Archal

About Archal

Archal is an improvement loop for production AI agents. The premise is that modern agents touch real services and can do real damage, so when one misbehaves you need more than a log line — you need the failure reproduced, fixed, and locked down against regression. Archal runs that in five stages. It grades every agent trace and flags the ones that are genuine failures rather than noise. It then recreates the exact environment the agent failed in, replaying the trace against a clone so the failure reproduces without touching your real systems. A separate coding agent writes a fix against your agent's harness repository. The fixed agent is re-run on that same reconstructed environment to prove the fix actually resolves the failure rather than looking plausible. Finally the failure is stored as an eval, so the same regression gets caught next time. The output arrives as a pull request on your harness repo, which keeps a human in the loop and fits the review process teams already have. Setup is two connections: GitHub access so the loop can open PRs, and access to wherever your traces live — your observability vendor or your own store — with an SDK to add tracing in a few lines if you don't have any yet. Archal supports a broad list of agent frameworks and SDKs including LangGraph, LlamaIndex, PydanticAI, AutoGen, CrewAI, Mastra, Google ADK, the Anthropic SDK, and the OpenAI Agents SDK.

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