Engineering AI Agent Reliability.
Technical writing on AI regression testing, production debugging, and agent observability.
Capturing Failure Contexts Without Breaking Your Agents
Instrumenting a production AI agent for failure capture requires careful scoping. This post covers how to collect what you need for a replay environment without interfering with the agent's live behavior.
Structured Diff: What a Verified Fix Actually Looks Like
A fix that passes a replay environment looks different from a guess. We walk through the anatomy of a structured diff proposal and what makes one trustworthy enough to merge.
The Production Testing Gap in AI Agent Development
Most teams have unit tests and evals, but almost none have a way to reproduce specific production failures. That gap is where undiagnosed regressions accumulate.
Understanding Nondeterminism in LLM Pipelines
Nondeterminism in language model outputs is not just a nuisance. It changes what reproducibility means for agent testing and shapes how replay environments need to be designed.
CI Integration for AI Agents: Running Replay Tests in Your Pipeline
Replay environments that only run manually are half-useful. This guide covers connecting captured failure cases to your existing CI pipeline so regressions surface before merge.
From Bug Report to Test Case: The Relai Workflow
A production bug report is raw signal. Relai converts it into a bounded, replayable test case that can verify any proposed fix without manual reproduction steps.
Proposing Fixes That Earn Their Merge
A fix proposal is only credible if it passes the failure it claims to solve. We explain how Relai ties every proposed change to a concrete replay gate before the pull request opens.
Anatomy of a Replay Environment
A replay environment is not just a saved trace. It bundles the inputs, tool state, model version, and expected output into a single executable artifact. This post explains each component.
Why Agent Regressions Are Harder Than Traditional Bugs
When a classical service regresses you can bisect commits and diff outputs. AI agent failures rarely bisect cleanly because the failure depends on model behavior, context, and tool state simultaneously.