o3-deep-research is failed with the status incomplete with the reason as content filter
I working on an to do an deep research on internal data. I'm using currently the Azure OpenAI Responses API with MCP Tool. The underlying MCP server deployed into ACA with search and fetch tool with signatures in complaint with the specification (https://developers.openai.com/apps-sdk/build/mcp-server#company-knowledge-compatibility). OpenAI client created with 03-deep-research model with MCP tool, in a loop response status being checked. (https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/deep-research#remote-mcp-server-with-deep-research) Deep Research is being carried out for sometime, I could see in the log that handshake has been made, ListTools invoked, search tool is called post that fetch is called for the queries framed by the model.. But intermittently, the response status is becoming "incomplete" with incomplete reason as "content_filter". Otherwise the deep research is working fine. Not able identify the root cause as there is seems to be no way to identify what caused the content filtration whether its the prompt or completion. How to debug and check the root cause and rectify this ? Or is there known issue with the o3-deep-research model's intermediate reasoning completions Or search and fetch tool results are causing this ? I had uploaded a file made it available to MCP server, the search and fetch tool uses an Azure OpenAI agent to search the data using File Search and fetch tool gets the content of the file based on the id passed. For same file and same research topic the issue is not occurring always but intermittently.
GPT-5.5-Pro not listed in foundry?
The model is mentioned in this blog post : https://azure.microsoft.com/en-us/blog/openais-gpt-5-5-in-microsoft-foundry-frontier-intelligence-on-an-enterprise-ready-platform/ But it is currently not listed on Foundry. Only latest pro model is 5.4-pro. When will 5.5-pro model be available on azure foundry?Migrating to GPT-5.x Without Breaking GPT-4: A Practical, Backward-Compatible Playbook
The first request your service sends after swapping gpt-4o for gpt-5.1 in production will return HTTP 400. Not in two weeks. On the first call. And the parameter the error points to isn't one you set anywhere in your code - it's bound onto the request by a LangChain helper you've used for two years. This post walks through every breaking change between the GPT-4 and GPT-5 families on Azure OpenAI in Microsoft Foundry, the integration cliffs nobody warns you about, and the small set of files you need so the same call sites work against both model families without branching. Who this is for: engineers maintaining an existing production codebase that calls Azure OpenAI / OpenAI - directly or through LangChain - and needs to onboard GPT-5.x while keeping the GPT-4 deployments alive during rollout. What you'll leave with: one copy-paste compatibility module, a tiny LangChain subclass, a prompt-audit harness, and a 10-step rollout checklist. 1. Why this migration is different Every previous Azure OpenAI bump - 3.5 → 4, 4 → 4o, 4o → 4o-mini - was additive. You changed engine="gpt-4o" and everything kept working. GPT-5.x is the first generation that is subtractive: parameters you used to send now return 400 Unsupported parameter. The wire protocol itself changed because GPT-5 is a reasoning model - it spends tokens thinking internally before it answers, so the parameters that controlled the old sampling pipeline (temperature, top_p, presence_penalty, frequency_penalty) no longer exist on the request schema. What this means for production code: A passing test suite against gpt-4o will fail on the first call against gpt-5.1 with HTTP 400. A passing test suite against gpt-5.1 will fail on every legacy gpt-4* deployment because the new reasoning controls (reasoning_effort, verbosity) are not recognised there. LangChain helpers that worked unmodified for two years (notably create_sql_query_chain) silently bind stop=[...] onto your LLM and trigger the same 400. Source-grep won't find the offending line because it lives inside the library. The good news: the divergence is mechanical. With one detection helper, one parameter-builder, and one tiny LangChain subclass you can run the same code against both families. 2. The breaking-changes matrix Concern GPT-4 / GPT-4o (legacy) GPT-5.x / o1 / o3 (reasoning) Output budget max_tokens max_completion_tokens (rejects max_tokens) temperature 0.0–1.0 Only the default (1) is accepted - omit it top_p Supported Rejected presence_penalty, frequency_penalty Supported Rejected logprobs, logit_bias Supported Rejected stop sequences Supported Rejected on most reasoning deployments reasoning_effort Rejected New: minimal | low | medium | high verbosity Rejected New: low | medium | high (sometimes via extra_body) System instruction role system developer recommended; system still works as alias Output token cost Output tokens only Output + reasoning tokens count against your cap Recommended API version 2024-12-01-preview or earlier 2025-03-01-preview or later Two consequences are easy to miss: max_completion_tokens is a shared budget. GPT-5.1 can burn 2–4× more tokens internally before emitting the first response token. A cap of 4096 that comfortably held a SQL query on GPT-4o now silently truncates the answer mid-token on GPT-5.1. Multiply your legacy budgets by ~2.5× and add a floor (e.g. 4096) before sending. The stop parameter is the silent killer. Any helper that calls llm.bind(stop=[...]) - and there are several in langchain - will turn a working code path into a 400 the moment you swap deployments. 3. Compatibility strategy: detect, don't fork The temptation is to fork: one branch for GPT-4, one for GPT-5. Don't. The right unit of abstraction is one function that classifies the deployment into a family, and one function that builds a kwargs dict the SDK will accept for that family. Every call site - SDK, LangChain, raw HTTP - drains into the same kwargs builder. When you eventually retire GPT-4 you delete the legacy branch in one file, not in fifty. 4. The industry-agnostic compatibility module Drop the following file into your project. It has no Azure / OpenAI / LangChain imports at module load time, so the same file works from a web service, a serverless function, a notebook, or a CLI tool. 4.1 model_compat.py """ Model compatibility helper for GPT-5.x with GPT-4 backward compatibility. This module centralises the parameter translation needed to talk to the "reasoning" generation of OpenAI / Azure OpenAI models (GPT-5, GPT-5.1, o1, o3, o4) while keeping older deployments (gpt-4, gpt-4o, gpt-4-32k, gpt-3.5-turbo, etc.) working unchanged. """ from __future__ import annotations import logging import os import re from typing import Any, Dict, Iterable, Mapping, Optional # --------------------------------------------------------------------------- # Family detection # --------------------------------------------------------------------------- _REASONING_PATTERNS = ( # gpt-5, gpt5, gpt-5.1, gpt_5, GPT 5, gpt5mini-prod-eu, ... re.compile(r"(?i)(^|[^a-z0-9])gpt[-_ ]?5(\.\d+)?([^0-9]|$)"), # o1, o3, o4, o1-mini, o3-preview ... re.compile(r"(?i)(^|[^a-z0-9])o[134](-mini|-preview)?([^a-z0-9]|$)"), ) _LEGACY_PATTERNS = ( re.compile(r"(?i)gpt[-_ ]?4o"), re.compile(r"(?i)gpt[-_ ]?4(?!\d)"), re.compile(r"(?i)gpt[-_ ]?4[-_ ]?32k"), re.compile(r"(?i)gpt[-_ ]?3\.?5"), re.compile(r"(?i)gpt[-_ ]?35"), ) def get_model_family(model_or_deployment: Optional[str]) -> str: """Return ``"reasoning"`` for GPT-5.x / o-series, ``"legacy"`` otherwise. Honours an ``OPENAI_MODEL_FAMILY`` env-var override for deployments whose user-defined name does not embed the model family (e.g. ``prod-default``). """ override = (os.getenv("OPENAI_MODEL_FAMILY") or "").strip().lower() if override in {"reasoning", "gpt-5", "gpt5", "gpt-5.1", "o-series", "o1", "o3"}: return "reasoning" if override in {"legacy", "gpt-4", "gpt4", "gpt-3.5", "gpt35", "chat"}: return "legacy" name = (model_or_deployment or "").strip() if not name: # Fail closed: when we don't know, assume legacy so old code keeps # working. Misclassifying a reasoning deployment as legacy fails fast # with a clear "Unsupported parameter" 400; the reverse silently # drops parameters the caller expected. return "legacy" for pat in _REASONING_PATTERNS: if pat.search(name): return "reasoning" for pat in _LEGACY_PATTERNS: if pat.search(name): return "legacy" return "legacy" def is_reasoning_model(model_or_deployment: Optional[str]) -> bool: return get_model_family(model_or_deployment) == "reasoning" # --------------------------------------------------------------------------- # Reasoning controls # --------------------------------------------------------------------------- _VALID_REASONING_EFFORT = {"minimal", "low", "medium", "high"} _VALID_VERBOSITY = {"low", "medium", "high"} def _coerce_choice(raw: Optional[str], valid: Iterable[str]) -> Optional[str]: if raw is None: return None value = str(raw).strip().lower() if not value: return None if value not in set(valid): logging.warning( "Ignoring unsupported value '%s'; expected one of %s", raw, sorted(valid), ) return None return value def get_reasoning_effort(override: Optional[str] = None) -> Optional[str]: return _coerce_choice( override if override is not None else os.getenv("OPENAI_REASONING_EFFORT"), _VALID_REASONING_EFFORT, ) def