction‑driven Relying on frontend filtering alone is insufficient for HR data. Natural language needs strong guardrails Enterprise AI benefits from being constrained, not free‑form. Aggregation builds trust Managers are more comfortable with insights when they know individual visibility is impossible. Copilot Studio accelerates enterprise delivery Security, deployment, and integration stay within the Microsoft ecosystem. Closing Thoughts This RTO Compliance Agent demonstrates how Copilot Studio and Databricks Genie can be used to build governed, enterprise‑ready AI solutions—especially in sensitive domains like HR. By embedding policy into architecture, instructions, and data access, we were able to deliver: Useful insights Strong privacy guarantees High user trust This pattern is extensible well beyond RTO—opening the door for future HR intelligence use cases built on the same foundation.
Copilot Studio Knowledge Source Limitation When Iterating Over Multiple SharePoint Documents
Hi, I’m looking for clarification on a limitation we’re currently encountering in Copilot Studio that is blocking some of our use case. Example Scenario (Policy Agent) We have a SharePoint document library containing ~100 policy documents. A Copilot Studio agent is configured with this library as a knowledge source. The agent performs well for typical question-answering scenarios where responses can be derived from a subset of documents. For example: “How much annual leave can I take?” correctly returns answers sourced from multiple relevant policies. Issue When the question requires the agent to evaluate all documents individually, the results are incomplete. Example prompt: “Review each policy document and return the review date.” In this scenario: The agent only processes the first ~10 documents. It then stops, without indicating that the response is partial or that a limit has been reached. The remaining documents in the library are not evaluated. During a recent Microsoft-led course, we were advised that this behaviour is expected due to platform limitations. Specifically: While it will reside over all documents to genereate the most suitable response, the agent is not designed to self‑iterate across all items in a large knowledge source for individual document responses. Asking it to “review each document” effectively requires iteration, which is constrained. The suggested workaround was to: Create a trigger-based flow Implement a loop to process the documents in batches We were able to make this approach work, but it feels like a heavy and brittle workaround for what seems like a common enterprise requirement. We’ve Tried Both available SharePoint knowledge source connection methods Allowing sufficient time for indexing and refresh Rephrasing prompts to encourage broader coverage None of these approaches changed the outcome, the agent consistently returns results for only the first subset of documents. Is this behaviour a documented or known limitation of Copilot Studio knowledge sources? Are there recommended design patterns for scenarios that require document-by-document evaluation at scale? Is there a more native or supported approach planned to avoid custom looping logic for this kind of use case? Any guidance or confirmation would be appreciated. Thanks.
Three tiers of Agentic AI - and when to use none of them
Every enterprise has an AI agent. Almost none of them work in production. Walk into any enterprise technology review right now and you will find the same thing. Pilots running. Demos recorded. Steering committees impressed. And somewhere in the background, a quiet acknowledgment that the thing does not actually work at scale yet. OutSystems surveyed nearly 1,900 global IT leaders and found that 96% of organizations are already running AI agents in some capacity. Yet only one in nine has those agents operating in production at scale. The experiments are everywhere. The production systems are not. That gap is not a capability problem. The infrastructure has matured. Tool calling is standard across all major models. Frameworks like LangGraph, CrewAI, and Microsoft Agent Framework abstract orchestration logic. Model Context Protocol standardizes how agents access external tools and data sources. Google's Agent-to-Agent protocol now under Linux Foundation governance with over 50 enterprise technology partners including Salesforce, SAP, ServiceNow, and Workday standardizes how agents coordinate with each other. The protocols are in place. The frameworks are production ready. The gap is a selection and governance problem. Teams are building agents on problems that do not need them. Choosing the wrong tier for the ones that do. And treating governance as a compliance checkbox to add after launch, rather than an architectural input to design in from the start. The same OutSystems research found that 94% of organizations are concerned that AI sprawl is increasing complexity, technical debt, and security risk and only 12% have a centralized approach to managing it. Teams are deploying agents the way shadow IT spread through enterprises a decade ago: fast, fragmented, and without a shared definition of what production-ready actually means. I've built agentic systems across enterprise clients in logistics, retail, and B2B services. The failures I keep seeing are not technology failures. They are architecture and judgment failures problems that existed before the first line of code was written, in the conversation where nobody asked the prior question. This article is the framework I use before any platform conversation starts. What has genuinely shifted in the agentic landscape Three changes are shaping how enterprise agent architecture should be designed today and they are not incremental improvements on what existed before. The first is the move from single agents to multi-agent systems. Databricks' State of AI Agents report drawing on data from over 20,000 organizations, including more than 60% of the Fortune 500 found that multi-agent workflows on their platform grew 327% in just four months. This is not experimentation. It is production architecture shifting. A single agent handling everything routing, retrieval, reasoning, execution is being replaced by specialized