How to Benchmark AI Agents on Your Own Data (Not Public Leaderboards)

Public leaderboards are useful for orientation. They are a poor release gate for the agent your customers actually use.

Leaderboards optimize for tasks that are public, static, and comparable across vendors. Your agent optimizes for private repos, internal APIs, ticket schemas, and policies that never appear on LMSYS.

Step 1: Pick workloads, not models

Start from three real jobs your agent must complete:

• a support refund with policy checks
• a coding fix in a service your team owns
• an internal ops workflow with approvals

Each job becomes a challenge pack case: inputs, tools, validators, artifacts. That is how agent evals turn anecdotes into coverage.

Step 2: Freeze the benchmark

Version the pack before you race candidates. If the task drifts every week, you are comparing runs that never met on the same track.

Freeze:

• fixtures and seed data
• tool allowlists and network policy
• scoring rules and pass thresholds
• time and token budgets

This is the same discipline public benchmarks use — except the workload is yours.

Step 3: Run head-to-head, not staggered

Run baseline and candidate agents concurrently on the same pack. Staggered comparisons leak cache warmth, provider weather, and prompt drift into the verdict.

AgentClash races agents on the same task at the same time in isolated sandboxes. See why we race agents head-to-head.

Step 4: Capture replay, not just scores

A number without a trajectory is not actionable. Reviewers need:

• tool paths and retries
• produced files and logs
• cost and latency per successful task
• judge or validator evidence

That replay is what makes a benchmark defensible to security and platform leads. The LLM agent evaluation page outlines the product surface.

Step 5: Promote failures into gates

When a candidate regresses, promote the failure into permanent coverage and wire a CI gate. Benchmarks that nobody gates on become slide deck decorations.

Follow CI/CD agent gates once you have a baseline you trust.

Feeding /benchmarks without fiction

We publish field reports on /benchmarks when we have reproducible packs and honest sample sizes. Your private benchmark should follow the same rule: publish internal scorecards, not vibes.

If you want help freezing the first pack, see eval services or the free enterprise pilot.