get_verbosity(override: Optional[str] = None) -> Optional[str]: return _coerce_choice( override if override is not None else os.getenv("OPENAI_VERBOSITY"), _VALID_VERBOSITY, ) # --------------------------------------------------------------------------- # max_completion_tokens scaling # --------------------------------------------------------------------------- def _reasoning_token_scale() -> float: """Multiplier applied to legacy ``max_tokens`` when targeting a reasoning model.""" try: scale = float(os.getenv("OPENAI_REASONING_TOKEN_SCALE", "2.5")) except (TypeError, ValueError): scale = 2.5 return scale if scale > 0 else 1.0 def _reasoning_token_floor() -> int: try: floor = int(os.getenv("OPENAI_REASONING_TOKEN_FLOOR", "4096")) except (TypeError, ValueError): floor = 4096 return floor if floor > 0 else 4096 def scale_max_tokens_for_reasoning(max_tokens: Optional[int]) -> Optional[int]: """Scale a legacy ``max_tokens`` budget up for reasoning models. ``None`` and ``-1`` ("no explicit cap") are passed through. """ if max_tokens is None: return None if max_tokens == -1: return -1 return max(int(round(max_tokens * _reasoning_token_scale())), _reasoning_token_floor()) # --------------------------------------------------------------------------- # Kwargs builders # --------------------------------------------------------------------------- _SAMPLING_KEYS = ("temperature", "top_p", "presence_penalty", "frequency_penalty") def _drop_none(mapping: Mapping[str, Any]) -> Dict[str, Any]: return {k: v for k, v in mapping.items() if v is not None} def build_openai_chat_kwargs( model: str, *, max_tokens: Optional[int] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, reasoning_effort: Optional[str] = None, verbosity: Optional[str] = None, extra: Optional[Mapping[str, Any]] = None, ) -> Dict[str, Any]: """Build kwargs for ``openai.OpenAI / AzureOpenAI .chat.completions.create``. Splat the result directly: ``client.chat.completions.create(**kwargs)``. Unsupported parameters are silently omitted for reasoning models; legacy deployments retain the historical behaviour. """ family = get_model_family(model) kwargs: Dict[str, Any] = {"model": model} # ---- output budget ---- if max_tokens is not None and max_tokens != -1: if family == "reasoning": kwargs["max_completion_tokens"] = scale_max_tokens_for_reasoning(int(max_tokens)) else: kwargs["max_tokens"] = int(max_tokens) # ---- sampling ---- if family == "legacy": kwargs.update(_drop_none({ "temperature": temperature, "top_p": top_p, "presence_penalty": presence_penalty, "frequency_penalty": frequency_penalty, })) else: for key, value in ( ("temperature", temperature), ("top_p", top_p), ("presence_penalty", presence_penalty), ("frequency_penalty", frequency_penalty), ): if value is not None: logging.debug( "Dropping unsupported parameter '%s' for reasoning model '%s'", key, model, ) # ---- reasoning controls ---- if family == "reasoning": effort = get_reasoning_effort(reasoning_effort) if effort is not None: kwargs["reasoning_effort"] = effort verb = get_verbosity(verbosity) if verb is not None: # ``verbosity`` is not a top-level kwarg in openai-python <= 1.65.x; # route it via ``extra_body`` so it lands in the JSON without a # TypeError from the SDK. kwargs.setdefault("extra_body", {})["verbosity"] = verb # ---- caller-supplied extras (already filtered) ---- if extra: for key, value in extra.items(): if value is None: continue if family == "reasoning" and key in _SAMPLING_KEYS: continue kwargs[key] = value return kwargs def build_langchain_chat_kwargs( deployment_name: str, *, max_tokens: Optional[int] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, reasoning_effort: Optional[str] = None, verbosity: Optional[str] = None, ) -> Dict[str, Any]: """Build kwargs for ``langchain_openai.AzureChatOpenAI`` / ``ChatOpenAI``. Older ``langchain-openai`` releases don't expose ``max_completion_tokens`` as a top-level kwarg, so we forward it through ``model_kwargs`` (which langchain passes straight to the SDK). """ family = get_model_family(deployment_name) kwargs: Dict[str, Any] = {} model_kwargs: Dict[str, Any] = {} if max_tokens is not None and max_tokens != -1: if family == "reasoning": model_kwargs["max_completion_tokens"] = scale_max_tokens_for_reasoning(int(max_tokens)) else: kwargs["max_tokens"] = int(max_tokens) if family == "reasoning": effort = get_reasoning_effort(reasoning_effort) if effort is not None: model_kwargs["reasoning_effort"] = effort verb = get_verbosity(verbosity) if verb is not None: model_kwargs.setdefault("extra_body", {})["verbosity"] = verb else: if temperature is not None: kwargs["temperature"] = temperature if top_p is not None: kwargs["top_p"] = top_p if model_kwargs: kwargs["model_kwargs"] = model_kwargs return kwargs def get_system_role(model_or_deployment: Optional[str] = None) -> str: """Return ``"developer"`` for reasoning models when opted in, ``"system"`` otherwise. Defaulting to ``"system"`` preserves compatibility with LangChain prompt templates and SDK helpers that don't yet recognise the new role. Opt in with ``OPENAI_USE_DEVELOPER_ROLE=1`` once your stack supports it. """ if not is_reasoning_model(model_or_deployment): return "system" raw = os.getenv("OPENAI_USE_DEVELOPER_ROLE", "") return "developer" if raw.strip().lower() in {"1", "true", "yes", "on"} else "system" 4.2 What this buys you Every direct-SDK call collapses to two lines: from openai import AzureOpenAI from model_compat import build_openai_chat_kwargs client = AzureOpenAI( azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], api_version=os.environ["OPENAI_API_VERSION"], api_key=os.environ["AZURE_OPENAI_API_KEY"], ) kwargs = build_openai_chat_kwargs( model=os.environ["OPENAI_ENGINE"], max_tokens=4096, # automatically becomes max_completion_tokens for GPT-5 temperature=0.2, # automatically dropped for GPT-5 reasoning_effort="low", # automatically dropped for GPT-4 ) response = client.chat.completions.create( messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": user_input}, ], **kwargs, ) The same call site now correctly targets gpt-5.1, gpt-4o, gpt-4-32k, o3-mini, or any future deployment whose name embeds the family - and you can override with the OPENAI_MODEL_FAMILY env var when the deployment alias is opaque. 4.3 Raw HTTP call sites Some legacy code paths bypass the SDK and POST JSON directly. The same builder works there: import json import requests from model_compat import build_openai_chat_kwargs, get_system_role deployment = os.environ["OPENAI_ENGINE"] api_version = os.environ["OPENAI_API_VERSION"] endpoint = ( f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/deployments/{deployment}" f"/chat/completions?api-version={api_version}" ) payload = { "messages": [ {"role": get_system_role(deployment), "content": system_prompt}, {"role": "user", "content": user_prompt}, ], } # Splat the kwargs into the payload, then strip the SDK-only ``model`` key. payload.update(build_openai_chat_kwargs( model=deployment, max_tokens=800, temperature=0.7, top_p=0.95, reasoning_effort="low", )) payload.pop("model", None) # ``model`` is encoded in the URL for Azure payload.pop("extra_body", None) # already on the payload root resp = requests.post( endpoint, headers={"Content-Type": "application/json", "api-key": api_key}, data=json.dumps(payload), timeout=60, ) resp.raise_for_status() 5. LangChain: the hidden stop parameter langchain.chains.sql_database.query.create_sql_query_chain calls llm.bind(stop=["\nSQLResult:"]) internally to terminate the model's output before the example block in its prompt. That stop value is forwarded to the SDK on every invocation. GPT-5.1 rejects it: openai.BadRequestError: Error code: 400 - {'error': { 'message': "Unsupported parameter: 'stop' is not supported with this model.", 'type': 'invalid_request_error', 'param': 'stop', }} You can't reach into the chain to disable it. The clean fix is a thin AzureChatOpenAI subclass that drops stop for reasoning models only: 5.1 langchain_compat.py """LangChain-side compatibility shim for reasoning-class deployments.""" from __future__ import annotations from typing import Any, List, Optional from langchain_core.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.messages import BaseMessage from langchain_core.outputs import ChatResult from langchain_openai import AzureChatOpenAI # use ChatOpenAI for non-Azure from model_compat import is_reasoning_model class ReasoningSafeAzureChatOpenAI(AzureChatOpenAI): """``AzureChatOpenAI`` variant that hides parameters reasoning models reject. Reasoning models (GPT-5.x, o1/o3/o4) return HTTP 400 when a request payload carries ``stop``. LangChain's SQL helpers unconditionally bind it, so the unsupported parameter reaches the SDK regardless of how the caller configured the LLM. This subclass strips ``stop`` for reasoning deployments while forwarding it unchanged for legacy GPT-4 / GPT-3.5 deployments - the behaviour is byte-identical to upstream LangChain for those models. """ def _deployment_id(self) -> str: # ``langchain-openai`` >= 0.2 exposes ``azure_deployment``; older # releases use ``deployment_name``. Either may be set by the caller. return ( getattr(self, "azure_deployment", None) or getattr(self, "deployment_name", None) or "" ) def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if is_reasoning_model(self._deployment_id()): stop = None return super()._generate(messages, stop=stop, run_manager=run_manager, **kwargs) async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if is_reasoning_model(self._deployment_id()): stop = None return await super()._agenerate(messages, stop=stop, run_manager=run_manager, **kwargs) Use it as a drop-in replacement: from langchain_compat import ReasoningSafeAzureChatOpenAI from model_compat import build_langchain_chat_kwargs llm_kwargs = build_langchain_chat_kwargs( deployment_name=os.environ["OPENAI_ENGINE"], max_tokens=6000, temperature=0, reasoning_effort="low", ) llm = ReasoningSafeAzureChatOpenAI( azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], azure_deployment=os.environ["OPENAI_ENGINE"], openai_api_version=os.environ["OPENAI_API_VERSION"], api_key=os.environ["AZURE_OPENAI_API_KEY"], **llm_kwargs, ) That single substitution makes create_sql_query_chain, SQLDatabaseChain, and the ChatOpenAI-based RAG helpers all work against GPT-5.1 without any other changes. 