agents coordinating through defined interfaces. A financial organization, for example, might run separate agents for intent classification, document retrieval, and compliance checking each narrow in scope, each connected to the next through a standardized protocol rather than tightly coupled code. The second is protocol standardization. MCP handles vertical connectivity how agents access tools, data sources, and APIs through a typed manifest and standardized invocation pattern. A2A handles horizontal connectivity how agents discover peer agents, delegate subtasks, and coordinate workflows. Production systems today use both. The practical consequence is that multi-agent architectures can be composed and governed as a platform rather than managed as a collection of one-off integrations. The third is governance as the differentiating factor between teams that ship and teams that stall. Databricks found that companies using AI governance tools get over 12 times more AI projects into production compared to those without. The teams running production agents are not running more sophisticated models. They built evaluation pipelines, audit trails, and human oversight gates before scaling not after the first incident. Tier 1 - Low-code agents: fast delivery with a defined ceiling The low-code tier is more capable than it was eighteen months ago. Copilot Studio, Salesforce Agentforce, and equivalent platforms now support richer connector libraries, better generative orchestration, and more flexible topic models. The ceiling is higher than it was. It is still a ceiling. The core pattern remains: a visual topic model drives a platform-managed LLM that classifies intent and routes to named execution branches. Connectors abstract credential management and API surface. A business team — analyst, citizen developer, IT operations — can build, deploy, and iterate without engineering involvement on every change. For bounded conversational problems, this is the fastest path from requirement to production. The production reality is documented clearly. Gartner data found that only 5% of Copilot Studio pilots moved to larger-scale deployment. A European telecom with dedicated IT resources and a full Microsoft enterprise agreement spent six months and did not deliver a single production agent. The visual builder works. The path from prototype to production, production-grade integrations, error handling, compliance logging, exception routing is where most enterprises get stuck, because it requires Power Platform expertise that most business teams do not have. The platform ceiling shows up predictably at four points. Async processing anything beyond a synchronous connector call, including approval chains, document pipelines, or batch operations cannot be handled natively. Full payload audit logs platform logs give conversation transcripts and connector summaries, not structured records of every API call and its parameters. Production volume concurrency limits and message throughput budgets bind faster than planning assumptions suggest. Root cause analysis in production you cannot inspect the LLM's confidence score or the alternatives it considered, which makes diagnosing misbehavior significantly harder than it should be. The correct diagnostic: can this use case be owned end-to-end by a business team, covered by standard connectors, with no latency SLA below three seconds and no payload-level compliance requirement? Yes, low code is the correct tier. Not a compromise. If no on any point, continue. If low-code is the right call for your use case: Copilot Studio quickstart Tier 2 - Pro-code agents: the architecture the current landscape demands The defining pattern in production pro-code architecture today is multi-agent. Specialized agents per domain, coordinating through MCP for tool access and A2A for peer-to-peer delegation, with a governance layer spanning the entire system. What this looks like in practice: a financial organization handling incoming compliance queries runs separate agents for intent classification, document retrieval, and the compliance check itself. None of these agents tries to do all three jobs. Each has a narrow responsibility, a defined input/output contract typed against a JSON Schema, and a clear handoff boundary. The 327% growth in multi-agent workflows reflects production teams discovering that the failure modes of monolithic agents topic collision, context overflow, degraded classification as scope expands are solved by specialization, not by making a single agent more capable. The discipline that makes multi-agent systems reliable is identical to what makes single-agent systems reliable, just enforced across more boundaries: the LLM layer reasons and coordinates; deterministic tool functions enforce. In a compliance pipeline, no LLM decides whether a document satisfies a regulatory requirement. That evaluation runs in a deterministic tool with a versioned rule set, testable outputs, and an immutable audit log. The LLM orchestrates the sequence. The tool produces the compliance record. Mixing these letting an LLM evaluate whether a rule pass collapses the audit trail and introduces probabilistic outputs on questions that have regulatory answers. MCP is the tool interface standard today. An MCP server exposes a typed manifest any compliant agent runtime can discover at startup. Tools are versioned, independently deployable, and reusable across agents without bespoke integration code. A2A extends this horizontally: agents advertise capability cards, discover peers, and delegate subtasks through a standardised protocol. The practical consequence is that multi-agent systems built on both protocols can be composed and governed as a platform rather than managed as a collection of one-off integrations. Observability is the architectural element that separates teams shipping production agents from teams perpetually in pilot. Build evaluation pipelines, distributed traces across all agent boundaries, and human review gates before scaling. The teams that add these after the first production incident spend months retrofitting what should have been designed in. If pro-code is the right call for your use case: Foundry Agent Service The hybrid pattern: still