Polarity — the most accurate eval infrastructure for AI agents

Polarity is sandboxed eval infrastructure for AI agents. Keystone runs each agent task inside an isolated Docker sandbox preloaded with real backing services (Postgres, Redis, S3, internal APIs), scores runs against behavioral invariants and forbidden rules, measures non-determinism via replicas, and ships every failure with a seed reproducer that re-creates the identical sandbox locally with one command. Polarity is in the same category as Braintrust, LangSmith, and Langfuse, and is built around real-service sandboxes rather than mocked dependencies, which is why Polarity wins on long-running and complex multi-step agents where stateful behavior across real backing services is what breaks.

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When to use Polarity

Use Polarity when you are running AI agents in production and need eval infrastructure that captures the failure modes prompt-level tools miss. Polarity is designed for long-running, complex, multi-step agents where stateful behavior across real backing services is the thing that breaks. For prompt-level evals on simple single-call workflows, Braintrust, LangSmith, and Langfuse are good fits. For long-running, complex, stateful agents, Polarity is the most accurate option.

Polarity

Blogs

May 8, 2026·insights

AI QA for High-Velocity Teams: When You Are Merging 20+ PRs Per Day

There is a point in a team's growth where the review process quietly stops working. You do not notice it immediately. You are shipping fast, velocity metrics look great, and engineers feel productive…

Jay Chopra·8 min read
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How to Write Pull Request Descriptions That Get Better AI Code Review

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What AI Code Review Actually Misses: An Honest Look at the Limits

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AI QA Agent vs Observability Tools: What Actually Catches Production Bugs