6. The second LangChain gotcha: prose where SQL should be create_sql_query_chain is documented to return the literal string "I don't know" (or a similar fallback) when the LLM cannot form a query. The default code path takes the chain output and runs it against the database: sql = chain.invoke({...}) # -> "I don't know" result = db.run(sql) # -> sends "I don't know" to pyodbc The database faithfully returns: [42000] Unclosed quotation mark after the character string 't know'. (105) Which surfaces to the end user as a misleading "SQL syntax error". The mitigation is a one-line guard that validates the chain output looks like SQL before execution: import re _SQL_START_RE = re.compile( r"^\s*(?:WITH|SELECT|INSERT|UPDATE|DELETE|CREATE|DROP|ALTER|MERGE|EXEC|EXECUTE|TRUNCATE)\b", re.IGNORECASE, ) def looks_like_sql(text: str) -> bool: """True only if ``text`` starts with a recognised SQL DML/DDL keyword.""" if not text or not text.strip(): return False return bool(_SQL_START_RE.match(text)) sql = extract_sql_query(chain.invoke({...})) if not looks_like_sql(sql): logging.warning("SQL chain returned a non-SQL response: %r", sql[:200]) return ( "I couldn't form a SQL query for that question. " "Please rephrase or add more context." ) result = db.run(sql) This isn't specific to GPT-5.1 - it's good hygiene for any LLM that backs a SQL agent - but the failure mode becomes much more frequent on reasoning models because they're better at refusing. 7. Cleaning Markdown out of create_sql_query_chain output Reasoning models like to wrap their answer in a markdown fence and append a "Note:" or "Explanation:" paragraph. None of that survives db.run(). A defensive extract_sql_query handles all the variants: import re def extract_sql_query(text: str) -> str: """Strip markdown fences, leading prose, and trailing explanations.""" # 1) Prefer SQL inside a markdown code fence. m = re.search(r"```(?:sql|SQL|Sql)?\s*\n(.*?)\n```", text, re.DOTALL) if m: text = m.group(1) text = text.strip() # 2) Drop any prose *before* the SQL by jumping to the first SQL keyword. m = re.search( r"(?im)^\s*(WITH|SELECT|INSERT|UPDATE|DELETE|CREATE|DROP|ALTER|MERGE|EXEC|EXECUTE|TRUNCATE)\b", text, ) if m: text = text[m.start(1):] # 3) Cut at the first "Explanation:" / "Note:" / "This query..." marker. m = re.compile( r"(?im)^\s*(?:Explanation|Note|Notes|Here(?:'|\u2019)?s|" r"This\s+(?:query|SQL|statement|returns|counts|selects|will|gets|finds)|" r"The\s+(?:query|SQL|above|result|statement)|" r"Result|Results|Description|Output|Answer)\b[^\n]*" ).search(text) if m: text = text[: m.start()].rstrip() # 4) Drop any trailing fence that survived step 1. if text.endswith("```"): text = text[:-3].rstrip() return text.strip() 8. Package versioning The bare minimum your requirements.txt / environment.yml needs: Package Last GPT-4-only version First GPT-5.x-safe version Notes openai 1.55.x 1.65.x (recommend 1.65.4+) Earlier versions reject max_completion_tokens and reasoning_effort as unknown kwargs langchain-openai 0.2.14 0.3.7+ 0.3.x line exposes azure_deployment and forwards model_kwargs correctly to the new SDK langchain 0.3.14 0.3.21+ Pin together with langchain-openai and langchain-core langchain-core 0.3.29 0.3.49+ Update in lockstep with the others langchain-community 0.3.14 0.3.20+ Mostly transitive; needed for SQLDatabase helpers tiktoken 0.7.x 0.8.0+ Encodings for GPT-5.1 ship in 0.8.0; older versions fall back to cl100k_base for unknown models tokencost (optional) 0.1.16 0.1.20+ Update for GPT-5.x price tables Azure OpenAI API version 2024-12-01-preview 2025-03-01-preview First version that ships reasoning_effort and the GPT-5.x routing Pin exact versions after testing - LangChain has a habit of moving public re-exports between minor releases. requirements.txt snippet: openai==1.65.4 langchain==0.3.21 langchain-core==0.3.49 langchain-openai==0.3.7 langchain-community==0.3.20 tiktoken==0.8.0 9. New GPT-5.x knobs worth using Once you're on a reasoning deployment, two new parameters become available. Both are optional, both default to a sensible value, and both are stripped by the kwargs builder above when the target is a legacy model. reasoning_effort minimal - one-shot lookups, classification. low - deterministic structured output (SQL, JSON-schema extraction, rule-based rewrites). Lowest cost overhead. medium (default) - RAG, summarisation, normal Q&A. high - multi-step analytical reasoning, complex code synthesis. A useful pattern is to choose the level by task profile rather than at the call site: TASK_EFFORT = { "sql": "low", "structured_extract": "low", "kg_cleaning": "low", "rag_qa": "medium", "vision": "medium", "analytical": "high", } verbosity low | medium | high. Controls the length of the response, not its substance. Useful for grounding chat UIs where you want crisp answers - set low for /answer endpoints and high for "explain like a senior engineer" panels. Note: in openai-python <= 1.65.x, verbosity is not yet a top-level keyword argument; pass it through extra_body (the builder above already does this). developer role GPT-5.x prefers {"role": "developer", "content": "..."} for instructions that previously used system. The change is non-breaking on the Azure side - system is still accepted as an alias - but some downstream LangChain prompt templates predate the role and will reject it on construction. Treat developer as opt-in (OPENAI_USE_DEVELOPER_ROLE=1) for now; flip the default after your prompt-template version is known good. 10. Auditing your existing prompts When the wire-level migration is done your service will talk to GPT-5.x - but that doesn't mean it says the right thing. Reasoning models read prompts differently in ways that won't show up as 400s: They take instructions more literally. A prompt that worked when GPT-4o rounded the corners may surface every edge case verbatim. They refuse more often. "I don't know" / "I cannot help with that" are more frequent because reasoning models are less willing to confabulate. They ignore "be concise" / "be terse". Use the new verbosity knob. Step-by-step / chain-of-thought instructions become redundant. The model already reasons internally; extra "think before you answer" prose competes with its own chain of thought and often hurts output quality. Negative-only instructions can backfire. "Never output X" prompts occasionally cause refusals where you'd rather have a workaround. 10.1 Build a prompt regression harness Capture every system+user prompt your service emits in a CSV, then replay each one against both deployments and diff the output. The diff is the single most useful artefact you can produce before the cutover: # prompt_audit.py - minimal differential tester import csv from openai import AzureOpenAI from model_compat import build_openai_chat_kwargs LEGACY = "gpt-4o" REASONING = "gpt-5.1" client = AzureOpenAI( azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], api_version=os.environ["OPENAI_API_VERSION"], api_key=os.environ["AZURE_OPENAI_API_KEY"], ) def run(model: str, system: str, user: str) -> str: kw = build_openai_chat_kwargs( model=model, max_tokens=4096, temperature=0.2, # auto-dropped for reasoning reasoning_effort="medium", # auto-dropped for legacy ) resp = client.chat.completions.create( messages=[ {"role": "system", "content": system}, {"role": "user", "content": user}, ], **kw, ) return resp.choices[0].message.content or "" with open("prompts.csv") as f_in, open("diff.tsv", "w", newline="") as f_out: writer = csv.writer(f_out, delimiter="\t") writer.writerow(["id", "legacy_first80", "reasoning_first80", "len_legacy", "len_new", "identical"]) for row in csv.DictReader(f_in): legacy = run(LEGACY, row["system"], row["user"]) new = run(REASONING, row["system"], row["user"]) writer.writerow([ row["id"], legacy[:80].replace("\n", " "), new[:80].replace("\n", " "), len(legacy), len(new), legacy.strip() == new.strip(), ]) Capture three signals per prompt - they're enough to triage 95% of drift: Format compliance. Did the output still parse as the expected JSON / YAML / Markdown / SQL? Run your existing downstream parser on both columns. Token cost delta. Reasoning models tend to be more verbose by default. Anything beyond +20% is a candidate for the verbosity="low" knob. Semantic drift. Spot-check 5–10% of rows by hand. You're looking for changes in intent, not changes in wording. 