MICROSOFT OPEN-SOURCES ASSERT; AGENT EVALUATION SHIFTS INTO CI
Microsoft open-sourced ASSERT, a framework that turns policy and spec text into executable tests for enterprise AI agents. ASSERT generates scenarios, datasets...
Microsoft open-sourced ASSERT, a framework that turns policy and spec text into executable tests for enterprise AI agents.
ASSERT generates scenarios, datasets, metrics, and scorecards directly from written requirements and governance docs, and plugs into pipelines to gate releases against policy-aligned checks source.
In parallel, Label Studio Enterprise added Interfaces that let teams vibe code custom labeling and evaluation UIs with agent help, while keeping enterprise-grade workflow, compliance, and analytics in place source.
Open source is hardening too: agentic-qe v3.10.6 adds evidence-class labels, behavioral safety evals, invariant CI checks, and auditable benchmark rubrics to reduce silent regressions in shipped agents source.
Policies and PRDs can now become regression tests that catch risky agent behavior before production.
Evaluation UX and ops are maturing, lowering the cost to run repeatable, auditable AI QA at scale.
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Feed a real governance or safety policy into ASSERT, generate tests, and run them in CI against a staging agent.
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Add agentic-qe's verify:invariants check to your PR workflow to prevent prompt/config drift between merges and releases.
Legacy codebase integration strategies...
- 01.
Start by converting one high-impact policy (PII handling, deletion rules) into ASSERT tests and gate merges on them.
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Map eval results to existing SLOs/error budgets; keep a manual override path for false positives during rollout.
Fresh architecture paradigms...
- 01.
Design eval-first pipelines: ASSERT for policy-derived tests, Label Studio Interfaces for human-in-the-loop review, and CI gates from day one.
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Standardize benchmark rubrics and store hashes alongside code to keep lineage auditable.
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