AI Agent Benchmarking Services: Custom Harnesses & Quality Gates
Your agent demos beautifully and then fumbles a tool call in production. I build custom benchmark harnesses — realistic multi-step scenarios, deterministic validation, judge rubrics, and CI regression gates — so you measure agent quality instead of hoping for it.
What a benchmark harness includes
Scenario-based benchmark suite
Multi-step, tool-use, and error-recovery scenarios modeled on your real workflows — single happy-path prompts are exactly what production traffic is not.
MCP / API harness with deterministic validation
Tool calls are checked deterministically wherever possible: right tool, right arguments, right order, correct handling of tool errors and ambiguous inputs.
LLM-as-a-Judge rubrics for open-ended steps
Where outputs cannot be string-matched, calibrated judge rubrics score them — designed around known judge biases so the scores are trustworthy.
CI-ready regression gates & dashboard
Every model swap, prompt change, or agent refactor runs the suite. You see pass rates per scenario class and catch regressions before deploy.
The methodology is documented in How to Benchmark Tool-Use in AI Agents — scenario taxonomy, validation design, and what 78 benchmark scenarios across 43 tools taught me about where agents really fail.
When teams bring me in
- Agents that orchestrate internal tools or APIs via MCP or function calling
- Multi-step workflows where a single wrong step corrupts the whole run
- Comparing frontier models before committing to a provider or an upgrade
- Proving agent reliability to enterprise buyers or compliance reviewers
- Catching silent regressions after prompt or model changes
Engagements run 4–8 weeks, fixed price from $15,000 — full scope and pricing here. Wondering if an open-source framework covers you instead? Read DeepEval vs. a custom eval harness.
Frequently asked questions
What is an AI agent benchmark harness?
A benchmark harness is a repeatable test system for agents: a suite of realistic scenarios (multi-step tasks, tool calls, error recovery), validation logic that scores each run (deterministic checks plus judge rubrics), and reporting that shows exactly where the agent succeeds and fails. It turns "the agent seems fine" into measurable pass rates you can gate releases on.
Why not just use public benchmarks?
Public benchmarks measure general capability, not your workflows, your tools, or your failure modes. An agent can score well on public leaderboards and still fail your specific tool-use sequences. Custom scenarios modeled on your actual traffic are what predict production behavior.
How long does a harness take to build?
Typically 4–8 weeks depending on the number of tools and workflows: scenario design, harness build with deterministic validation, judge rubrics for open-ended steps, and a CI-compatible dashboard. I have built harnesses up to 43 tools and 78 benchmark scenarios.
Which agent stacks do you work with?
Any stack that exposes tool calls: MCP servers, OpenAI/Anthropic function calling, LangChain/LangGraph-style orchestration, or custom in-house frameworks. The harness tests behavior at the interface level, so it stays valid when you refactor internals.