10.2 Common rewrites to make prompts model-agnostic The goal isn't to write two prompts. It's to write one prompt that produces correct output on both families by moving constraints out of the natural-language body and into the request shape. 10.2a. Format constraints belong in response_format, not the prose Don't: Output ONLY a JSON object with keys `name` and `score`. Do not include any explanation. Do not wrap in markdown. Do not say anything else. Do: resp = client.chat.completions.create( messages=[...], response_format={ "type": "json_schema", "json_schema": { "name": "scored_entity", "schema": { "type": "object", "properties": { "name": {"type": "string"}, "score": {"type": "number"}, }, "required": ["name", "score"], "additionalProperties": False, }, "strict": True, }, }, **kw, ) response_format is honoured by both gpt-4o (>= 2024-08-06) and the entire GPT-5.x line. The prompt loses three lines of brittle natural-language constraints and you get schema-validated output for free. 10.2b. Replace "think step by step" with reasoning_effort Don't: Let's think step by step. First identify the entity. Then find the category. Then compute the score. Then format the answer. Do: delete the prose and pass reasoning_effort="medium" (or "high") for reasoning deployments. The kwargs builder drops the parameter automatically for GPT-4 models, so the same prompt now produces: step-by-step reasoning internally on GPT-5.x (lower output token cost), the same final answer on GPT-4o that the verbose prompt used to elicit. 10.2c. Replace temperature-based variety with n sampling If your code relied on temperature=0.9 to get diverse completions, GPT-5.x will return roughly the same answer every time. Generate variety the explicit way: resp = client.chat.completions.create(messages=[...], n=5, **kw) candidates = [c.message.content for c in resp.choices] Or call the model N times with slightly different framings. Both patterns work against either family with no further code changes. 10.2d. Move procedural instructions to the developer role For multi-step workflows, the new developer role gives clearer separation between what the system enforces and what the user is asking: messages = [ {"role": get_system_role(deployment), "content": role_card_for_assistant}, {"role": "developer", "content": procedural_instructions}, {"role": "user", "content": user_question}, ] get_system_role returns "system" for legacy models and "developer" for reasoning models opted in via OPENAI_USE_DEVELOPER_ROLE=1. Once your LangChain templates support the new role you can flip the default. 10.2e. Add a literal-execution header for strict formats For prompts where the exact output shape matters (table generation, SQL with a fixed column order, structured incident reports), prepend an explicit literal-execution header so reasoning models don't drift into "helpful improvements": LITERAL_EXECUTION_HEADER = ( "Execution mode: follow the instructions below literally and in order. " "Do not infer intent, skip, reorder, merge, or add steps. Honour the " "exact formatting, tone, and verbosity specified. If a step is " "ambiguous, respond with the literal interpretation and flag the " "ambiguity instead of guessing." ) def apply_literal_execution(prompt: str) -> str: if LITERAL_EXECUTION_HEADER in prompt: return prompt return f"{LITERAL_EXECUTION_HEADER}\n\n{prompt}" It's a no-op on GPT-4o (the older models already follow instructions literally enough) and a meaningful guard rail on GPT-5.1. Wire it behind an OPENAI_LITERAL_EXECUTION flag so you can disable it without redeploying. 10.3 A prompt-shaped checklist Run every prompt your service emits past these questions: Question Action Does it specify output format in prose? Move to response_format (10.2a) Does it include "think step by step"? Remove; set reasoning_effort (10.2b) Does it set tone constraints ("be concise")? Use verbosity Does it use negative-only instructions ("never X")? Add positive alternative ("do Y instead") Does it embed example outputs with values that would change? Replace concrete values with placeholder tokens (<VALUE>) Does it rely on temperature > 0 for variety? Use n=K sampling (10.2c) Is the system prompt > 2k tokens? Split into role-card (system) + procedure (developer) Does output ordering matter? Add the literal-execution header (10.2e) 10.4 Score before you ship Don't approve a rewritten prompt by eyeballing one example. Score it: Format compliance rate. Percentage of N=50 outputs that pass your existing downstream parser / JSON schema validation. Token cost delta. Cap regression at +20% versus the legacy baseline. Beyond that, dial verbosity="low" or tighten the prompt. Latency p50 / p95 delta. Reasoning models add tail latency. If your SLA is tight, set reasoning_effort="low" for the path or move it to a background queue. A prompt that regresses on any of those by more than your tolerance window ships behind a feature flag with rollback wired in. 11. Testing strategy Two test layers catch >90% of regressions: Family-classification tests import pytest from model_compat import get_model_family, build_openai_chat_kwargs @pytest.mark.parametrize("name,expected", [ ("gpt-5.1", "reasoning"), ("gpt5", "reasoning"), ("gpt-5-prod-eu", "reasoning"), ("o3-mini", "reasoning"), ("o1", "reasoning"), ("gpt-4o", "legacy"), ("gpt-4", "legacy"), ("gpt-4-32k", "legacy"), ("gpt-35-turbo", "legacy"), ("", "legacy"), # unknown -> fail closed to legacy (None, "legacy"), ]) def test_family(name, expected): assert get_model_family(name) == expected def test_kwargs_for_reasoning_drops_temperature(): kw = build_openai_chat_kwargs( model="gpt-5.1", max_tokens=1000, temperature=0.2, top_p=0.9, reasoning_effort="low", ) assert "temperature" not in kw assert "top_p" not in kw assert kw["max_completion_tokens"] >= 4096 # floor applied assert kw["reasoning_effort"] == "low" def test_kwargs_for_legacy_keeps_temperature(): kw = build_openai_chat_kwargs( model="gpt-4o", max_tokens=1000, temperature=0.2, top_p=0.9, ) assert kw["max_tokens"] == 1000 assert kw["temperature"] == 0.2 assert kw["top_p"] == 0.9 assert "reasoning_effort" not in kw Wire-level smoke tests For each LLM call site you maintain, write a single integration test that exercises the chain against a real (or mocked) endpoint and asserts: HTTP 200, non-empty content, finish_reason != "length" (so you catch silent truncation), (optional) classifier-style assertions against a golden output. Run those tests once against the legacy deployment and once against the new one - same test code, two OPENAI_ENGINE values. 12. Things that don't change It's easy to over-correct. Several pieces of plumbing keep working without modification: Authentication. AAD token providers, managed identity, and API keys are unchanged. Embeddings. text-embedding-3-small, text-embedding-3-large, and text-embedding-ada-002 are not part of the reasoning generation; the embeddings call shape is identical. Function calling / tool use. Same JSON schema, same response shape. Streaming. SSE format is unchanged. Token counters. tiktoken still works, but bump to 0.8.0+ so the new model name resolves to the right encoding instead of silently falling back to cl100k_base. 13. Next steps If you only do four things from this post, do these - in order: Deploy a GPT-5.1 model side-by-side with your current GPT-4 deployment in Microsoft Foundry. Keep the GPT-4 deployment live; you'll need both for the parallel-run period. Drop model_compat.py and langchain_compat.py into your project (Sections 4 and 5). Replace every AzureChatOpenAI(...) construction with ReasoningSafeAzureChatOpenAI and route every kwargs literal through the builders. Run the prompt-audit harness (Section 10.1) against your top 50 most frequently invoked prompts. Triage the diff with the checklist in 10.3. Roll out behind a percentage-based flag. Start at 5% of traffic for 24 hours, compare quality and cost telemetry against the GPT-4o baseline, then ramp. Reference material Azure OpenAI in Microsoft Foundry - model overview Azure OpenAI model retirements and deprecations Reasoning models in Azure OpenAI Structured Outputs in Azure OpenAI openai-python SDK changelog langchain-openai release notes Talk to us Open an issue on the Microsoft Foundry GitHub samples repository if you hit a gap this post didn't cover. Share your migration story or numbers in the comments below - field data is the fastest way to make this guide better for the next team. If you operate a regulated workload (finance, health, public sector) and need help sequencing the rollout with your model retirement deadlines, reach out to your Microsoft account team or a Microsoft Foundry partner. GPT-5.x is the first major model bump in two years that requires code changes - but the changes collapse into one small compatibility module and a one-line LangChain subclass. With those in place your code is forwards-compatible (works on reasoning models today) and backwards- compatible (still works on every GPT-4 deployment you haven't migrated yet). The investment pays a recurring dividend: when the next reasoning bump ships, the only file that needs updating is model_compat.py. Appendix A - Minimal .env template # Endpoint and auth (unchanged between families) AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com AZURE_OPENAI_API_KEY=<key> # The deployment name decides the family. The classifier reads it. OPENAI_ENGINE=gpt-5.1 OPENAI_API_VERSION=2025-03-01-preview # Optional override for opaque deployment names # OPENAI_MODEL_FAMILY=reasoning # or "legacy" # Optional reasoning controls (ignored for legacy deployments) OPENAI_REASONING_EFFORT=medium OPENAI_VERBOSITY=medium OPENAI_REASONING_TOKEN_SCALE=2.5 OPENAI_REASONING_TOKEN_FLOOR=4096 # Flip when your LangChain templates support it # OPENAI_USE_DEVELOPER_ROLE=1 Appendix B - One-liner sanity checks # Does a deployment name classify correctly? python -c "from model_compat import get_model_family; print(get_model_family('gpt-5.1'))" # -> reasoning # Does the LangChain LLM strip ``stop`` when the deployment is GPT-5.1? python -c " from langchain_compat import ReasoningSafeAzureChatOpenAI import inspect; print(inspect.getsource(ReasoningSafeAzureChatOpenAI._generate)) " Companion repository: drop model_compat.py and langchain_compat.py next to each other in your utils/ package. They are zero-dependency on import, so you can vendor them into any service - web, function, batch job - without dragging Azure SDK or LangChain into module-load.
