Release gates for agents
AI agent regression testing for CI
Agent changes can pass a demo and still get worse in production. AgentClash reruns real tasks, compares candidates against a baseline, and blocks pull requests when scorecards or evidence regress.
regression scorecard
threshold -3
threshold +15%
threshold +10%
required
replay evidence
agentclash compare gate --baseline run-stable --candidate run-pr-184
What a gate checks
Regression signals that survive model churn
Prompts, models, tools, and sandbox images all move. AgentClash makes the workload repeatable so the release decision is based on behavior, not a one-off transcript.
Baseline versus candidate scorecards
Replay timelines for every failed gate
Artifact checks for files, logs, and evidence
Cost and latency thresholds for production budgets
Challenge packs that make failures repeatable
Pull request gates for model, prompt, and tool changes
Workflow
From escaped failure to pull request gate
Freeze the workload
Turn a real failure or release risk into a challenge pack with inputs, tools, artifacts, and scoring rules.
Compare candidate to baseline
Run both agents under the same constraints and compare correctness, latency, cost, and evidence.
Block risky changes
Fail the pull request when the scorecard regresses past the release gate threshold.
Promote failures
Convert escaped failures into reusable regression cases so the same mistake stays covered.
Start with docs
Wire regression gates into the release loop
Start with one challenge pack and one release gate. Then add escaped failures as reusable cases instead of rebuilding the entire eval stack every time an agent changes.
FAQ
Questions teams ask before gating agent releases
What is AI agent regression testing?
AI agent regression testing reruns repeatable agent tasks against a candidate agent or model, compares the result to a baseline, and flags behavior that got worse before it ships.
How do AgentClash CI gates work?
AgentClash can run a challenge pack in CI, compare candidate and baseline scorecards, then fail a pull request when correctness, cost, latency, or required artifacts cross the threshold you set.
Can teams debug a failed agent gate?
Yes. Each run keeps replay evidence, tool calls, logs, artifacts, and scorecards so reviewers can see why the candidate failed instead of guessing from a final answer alone.