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Microsoft ASSERT
Microsoft ASSERT is a Microsoft Responsible AI evaluation harness for AI agents and LLM applications that starts from natural-language requirements or policies, generates test scenarios, runs them against a target, and writes local artifacts for inspection.
The GitHub README describes ASSERT as local-first, framework-agnostic, and trace-aware. Official materials list model endpoints through LiteLLM, agent and multi-agent systems through OpenInference, OpenTelemetry trace capture, JSON and JSONL artifacts, and a local viewer for comparing runs. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A spec-driven evaluation harness
ASSERT sits in the agent evaluation layer rather than the model or chatbot layer. It is built around turning written behavior expectations into generated cases, traces, scores, and reviewable run files.
Why it stands out
Requirements become test materials
Agent builders often need to check whether a system follows product requirements, tool-use rules, launch criteria, or other written expectations. ASSERT gives readers a concrete project to inspect for that requirements-to-evals workflow.
Availability
Repo, project site, docs, and Microsoft posts
Readers can open the GitHub repository, project site, Command Line technical post, and Microsoft Foundry Build post to inspect the setup path, example evaluation, artifacts, and stated limits.
Why it matters
What makes it useful
When an ASSERT case receives a failing score, the generated case, recorded trace, and judge rationale give a team something specific to inspect. They can review the written requirement or agent behavior, then rerun the case after a change.
What to know
Where it fits
ASSERT fits teams evaluating an agent or LLM application against written behavior requirements. It turns those requirements into cases that can be run, reviewed, and repeated as the application changes.
Notable points
What stands out
ASSERT turns written behavior requirements into single-turn and multi-turn cases, runs them against model endpoints or traced agents, and uses a model judge to score the results. LiteLLM and OpenInference integrations connect those targets; local JSON and JSONL files, traces, judge rationales, and a viewer let a person inspect what happened.
Before using
What to review
Current setup steps, Python version support, dependency extras, provider credentials, and example configuration before trying a run.
Which target system, judge model, model provider, trace collector, and external services would receive prompts, responses, traces, metadata, or evaluation artifacts.
The quality of the written behavior definition, because narrow and explicit requirements are easier to turn into useful scenarios than vague ones.
Generated cases, trace evidence, policy citations, judge rationales, and possible false positives or false negatives before using a result to make decisions.
The project's stated limits: synthetic interactions can miss production-only failures, and model-based judging still needs human review for subtle or high-stakes distinctions.
Reader fit
Who may find it relevant
Developers comparing ways to evaluate agents against written requirements.
Teams already using or testing agent frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, DSPy, LlamaIndex, AutoGen, or custom Python callables.
Readers studying trace-aware evaluation, local run artifacts, and repeatable agent regression checks.
Less relevant for readers who mainly want a consumer AI app, a model download, or a no-code automation builder.
Editorial note
Why LifeHubber lists it
ASSERT connects a written requirement to generated test cases, recorded traces, and judge rationales. Those artifacts let a team examine why a case was scored as pass or fail and whether its requirement needs clearer wording.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Connect written requirements to the system under test.
ASSERT turns written expectations into evaluation cases and reviewable artifacts. Continue with a framework that can host the agent workflow, or compare runtime controls that sit outside the evaluation itself.
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