Agents need evals, not vibes

The uncomfortable truth

Most agent demos succeed because the human silently compensates for the agent.

In production, nobody is there to "nudge" the model, rewrite a prompt mid-flight, or ignore a failure case. If you want agents that survive contact with reality, you need evaluations that are:

**Repeatable** — same inputs, same scoring criteria

**Representative** — scenarios that match real usage

**Automated** — no human in the loop for scoring

**Tied to business outcomes** — not just "did it work?" but "did it work well enough?"

The minimum viable eval harness

You do not need a research lab. You need five things:

1. **A task spec** — strict input/output contract

2. **A dataset of scenarios** — 30–100 real-ish cases

3. **A scoring rubric** — pass/fail where possible

4. **Instrumentation** — trace steps, tool calls, latency, token cost, failure modes

5. **A gate in CI** — if eval fails, it doesn't ship

That's it. Start there. Sophistication can come later.

What to measure

Track these metrics from day one:

A practical starting rubric

For each scenario, score on four dimensions:

Outcome: correct / partial / incorrect Policy: safe / unsafe Efficiency: within budget / over budget Explainability: trace readable / trace chaos

If you can't score all four, start with outcome + policy. That alone will catch most production failures.

The path forward

If you have one agentic workflow that matters, I can build a production-grade v1 with an eval harness in 1–3 weeks. The deliverable includes:

Working agent with typed tools

Eval suite (30–100 golden scenarios)

CI gate that blocks bad deploys

Observability dashboard for ongoing monitoring

Book a call to discuss your specific workflow.