Use case
Evaluate research agents with evidence quality
Research agents live or die on sourcing, synthesis, and artifact quality. AgentClash scores whether an agent found the right evidence, cited it correctly, and finished the investigation under budget.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
Research eval signals
Built for reviewable agent decisions
Measure coverage, citation quality, artifact completeness, and whether the agent stopped with a useful deliverable.
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
Research 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.
Encode investigations as packs
Bring your first workload into the loop
Turn recurring research workflows into challenge packs so every model or prompt change reruns the same investigation fairly.
FAQ
Research agent evaluation FAQ
What should research agent evaluation score?
Task completion, evidence quality, artifact completeness, tool discipline, and whether the final synthesis matches the sources collected.
Can evals include web or file tools?
Yes. Challenge packs define which tools agents may use and which artifacts must be produced for a pass.
How do teams debug a failed research run?
Replay shows each search, fetch, note, and synthesis step so reviewers can see where the investigation went off track.