Data Visualisation / Charting in Azure Foundry
Hi Foundry community, We are working on an agent that can query internal data sources, and are looking for ways that we can visualise data (think pie charts, bar charts, etc.). This would be consumed by end users through Copilot/Teams. However we are unable to find a way to do so, which is surprising given that you easily can create charts through M365 Copilot Chat and through Copilot Studio. We have tried using the 'Code Interpreter' tool, but the Teams/Copilot client UIs just do not render the results inline, either interactive or as an embedded image. They also do not give any option to download them. Has anyone tackled this before? How have you been able generate charts? Many thanks!
Foundry Agent deployed to Copilot/Teams Can't Display Images Generated via Code Interpreter
Hello everyone, I’ve been developing an agent in the new Microsoft Foundry and enabled the Code Interpreter tool for it. In Agent Playground, I can successfully start a new chat and have the agent generate a chart/image using Code Interpreter. This works as expected in both the old and new Foundry experiences. However, after publishing the agent to Copilot/Teams for my organization, the same prompt that works in Agent Playground does not function properly. The agent appears to execute the code, but the image is not accessible in Teams. When reviewing the agent traces (via the Traces tab in Foundry), I can see that the agent generates a link to the image in the Code Interpreter sandbox environment, for example: `[Download the bar chart](sandbox:/mnt/data/bar_chart.png)` This works correctly within Foundry, but the sandbox path is not accessible from Teams, so the link fails there. Is there an officially supported way to surface Code Interpreter–generated files/images when the agent is deployed to Copilot/Teams, or is the recommended approach perhaps to implement a custom tool that uploads generated files to an external storage location (e.g., SharePoint, Blob Storage, or another file hosting service) and returns a publicly accessible link instead? I've been having trouble finding anything about this online. Any guidance would be greatly appreciated. Thank you!Azure AI Foundry Agent Unable to Use Credentials Stored in Key Vault Through Playwright MCP Tool
Hello everyone, I am trying to understand how Azure AI Foundry agents interact with Azure Key Vault when using custom MCP tools, and I would appreciate any guidance from the community. My Setup - Created an Azure AI Foundry agent. - Created an Azure Key Vault and configured all permissions according to Microsoft's official documentation. - Stored the required website credentials (username and password) in the Key Vault. - Deployed the official Playwright MCP Docker image. - Exposed the MCP server using ngrok and verified that the endpoint is accessible. - Connected the MCP endpoint as a Custom MCP Tool in Azure AI Foundry. - Performed all configuration through the Azure portal, Foundry UI, and Playground only (no SDK or custom application code involved). The Issue The agent can access and use the Playwright MCP tool. However, when I ask it to log in to a website using credentials that are already stored in Key Vault, it does not populate the username and password fields. My expectation was that the agent would be able to retrieve the secrets from Key Vault and provide them to the Playwright tool during execution. Questions Is there currently a supported mechanism for Azure AI Foundry agents to automatically retrieve Key Vault secrets and pass them to a Custom MCP tool? Does the Playwright MCP Docker image have any built-in integration with Azure Key Vault? When using only the Foundry UI (without SDK code), can a Foundry agent securely inject Key Vault secrets into MCP tool calls? Are additional configurations required beyond Key Vault permissions and agent connections? Has anyone successfully implemented a similar setup where a Foundry agent uses credentials stored in Key Vault to perform browser automation through Playwright MCP? Any clarification on the expected architecture and whether this scenario is currently supported in Azure AI Foundry would be greatly appreciated. Thank you.Auto-Generated Rubric Evaluators: Building Context-Aware Evaluators for AI Agents
Authors: Shuo Qiu, Sydney Lister, Ilya Matiach, Ali Mahmoudzadeh, Salma Elshafey, José Santos, Vivek Bhadauria, Morteza Ziyadi, April Kwong Why Your Agent Needs a Task-Specific Evaluator Picture a customer-service agent for a telecom company. A customer messages in asking to switch plans and get a refund for last month's overcharge. The agent needs to verify the customer's identity and confirm the new plan before ending the conversation. Miss the verification step and you have a security incident. Those success criteria are specific to this one scenario. The auto-generated rubric evaluator is designed to help address this: use the context you already have to generate a task-specific rubric evaluator that returns a weighted score with per-dimension explanations, then can be reused across iterations. How We Validated Evaluator Quality We validate auto-generated rubric evaluators across four aspects: Verdict Validity — whether judgments on real cases reflect what a competent reviewer would conclude. Rubric Validity — whether generated rubrics capture the task requirements and failure modes. Manual Quality Inspection — whether judgments on real cases look right to a human reviewer. Reliability and Separability — whether judgments are stable across repeated runs and distinguish stronger from weaker candidate agents. Validation Results 1. Agreement with Trusted Reference Signals We first validate the auto-generated rubric evaluator end-to-end: we use the rubric generator to produce the rubric's dimensions, then the rubric evaluator scores each case against them. We use GPT-5.4 for both rubric generator and rubric evaluator. The first question is whether those end-to-end scores move with signals teams already trust. For example, does the rubric evaluator give lower scores to failed cases, and higher scores to successful ones? We start by choosing benchmarks the community already uses as reference points: Dataset What It Tests JSON Editing Deterministic structured-editing tasks where outputs can be checked exactly. TauBench Telecom Customer-service agent tasks requiring policy following, tool use, and task completion. The Agent Company Long-horizon workplace-agent tasks with multi-step tool use. We InspectAI’s 10-case subset. BFCL Multi-Turn Tool Calling Multi-turn function-calling behavior across realistic tool-use scenarios. LiveClawBench Open-ended web-agent tasks that require browsing, interaction, and judgment. Retail-Agent Customer Service Real production-style retail support conversations. We then ask the generation pipeline to generate rubric evaluators for each scenario, and measure the correlation between the evaluator's scores and the trusted reference signals. For the three datasets with per-case reference signals, we can directly check whether the evaluator gives higher scores to successful cases than failed ones. We then create traces from different candidate agents. In these experiments, each candidate agent uses the same task setup and prompt but a different underlying model, which gives us a controlled range of stronger and weaker agent behaviors. Because the evaluator returns a continuous score, we use receiver operating characteristic area under the curve (ROC AUC) when the trusted case-level signal can be read as success versus failure. It measures how often, when comparing a successful case with a failed case, the evaluator assigns the successful case the higher score. In these experiments, generated rubric evaluators align well with trusted signals at the case level, with ROC AUC of 0.794 on TauBench Telecom, 0.869 on The Agent Company, and 0.972 on JSON Editing. An important goal of evaluation is to score candidate agents that perform better on the reference signal also higher by the evaluator. This is more directly relevant when choosing among candidate agents, and it is a more forgiving test of alignment because aggregated scores are less sensitive to noise in individual judgments. We measure this with aggregate candidate-agent Spearman ρ, which checks whether the evaluator ranks candidate agents the same way as the oracle — a ρ of 1.0 means the evaluator's ranking is perfectly aligned with the oracle's, while 0 means no relationship. For BFCL and LiveClawBench, the oracle ranking comes from their official leaderboard scores. At the aggregate candidate-agent level, Spearman ρ ranges from 0.69 on The Agent Company to 0.98 on JSON Editing across all five benchmarks. Aggregation reduces per-case noise, so the candidate-agent ranking is the more relevant view when the goal is agent selection. 