Agent testing
AI agent testing that starts from real failures
AI agent testing should feel closer to software testing than prompt tweaking. AgentClash turns traces, golden datasets, and escaped failures into repeatable tests with baseline comparisons.
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, including tool calls, retrieval, cost, latency, and artifacts.
OpenTelemetry-compatible trace import
Pinned datasets and golden test cases
Baseline versus candidate regression checks
Replay trails for tool calls, outputs, and artifacts
Scorecards for correctness, cost, latency, and evidence
CI gates for prompt, model, RAG, and tool changes
Workflow
Test loop
Import the evidence
Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.
Pin the baseline
Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.
Replay the evidence
Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.
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
Import a trace or dataset, run an eval, inspect replay, then wire the same workload into CI.
Agent evals
Real-task agent evals with replay evidence and CI gates.
LLM agent evaluation
Evaluate LLM agents on full trajectories, not one-shot answers.
Compare tools
See how AgentClash differs from prompt-eval platforms.
Datasets overview
Import examples, record baselines, sync regression suites, and gate CI.
Dataset CI gates
Fail builds when a candidate regresses against a pinned baseline.
CI/CD agent gates
Block pull requests when agent behavior gets worse.
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 datasets, challenge packs, and regression suites so the same mistake stays covered after the next model swap.