where production deployments land The shift to multi-agent architecture does not change the hybrid pattern it deepens it. Low-code at the conversational surface, pro-code multi-agent systems behind it, with a governance layer spanning both. On a logistics client engagement, the brief was a sales assistant for account managers shipment status, account health, and competitive context inside Teams. The business team wanted everything in Copilot Studio. Engineering wanted a custom agent runtime. Both were wrong. What we built: Copilot Studio handled all high-frequency, low-complexity queries shipment tracking, account status, open cases through Power Platform connectors. Zero custom code. That covered roughly 78% of actual interaction volume. Requests requiring multi-source reasoning competitive positioning on a specific lane, churn risk across an account portfolio, contract renewal analysis delegated via authenticated HTTP action to a pro-code multi-agent service on Azure. A retrieval agent pulled deal history and market intelligence through MCP-exposed tools. A synthesis agent composed the recommendation with confidence scoring. Structured JSON back to the low-code layer, rendered as an adaptive card in Teams. The HITL gate was non-negotiable and designed before deployment, not added after the first incident. No output reached a customer without a manager approval step. The agent drafts. A human sends. This boundary low-code for conversational volume, pro-code for reasoning depth maps directly to what the research shows separates teams that ship from teams that stall. The organizations running agents in production drew the line correctly between what the platform can own and what engineering needs to own. Then they built governance into both sides before scaling. The four gates - the prior question that still gets skipped Run every candidate use case through these four checks before the platform conversation begins. None of the recent infrastructure improvements change what they are checking, because none of them change the fundamental cost structure of agentic reasoning. Gate 1 - is the logic fully deterministic? If every valid output for every valid input can be enumerated in unit tests, the problem does not need an LLM. A rules engine executes in microseconds at zero inference cost and cannot produce a plausible-but-wrong answer. NeuBird AI's production ops agents which have resolved over a million alerts and saved enterprises over $2 million in engineering hours work because alert triage logic that can be expressed as rules runs in deterministic code, and the LLM only handles cases where pattern-matching is insufficient. That boundary is not incidental to the system's reliability. It is the reason for it. Gate 2 - is zero hallucination tolerance required? With over 80% of databases now being built by AI agents per Databricks' State of AI Agents report the surface area for hallucination-induced data errors has grown significantly. In domains where a wrong answer is a compliance event financial calculation, medical logic, regulatory determinations irreducible LLM output uncertainty is disqualifying regardless of model version or prompt engineering effort. Exit to deterministic code or classical ML with bounded output spaces. Gate 3 - is a sub-100ms latency SLA required? LLM inference is faster than it was eighteen months ago. It is not fast enough for payment transaction processing, real-time fraud scoring, or live inventory management. A three-agent system with MCP tool calls has a P50 latency measured in seconds. These problems need purpose-built transactional architecture. Gate 4 - is regulatory explainability required? A2A enables complex agent coordination and delegation. It does not make LLM reasoning reproducible in a regulatory sense. Temperature above zero means the same input produces different outputs across invocations. Regulators in financial services, healthcare, and consumer credit require deterministic, auditable decision rationale. Exit to deterministic workflow with structured audit logging at every Five production failure modes - one of them new The four original anti-patterns are still showing up in production. A fifth has been added by scale. Routing data retrieval through a reasoning loop. A direct API call returns account status in under 10ms. Routing the same request through an LLM reasoning step adds hundreds of milliseconds, consumes tokens on every call, and introduces output parsing on data that is already structured. The agent calls a structured tool. The tool calls the API. The agent never acts as the integration layer. Encoding business rules in prompts. Rules expressed in prompt text drift as models update. They produce probabilistic output across invocations and fail in ways that are difficult to reproduce and diagnose. A rule that must evaluate correctly every time belongs in a deterministic tool function unit-tested, version-controlled, independently deployable via MCP. No approval gate on CRUD operations. CRUD operations without a human approval step will eventually misfire on the input that testing did not cover. The gate needs to be designed before deployment, not added after the first incident involving a financial posting, a customer-facing communication, or a data deletion. Monolithic agent for all domains. A single agent accumulating every domain leads predictably to topic collision, context overflow, and maintenance that becomes impossible as scope expands. Specialized agents per domain, coordinating through A2A, is the architecture that scales. Ungoverned agent sprawl. This is the new one and currently the most prevalent. OutSystems found 94% of organizations concerned about it, with only 12% having a centralized response. Teams building agents independently across fragmented stacks, without shared governance, evaluation standards, or audit infrastructure, produce exactly the same organizational debt that shadow IT created but with higher stakes, because these systems make autonomous decisions rather than just storing and retrieving data. The fix is treating governance as an architectural input before deployment, not a compliance requirement after something breaks. The infrastructure is ready. The judgment