2. Rubric Quality on GDPVal GDPVal is a benchmark that measures how well AI models perform real-world, economically valuable work in sectors such as government, manufacturing, and technical services. This benchmark includes a rubric for each task, authored by a domain expert, which is useful for rubric-validity measurement. We ask the rubric generator to produce a rubric for each test case, then use a separate matching judge to match the generated dimensions to the expert dimensions. This gives us two metrics for rubric quality: Recall. For each annotated dimension, did at least one generated dimension express a similar requirement? Precision. For each generated dimension, did at least one annotated dimension express a similar requirement? Under this setup, the generated rubric achieved 72.1% recall and 86.4% precision against the expert dimensions on GDPVal tasks. 3. Manual Quality on Retail-Agent Conversations For a real-world retail-agent customer-service dataset, we generated a rubric with six dimensions, then graded 12 conversations over those dimensions, and manually inspected every case-by-dimension judgment. In this small sample (12 conversations), the reviewer disagreed with only one of the 72 case-by-dimension judgments. Most neutral cases involved applicability questions that the evaluator flagged inconsistently. Reliability and Separability Another key question is how reliable the evaluator's scores are. We look at two things: reliability (does the same case get the same score next time?) and separability (can the evaluator confidently rank two candidate agents against each other?). Reliability If you re-grade the same case tomorrow, do you get the same score? We measure this two ways: single-measure intraclass correlation, ICC(3,1) measures how much of the score variance comes from real case differences rather than repeat noise, and Kendall's W measures rank reliability across repeats — 1.0 means the evaluator ranks cases in the same order every time. On JSON Editing, single-measure intraclass correlation, ICC(3,1), is 0.852 and Kendall's W is 0.767, which means re-running the evaluator on the same case gives similar numbers under repeated runs in this experimental setup. TauBench Telecom shows similarly strong reliability, with ICC(3,1) of 0.85 and Kendall's W of 0.89 under the same recommended configuration. Separability Separability measures whether the score is decisive: when you put two candidate agents side by side, can the evaluator confidently say which one is better? We report mean pairwise bootstrap confidence, which measures ranking stability. For each pair of candidate agents, we resample cases and recompute each agent's mean evaluator score. The pair confidence is the fraction of bootstrap samples supporting the more common ordering: a value near 0.5 means the ordering is unstable, while a value near 1.0 means the evaluator consistently separates that pair. We average this across all candidate-agent pairs. The candidate-agent intervals are tight on JSON Editing and TauBench Telecom. Mean pairwise bootstrap confidence is 0.96 on JSON Editing dataset and 0.95 on TauBench Telecom dataset. Get Started The auto-generated rubric evaluator's results may vary depending on task design, input quality, and evaluation setup. Start with a clear, well-defined description for your evaluation in the prompt field, include as much high-quality context as possible, such as the agent definition and examples, and review the generated rubric carefully before using it. Run it against a small set of known-good and known-bad cases to understand how the score reflects different failure modes. Try the workflow in the Foundry portal and follow the rubric evaluator tutorial. For a demo that covers Rubric in the broader observability workflow, watch the Build breakout session From observability to ROI for AI agents on any framework. For the full set of Build observability announcements, read Build 2026: From observability to ROI for AI agents on any framework.Evaluate before you ship: introducing the Voice Live Evaluation Harness
You've built a voice agent on Azure Voice Live. It demos beautifully. Then a teammate asks the question that keeps every voice-agent team up at night: "How do we know it's actually good — across 200 customer calls, not the three we just listened to?" Until today, the honest answer was: put on headphones. Manual listening. Subjective scoring in a spreadsheet. No baseline, no regression signal, no way to defend a model swap with data. We're releasing the Voice Live Evaluation Harness to change that. It's an open-source, deployable evaluation pipeline that runs pre-recorded multi-turn audio through your Voice Live agent and scores every turn with the same evaluators built into Microsoft Foundry — automatically, repeatably, and in parallel. TL;DR Two flavors, one repo. Run the CLI harness locally against a Foundry project for fast iteration, or deploy the evaluation agent into your Azure subscription with the Azure Developer CLI (azd) for a fully-hosted evaluation backend. 13 built-in evaluators score every turn — intent resolution, task adherence, task completion, response completeness, tool-call accuracy, groundedness, and more — viewable per-turn and in aggregate inside the Foundry portal. Supports the three Voice Live modes you actually ship in — Semantic VAD, Push-to-Talk, and Foundry Agent mode — including multi-turn conversations with tool calls and grounding. Grows with your agent. Start with the sample datasets, then layer in audio collected from user testing and production traffic so your evaluation set matures alongside the agent. 🔗 Repo: microsoft-foundry/voicelive-evaluation · Docs: Evaluate Voice Live agents (preview) Why systematic evaluation matters for voice agents Text agents have a mature evaluation story. Voice agents don't — and the gaps actually matter more, because every voice failure happens in real time, in front of a customer, on a phone line you can't easily replay. The Voice Live Evaluation Harness closes that gap with four concrete capabilities: Establish a quality baseline. Run a representative audio dataset through your agent and get scores you can publish as your launch bar. Compare configurations side-by-side. Swap the underlying model (GPT-Realtime 1.5, Azure-Realtime, MAI-Transcribe-1.5), change the voice, tune VAD thresholds — and see exactly which knobs moved which scores. Catch regressions before users do. Wire it into CI and fail the build when intent resolution drops below your threshold. Optimize with data, not vibes. When task-completion drops, drill into the per-turn scores to see whether the agent failed to call the right tool, misunderstood intent, or generated an incomplete response. Keep iterating as production data rolls in. Start with the sample datasets, then grow your evaluation set with audio captured from internal testing, pilot users, and real production traffic. Re-run after every prompt tweak or model swap so the harness becomes a continuous quality signal — not a one-time launch checklist. How it works The pipeline is a five-stage loop: Audio Dataset. Multi-turn audio + expected behaviors in a simple JSONL schema. Four sample datasets ship in the repo (travel planning, complex data analytics, tool-calling tests, batch multi-conversation) so you can run end-to-end on day one. Voice Live API. Pick your Voice Live mode (Semantic VAD, PTT, or Foundry Agent), model, voice, and turn-detection settings via a JSON config file, then stream each turn of audio through the API — locally with the CLI harness, or, if you've deployed the evaluation agent, via the hosted Container App for long-running batches in your own subscription. Transcript + Response. Every turn produces an agent transcript, the model's response, and any tool calls it made — captured automatically for scoring. Foundry Evaluators. 13 built-in evaluators — powered by the same Foundry evaluator models (GPT-4.1-mini and o4-mini) used across Microsoft Foundry — judge every turn on intent resolution, task adherence, tool-call accuracy, groundedness, and more. Quality Scores. Per-turn and aggregate scores land in the Microsoft Foundry portal under your project's Evaluation tab — sortable, filterable, comparable across runs. Then loop. Audio captured from internal testing, pilots, and production traffic feeds back into the dataset — each pass makes the next evaluation more representative of what users actually do. What gets measured The accelerator ships 13 built-in evaluators out of the box, covering the dimensions that matter most for production voice agents: Category Evaluators Intent & task quality Intent Resolution · Task Adherence · Task Completion · Response Completeness Tool calling Tool Call Accuracy · Tool Call Parameter Validity · Tool Result Usage · Tool Call Success Content quality Groundedness · Relevance · Fluency · Coherence Conversational dynamics Turn-taking quality Every evaluator runs against the same Foundry evaluator models (GPT-4.1-mini and o4-mini) that power evaluation across the rest of Microsoft Foundry — so your voice-agent scores are directly comparable to your text-agent scores. Run the CLI locally against your existing Voice Live endpoint If you already have a Voice Live agent deployed and just want fast iteration on a laptop: git clone https://github.com/microsoft-foundry/voicelive-evaluation.git cd voicelive-evaluation/evaluation_harness python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt cp .sample_env .env # Edit .env with your AZURE_VOICELIVE_ENDPOINT python voice_agent_evaluation.py \ --config configs/sample_vad_realtime.json The full walkthrough — dataset schema, configuration reference, score interpretation, and troubleshooting — is in the documentation. Get started Repo: microsoft-foundry/voicelive-evaluation Docs: How to evaluate Voice Live agents (preview) We'd love your feedback — try it, file issues, and tell us which evaluators you wish you had.Now in Foundry: Tongyi-MAI Z-Image-Turbo, with FLUX.1-schnell and SDXL base 1.0
