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.
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
A practical LLM agent eval workflow
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.
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.
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 races 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.