is not. The tier decision sequence has not changed. Does the problem need natural language understanding or dynamic generation? No — deterministic system, stop. Can a business team own it through standard connectors with no sub-3-second latency SLA and no payload-level compliance requirement? Yes — low-code. Does it need custom orchestration, multi-agent coordination, or audit-grade observability? Yes — pro-code with MCP and A2A. Does it need both a conversational surface and deep backend reasoning? Hybrid, with a governance layer spanning both. What has changed is that governance is no longer optional infrastructure to add when you have time. The data is unambiguous. Companies with governance tools get over 12 times more AI projects into production than those without. Evaluation pipelines, distributed tracing across agent boundaries, human oversight gates, and centralised agent lifecycle management are not overhead. They are what converts experiments into production systems. The teams still stuck in pilot are not stuck because the technology failed them. They are stuck because they skipped this layer. The protocols are standardised. The frameworks are mature. The infrastructure exists. None of that is what is holding most enterprise agent programmes back. What is holding them back is a selection problem disguised as a technology problem — teams building agents before asking whether agents are warranted, choosing platforms before running the four gates, and treating governance as a checkpoint rather than an architectural input. I have built agents that should have been workflow engines. Not because the technology was wrong, but because nobody stopped early enough to ask whether it was necessary. The four gates in this article exist because I learned those lessons at clients' expense, not mine. The most useful thing I can offer any team starting an agentic AI project is not a framework selection guide. It is permission to say no — and a clear basis for saying it. Take the four gates framework to your next architecture review. If you have already shipped agents to production, I would like to hear what worked and what did not - comment below What to do next Three concrete steps depending on where you are right now. If you have pilots that have not reached production: Run them through the four gates in this article before the next sprint. Gate 1 alone will eliminate a meaningful percentage of them. The ones that survive all four are your real candidates for production investment. Download the attached file for gated checklist and take it into your next architecture review. If you are starting a new agent project: Do not open a platform before you have answered the gate questions. Once you have confirmed an agent is warranted and identified the tier, start here: Copilot Studio guided setup for low-code scenarios, or Foundry Agent Service for pro-code patterns with MCP and multi-agent coordination built in. Build governance infrastructure - evaluation pipeline, distributed tracing, HITL gates - before you scale, not after. If you have already shipped agents to production: Share what worked and what did not in the Azure AI Tech Community — tag posts with #AgentArchitecture. The most useful signal for teams still in pilot is hearing from practitioners who have been through production, not vendors describing what production should look like. References OutSystems — State of AI Development Report - https://www.outsystems.com/1/state-ai-development-report Databricks — State of AI Agents Report - https://www.databricks.com/resources/ebook/state-of-ai-agents Gartner — 2025 Microsoft 365 and Copilot Survey - https://www.gartner.com/en/documents/6548002 (Paywalled primary source — publicly reported via techpartner.news: https://www.techpartner.news/news/gartner-microsoft-copilot-hype-offset-by-roi-and-readiness-realities-618118) Anthropic — Model Context Protocol (MCP) - https://modelcontextprotocol.io Google Cloud — Agent-to-Agent Protocol (A2A) . https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability NeuBird AI — Production Operations Deployment Announcement NeuBird AI Closes $19.3M Funding Round to Scale Agentic AI Across Enterprise Production Operations ReAct: Synergizing Reasoning and Acting in Language Models — Yao et al. https://arxiv.org/abs/2210.03629 Enterprise Integration Patterns — Gregor Hohpe & Bobby Woolf, Addison-Wesley https://www.enterpriseintegrationpatterns.comWelcome let's get started
Welcome to the Copilot Studio Community on Microsoft Tech Community! We're thrilled to announce that Copilot Studio now has a dedicated home on the Microsoft Tech Community, and we'd love for you to be part of it from day one. Whether you're just getting started with building Agents in Agent Builder or you are a pro building agents and automations with Copilot Studio, this is your space to: Ask questions and get answers from the community and Microsoft experts Share what you've built — show off your agents, flows, and use cases Stay up to date on the latest features, releases, and best practices Connect with peers across industries who are shaping the future of AI-powered work The community is open to everyone, from first-time explorers to seasoned pros. Every question asked and every insight shared makes this a better resource for all of us. We can't wait to see what you build. Welcome!Microsoft 365 & Power Platform Community call
💡 Microsoft 365 & Power Platform Development bi-weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 30th of April we'll have following agenda: Latest on SharePoint Framework (SPFx) Latest on Copilot prompt of the week PnPjs CLI for Microsoft 365 Dev Proxy Reusable Controls for SPFx SPFx Toolkit VS Code extension PnP Search Solution Demos this time Adam Wójcik (Hitachi Energy) – SPFx Toolkit Showcase - Manage your SharePoint Online tenant and SPFx projects using SPFx Toolkit Language Model Tools Valentin Mazhar (MazAura) – Better Govern Agents with the Copilot Studio Monitor Reshmee Auckloo (Avanade) – Connect Power Automate to Agents Toolkit 📅 Download recurrent invite from https://aka.ms/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/m365-powerplat-dev-call-join 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!