This week's Model Mondays edition pairs three models available through the Hugging Face collection in Microsoft Foundry: Tongyi-MAI's Z-Image-Turbo, a new designed for lower latency on a single GPU and native bilingual text rendering; Black Forest Labs' FLUX.1-schnell, a 12B rectified flow transformer distilled to 1–4 step inference and one of the most adopted open-weight image models since its 2024 release; and Stability AI's stable-diffusion-xl-base-1.0 (SDXL), a latent diffusion research model that can be used to generate and modify images based on text prompts. Models of the week Tongyi-MAI: Z-Image-Turbo Model Specs Parameters / size: 6B (BF16) Resolution: Up to 1024×1024 native Primary task: Text-to-image generation (English and Chinese) Why it's interesting (Spotlight) Scalable Single-Stream Diffusion Transformer (S3-DiT) architecture: Z-Image concatenates text tokens, visual semantic tokens, and image VAE tokens into a single unified input stream rather than running text and image through separate branches. This single-stream design can improve parameter efficiency relative to dual-stream DiT architectures at the same capacity. See the Z-Image technical report for details. 8-step inference at sub-second latency, fits in 16GB VRAM: Z-Image-Turbo is distilled with Decoupled Distribution Matching Distillation (Decoupled-DMD) and further refined with DMDR, a method that fuses DMD with reinforcement learning during post-training. The result is a model that runs 8 Number-of-Function-Evaluations (NFE) per image with no Classifier-Free Guidance (CFG)—which roughly halves the per-step compute compared to CFG-based inference. See the Decoupled-DMD and DMDR papers. Native bilingual text rendering and strong instruction adherence: Unlike most open-weight image models, which struggle with legible in-image text, Z-Image-Turbo renders complex English and Chinese text accurately which is useful for posters, signage, packaging mockups, and marketing creative. Try it Imagine you're a community programs coordinator at your city's parks department, planning a new summer event series — a "Cake Picnic in the Park" — designed to bring neighbors together over food in shared green space. The event is a few weeks out. You haven't booked bakery partners yet, so no actual cake exists, and you need marketing assets this week to start driving sign-ups: a hero image for the registration page, a flyer for community centers and libraries, social tiles for the city's channels. Use the prompt below and a photorealistic image, that can now be scaled to become additional assets like printed flyers or social images in minutes using image editing tools (or another model). Prompt: A round layered cake displayed on a white ceramic cake stand, topped with glossy fresh red cherries and smooth pastel pink buttercream frosting piped in delicate rosettes around the edge. One generous slice has been cleanly cut and removed from the front, revealing a perfect cross-section: four distinct horizontal layers alternating between soft pink sponge cake and fluffy white vanilla cream frosting. Professional bakery photography, soft natural window light from the left, shallow depth of field, marble countertop, warm and inviting atmosphere, photorealistic detail on the cake texture, cherry highlights, and frosting swirls. Black Forest Labs: FLUX.1-schnell Model Specs Parameters / size: 12B (rectified flow transformer) Resolution: Flexible up to 2 megapixels Primary task: Text-to-image generation Why it's interesting (Spotlight) Rectified flow transformer with adversarial distillation for 1–4 step inference: FLUX.1-schnell is the distilled, Apache 2.0 sibling of the FLUX.1 family. It uses a rectified flow formulation (a diffusion variant that learns straight-line probability paths between noise and data, reducing the number of solver steps needed) and is further compressed with latent adversarial diffusion distillation. The model generates high quality images in for latency-sensitive workloads. Permissive licensing for commercial use: Released under Apache 2.0, FLUX.1-schnell can be used for personal, scientific, and commercial purposes. This has driven broad adoption across product features that need an open, redistributable image backbone. Strong prompt adherence at its parameter range: At 12B parameters, FLUX.1-schnell sits between the SDXL family and frontier proprietary image models, and it remains a common reference point for evaluating open image generation prompt following—particularly for complex compositional prompts and longer captions—roughly two years after its initial release. Try it Hugging Face Spaces give developers the ability to experiment and try new models before deploying them. Test out a few prompts here: https://black-forest-labs-flux-1-schnell.hf.space then when you are ready, deploy the model in Microsoft Foundry. Stability AI: stable-diffusion-xl-base-1.0 stabilityai/stable-diffusion-xl-base-1.0 · Hugging Face Model Specs Parameters / size: 2.6B UNet (≈3.5B total with text encoders) Resolution: 1024×1024 native Primary task: Text-to-image generation Why it's interesting (Spotlight) Dual text encoder design and an ensemble-of-experts pipeline: SDXL uses two pretrained text encoders—OpenCLIP-ViT/G and CLIP-ViT/L—concatenated to capture both broad semantic alignment and finer-grained token-level cues. It can be run standalone or paired with the SDXL refiner in an ensemble-of-experts pipeline where the base model handles early denoising and the refiner specializes in the final steps. See the SDXL report for the original training and architecture details. CreativeML Open RAIL++-M licensing for managed deployments: SDXL is distributed under the CreativeML Open RAIL++-M license, which permits commercial use and downstream fine-tuning with documented use restrictions. Try it To go deeper on SDXL, take a look at Stability AI's generative-models GitHub repository, which implements the most popular diffusion frameworks for both training and inference and continues to expand with new capabilities like distillation. Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry in two ways. The first by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. The second way is direct through the Hugging Face Hub, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure. Learn how to discover models and deploy them using Microsoft Foundry documentation: Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft Foundry"Not Available in Your Region" Isn't a Dead End: A Security Assessment of Global Deployments
You want to build with the latest Microsoft Foundry model. You checked the regional availability, and it isn't there yet — only Global Standard. Now you're weighing the capability you actually need against your instinct to keep everything in a regional SKU. This post is for that moment. This is a more common situation than people realise. Microsoft typically releases new and preview models on Global first, then expands into specific regions over time as capacity is built out. It isn't an oversight. It's how Microsoft makes new capabilities available to the broadest set of customers as quickly as possible. If you want those capabilities, Global is the path. The good news is that the path is well-paved. Microsoft Foundry Global Standard is a secure, enterprise-grade deployment type backed by the same Azure controls you already rely on, with explicit contractual commitments on how your data is used. The data protection guarantees don't change because the model is newer or because regional capacity hasn't caught up — they're the same on day one of a new model on Global as they are on a model that's been deployed regionally for a year. The rest of this post walks through what Microsoft commits to, what you get out of the box, what you add on top, and the small number of cases where Global is genuinely the wrong choice. It's written for three audiences: Developers who want to know if they're allowed to ship on Global. Solution architects weighing the model choice against latency, quota, and resilience. Security architects who need to map Foundry's behaviour to enterprise controls before they sign off. Where does my data actually go? This is the question that drives most of the concern, and the