AgentClash

AgentClash platform

AI agent evaluation platform for real tasks

Race agents against the same workload with the same tools and same constraints. AgentClash captures replay evidence, scorecards, artifacts, and release-gate verdicts so evals turn into decisions instead of dashboard archaeology.

live race
gate: pass

Codex

92correct patch, low cost

Claude

88strong reasoning

Gemini

73missed edge case

replay timeline

1cloned repo and installed dependencies
2ran failing test and inspected trace
3patched parser, reran unit suite
4attached diff, logs, and scorecard

ci verdict

Candidate clears release gate

Correctness +7, latency -11%, cost unchanged. No promoted failures exceeded the configured threshold.

agentclash ci run --manifest .agentclash/ci.yaml

What the platform evaluates

The whole agent, not just the final answer

AgentClash looks at the trajectory that produced the answer: tool choices, runtime behavior, artifacts, cost, latency, and whether the agent actually satisfied the task.

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

From one failed task to a reusable gate

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 at the same time 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.

Start with docs

Bring your first workload into the loop

Use challenge packs for repeatable tasks, then wire the same workload into local runs, hosted runs, or pull request gates.

FAQ

Questions teams ask before replacing benchmark-only evals

What is an AI agent evaluation platform?

An AI agent evaluation platform runs agents against repeatable tasks, captures what they did, scores outcomes, and helps teams decide whether an agent or model is ready to ship.

How is AgentClash different from static benchmarks?

AgentClash runs your agents on your tasks with the same tools, constraints, and time budget, then records replay evidence and scorecards that can become regression gates.

Can AgentClash run in CI?

Yes. AgentClash can compare a candidate run against a baseline and fail CI when the candidate regresses on the scorecard or release gate you define.