Agent evals
Agent evals that catch production regressions
Agent evals should answer whether a change made your agent worse. AgentClash imports traces or curated examples, compares candidates to baselines, and turns failures into release gates.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
What useful agent evals capture
Built for reviewable agent decisions
If your agent eval only checks the final string, you miss the tool misuse, RAG drift, latency spikes, and artifact gaps that show up in production.
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
From one eval to a reusable gate
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.
Run your first agent eval
Bring your first workload into the loop
Use datasets and challenge packs for repeatable workloads, then promote failures into regression cases your team can run in CI.
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
Agent eval FAQ
What is an agent eval?
An agent eval is a repeatable test that runs an agent on a task, scores the full trajectory, and compares the result to a baseline, dataset, or competitor.
How is AgentClash different from prompt evals?
Prompt evals score one model response. AgentClash evals multi-turn agents that use tools in a sandbox and scores correctness, cost, latency, and evidence quality across the run.
Can agent evals run in CI?
Yes. AgentClash can compare candidate and baseline scorecards, including dataset baselines, and fail a pull request when the configured release gate regresses.