Agent testing
AI agent testing that looks like release engineering
AI agent testing should feel closer to software testing than prompt tweaking. AgentClash turns real failures into repeatable challenge packs, compares candidates to baselines, and keeps the evidence reviewers need.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
What production-grade agent testing covers
Built for reviewable agent decisions
Testing agents means checking behavior across the whole run — not approving a single polished answer from a cherry-picked prompt.
Sandboxed real-tool execution
Head-to-head runs with fair constraints
Scorecards for correctness, cost, latency, and tool strategy
Replay trails for every important action
Challenge packs that turn failures into reusable tests
CI gates for baseline versus candidate decisions
Workflow
Test loop
Package the task
Describe the workload as a challenge pack with inputs, tools, scoring rules, and artifacts.
Race the agents
Run every candidate against the same task with the same constraints.
Replay the evidence
Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence after the run.
Gate the release
Compare candidate and baseline runs, then fail CI before a regression reaches users.
Start testing with docs
Bring your first workload into the loop
Write a challenge pack, run a race, inspect replay, then wire the same workload into CI.
FAQ
AI agent testing FAQ
Is AI agent testing different from LLM testing?
Yes. LLM testing often means scoring one response. AI agent testing evaluates plans, tool calls, artifacts, recovery behavior, and whether the task actually finished.
Can non-ML engineers review agent test failures?
Yes. Replay timelines, artifacts, and scorecards are designed for reviewers who need to understand what changed without reading raw model traces alone.
How do we keep tests from getting stale?
Promote escaped production failures into challenge packs and regression suites so the same mistake stays covered after the next model swap.