Benchmarks
AI agent benchmarks grounded in real workloads
Leaderboards are a starting point, not a release decision. AgentClash lets teams benchmark agents on the tasks they actually ship — with the same tools, budgets, and evidence requirements.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
Better than a one-number benchmark
Built for reviewable agent decisions
A useful AI agent benchmark reports correctness, cost, latency, tool strategy, and artifact quality on workloads your team owns.
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
Benchmark 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.
Build a benchmark you can reuse
Bring your first workload into the loop
Encode workloads as challenge packs so benchmark runs become regression gates instead of one-off demos.
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 benchmark FAQ
How is AgentClash different from public leaderboards?
Public leaderboards summarize generic tasks. AgentClash benchmarks your agents on your tools, repositories, APIs, and release constraints.
Can we benchmark multiple agents on the same task?
Yes. AgentClash runs candidates on the same challenge pack with the same sandbox policy and produces comparable scorecards.
Can a benchmark become a regression test?
Yes. The same challenge pack can power ad-hoc benchmarks and CI gates once your team trusts the scoring rules.