answer has two parts. Mixing them up is what causes the confusion. Data at rest stays in the Azure geography of your Foundry resource. That includes your configuration, uploaded files, stored artifacts, and logs. This is true for Global deployments, exactly the same as it is for regional ones. Microsoft commits to this in the Azure data residency page. Data in processing is different. When you send a prompt, the model processes it in memory for a few hundred milliseconds and returns a response. For Global deployments, that processing can happen in any Azure region where the model is hosted. This is how Microsoft gives you the highest available capacity and the broadest model access. The prompt and response are not persisted as part of inference processing in the region that processed them. Once you separate "where my data lives" from "where the request runs," the residency picture becomes much clearer. Your customer data lives where you put it. The model that processes that data runs on Microsoft's global fleet. You can read the official description on the Microsoft Foundry deployment types page. What Microsoft commits These commitments are contractual, not marketing language — they sit inside Microsoft's Product Terms and Data Protection Addendum. According to the data privacy page for Azure Direct Models, your prompts and completions are not used to train Microsoft or OpenAI models, and your fine-tuned models are exclusively yours. Microsoft is also explicit that your data does not touch consumer OpenAI services: "Microsoft hosts the Azure Direct Models in Microsoft's Azure environment and Azure Direct Models do NOT interact with any services operated by Azure Direct Model providers, for example, OpenAI (e.g. ChatGPT, or the OpenAI API)." For partner and community models served through serverless APIs, the model catalog data privacy page confirms that those models are stateless and that Microsoft does not use prompts or outputs to train any model. What Global does NOT do A Global deployment does not replicate your stored data into other regions, does not expose your prompts to consumer OpenAI services, and does not use your inputs or outputs for training. The only cross‑region behavior is the transient execution of model inference, which is stateless and not customer‑addressable. What Global gives you on day one Before you configure anything yourself, a Global Standard deployment already includes the following: Encryption at rest using FIPS 140-2 compliant 256-bit AES with Microsoft-managed keys, applied transparently. See the Microsoft Foundry architecture page. Encryption in transit using TLS 1.2 or higher, enforced by the platform. Microsoft Entra ID authentication with Azure RBAC. Foundry separates control-plane actions (like creating deployments) from data-plane actions (like invoking models), so you can grant least privilege without writing custom roles. Tenant isolation. Your Foundry resource lives in your subscription, your data lives in your tenant, and any fine-tuned models you create are exclusively yours. Compliance inheritance. Foundry runs on Azure and inherits Azure's compliance controls, including ISO 27001, SOC 1/2/3, HIPAA, PCI DSS, FedRAMP, and many others. The current authoritative list is in the Azure compliance offerings catalogue and the Microsoft Trust Center. This baseline, with no extra configuration, already meets the security posture most enterprise teams target for new workloads. The controls you already know Securing Microsoft Foundry uses the same building blocks as securing any other Azure PaaS service. If your team already knows how to lock down Azure Storage or Azure SQL, you already know how to lock down Foundry. Developers see familiar patterns. Architects get a clean fit into the landing zone. Security architects review the same control surfaces they review elsewhere. The controls you'd apply are exactly what you'd expect: Private networking: Map the Foundry resource to a private IP using Private Link, back it with Private DNS, disable public network access, and route egress through Azure Firewall or an NVA. For agent workloads, Microsoft publishes a private networking template for Foundry Agent Service you can deploy with Bicep or Terraform. Note that Private Link secures the path to the endpoint, not the routing of requests inside the model fleet — you get a private network path without giving up Global's capacity benefits. Azure APIM GenAI gateway: Put Azure API Management's GenAI gateway in front of your Foundry Global deployments to control who can call models, how much they can use, and under what policies, independent of where inference runs. It enforces central auth, per‑consumer token limits, logging, and policy controls, turning Global deployments from “globally available” into centrally governed and auditable services. Identity and secrets: Use Managed Identity for application-to-model calls and avoid embedding API keys in code. Apply Conditional Access to admin sign-in and use Privileged Identity Management for just-in-time elevation on admin roles. Customer-managed keys: If your compliance regime requires key ownership, enable CMK on the Foundry resource via Azure Key Vault for rotation, revocation, and separation of duties. Logging and monitoring: Send diagnostics to a customer-owned Log Analytics workspace, enable the Azure Activity Log, and alert on token-usage spikes, unusual source IPs, and repeated authentication failures. Governance at scale: Use Azure Policy to enforce baselines (allowed locations, mandatory diagnostics, required private access) across your tenant, and pair it with Microsoft Defender for Cloud for continuous posture management. The risk that deserves attention: Data Exfiltration The most common security risk in any LLM deployment, on any SKU, is not Microsoft's infrastructure. It's the application layer. Examples include over-broad RAG retrieval pulling data the user shouldn't see, a tool-calling agent reaching an unintended destination, or a prompt that quietly echoes PII into a downstream log. These risks exist on Global, Data Zone, and Regional deployments equally. Choosing a more restrictive SKU does not mitigate them. The good news is that the mitigations are well understood and entirely under your control: Use Private Endpoints for Storage, AI Search, Cosmos DB, and any other backing services your application uses for RAG, so retrieval traffic stays off the public internet. For tool-calling and agent scenarios, route outbound traffic through Azure Firewall with FQDN filtering, and keep an explicit allowlist of destinations the agent is permitted to reach. Apply DLP and redaction at the application layer for high-risk data classes, before that data ever becomes part of a prompt. Treat prompts and completions as transient. Don't persist them unless you have a specific, auditable reason to do so. Doing this work on a Global deployment gives you exactly the same protection as doing it on a regional one. Is Global Deployment right for you? For most teams building on Microsoft Foundry, the answer is yes. Global Standard gives you: The highest default quotas and the broadest model availability in the catalogue. First access to new models and features, often weeks or months ahead of regional rollouts. Elastic absorption of demand spikes through Microsoft's global capacity pool. A simpler architecture, with no regional duplication or custom failover logic. The full Azure security stack: Entra ID, RBAC, Private Link, CMK, Azure Policy, Defender for Cloud, and Monitor. Contractual guarantees that your data isn't used for training and isn't shared with consumer OpenAI services. Global is not the right choice when a specific regulation explicitly requires inference processing to occur within a named country or zone. Note the word "processing" there: not data at rest, but the transient processing of the prompt itself. These cases do exist, particularly in some government, healthcare, and financial sector contexts, and Microsoft Foundry offers Data Zone (US or EU) and Regional SKUs for exactly those situations. But unless someone has pointed you at a specific clause in a specific regulation that names processing locality, you most likely don't need to step down from Global. Summary Microsoft Foundry Global deployments are secure, compliant, and enterprise‑ready. Data at rest remains in your chosen Azure geography. Prompts and completions are not used for training and do not interact with consumer AI services. Encryption, identity, networking, logging, governance, and monitoring are all first‑class Azure controls. Modified Abuse Monitoring is available for qualifying enterprise customers where required. A short summary for each audience: Developers: you can build on Global with confidence, using the Azure patterns you already know. Solution architects: Global is a sensible default unless a regulatory requirement specifically rules it out. Data Zone and Regional remain available for the cases that need them. Security architects: the control surfaces are familiar, the contractual commitments are explicit, and Global can be approved on the same basis as any other Azure PaaS service handling equivalent data classifications. If you've been defaulting to a regional SKU "just to be safe," it's worth taking a fresh look at whether Global actually fits your workload. In most cases, it will.
