LLM agents
LLM agent evaluation beyond single-turn answers
LLM agents plan, call tools, inspect results, and recover from mistakes. AgentClash evaluates that full loop on your workloads so model swaps and prompt changes do not hide regressions.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
Evaluate the agent loop, not just the model
Built for reviewable agent decisions
LLM agent evaluation needs the same task, same tools, and preserved evidence — otherwise you are comparing demos, not systems.
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
A practical LLM agent eval workflow
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.
Bring your workload into AgentClash
Bring your first workload into the loop
Start with one real failure, encode it as a challenge pack, then scale to model comparisons and CI gates.
Agent evals
Real-task agent evals with replay evidence and CI gates.
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
LLM agent evaluation FAQ
What should LLM agent evaluation measure?
At minimum: task success, tool strategy, artifacts produced, cost, latency, and whether the agent stayed inside policy. AgentClash captures all of that in a scorecard.
Can we compare multiple LLM agents fairly?
Yes. AgentClash runs candidates on the same challenge pack with the same tool policy, time budget, and sandbox resources.
Does AgentClash work with hosted model providers?
Yes. AgentClash routes to major LLM providers and normalizes tool-call shapes so evals stay comparable across models.