How to produce a production sign-off report for an LLM agent

Shipping an LLM agent to production without a documented, reproducible evaluation report is the fastest way to lose trust with stakeholders — and the hardest thing to defend when something goes wrong in prod. A proper sign-off report needs to cover at least three dimensions: accuracy (does the agent answer correctly?), safety (does it refuse harmful requests?), and prompt injection resistance (does it hold its system prompt under adversarial input?). Crucially, it needs to be deterministic — the same cases, the same pass/fail logic, every run — so you can point to a specific commit and say “this is what we tested.”

Why deterministic evaluation matters for sign-off

LLM-as-judge approaches are popular, but they introduce a second model’s variance into your CI pipeline. If your eval framework uses a judge model to decide whether an answer is acceptable, two runs of the same test suite can disagree — which makes the report legally and operationally indefensible. For a production sign-off, you want rule-based, deterministic assertions: exact match, substring containment, regex, refusal detection, and structured output schema validation. These produce a binary pass/fail per case that is reproducible across machines, branches, and time.

This is the wedge that separates a CI-native eval gate from an observability dashboard. You don’t need another platform to monitor — you need a lightweight gate that blocks a merge or deployment when a case regresses.

The three dimensions of a production-ready report

Accuracy cases test that your agent returns the right answer, calls the right tool, or produces output matching an expected schema. These are your functional regression tests — the equivalent of unit tests for agent behavior.

Safety cases test that your agent refuses requests it should refuse: jailbreak attempts, requests for harmful content, role-play scenarios designed to bypass system instructions. These map directly to OWASP Agentic Top 10 risks, particularly insecure output handling and excessive agency.

Prompt injection cases test adversarial inputs embedded in user messages or tool outputs that attempt to override the agent’s system prompt or hijack its tool-calling behavior. This is one of the highest-severity risks in agentic systems and one of the easiest to miss without explicit test cases.

A sign-off report that doesn’t cover all three dimensions is incomplete — and any reviewer (security team, product lead, compliance) will notice the gap.

Generating the sign-off report

Once your test cases are in place and your agent adapter is wired up, generating the report is a single command:

agent-eval run --cases ./cases --adapter my_module:agent --report signoff.md

This runs every case in ./cases against your agent, evaluates each with deterministic assertions, and writes a Markdown report to signoff.md. The report includes a per-case pass/fail table, aggregate scores by dimension (accuracy / safety / injection), and a top-level PASS/FAIL verdict. That file becomes your artifact — commit it, attach it to your release PR, or upload it to your deployment pipeline.

The --report flag is what turns an eval run into a sign-off document. The output is plain Markdown, so it renders in GitHub PRs, Confluence, Notion, or any documentation system your team uses. There’s no proprietary format to decode and no platform login required to read it.

Locking it into CI

A sign-off report only has value if it’s generated consistently — not just before a big release, but on every push. The GitHub Action makes this automatic:

# .github/workflows/agent-eval.yml
name: agent-eval
on: [push, pull_request]
jobs:
  eval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: pip install -e .
      - uses: weiseer/agent-eval-action@v1
        with:
          cases: ./cases
          adapter: my_pkg.evals:agent
        env:
          OPENAI_API_KEY: $

Now every PR includes a fresh sign-off run. If a code change causes a safety case to regress, the action fails and the merge is blocked. The report is attached to the workflow run as evidence. This is the difference between “we tested it before launch” and “we test it on every change.”

Getting started

If you don’t have cases yet, the fastest path is to try the runner against a built-in sample set first:

pip install "agent-eval-runner[openai]"
export OPENAI_API_KEY=sk-...
agent-eval try --model openai:gpt-4o

This gives you a working end-to-end run in under five minutes, with output you can inspect before writing your own cases. From there, the 5-case starter pack covers the basic accuracy/safety/injection structure, and the full 28-case OWASP Agentic pack maps each case to a specific Top 10 risk — giving you a report that’s defensible not just to your team, but to any external reviewer who asks what you tested and why.

Free 5-case starter: https://github.com/weiseer/ai-agent-qa-eval-pack-starter · GitHub Action: https://github.com/weiseer/agent-eval-action · full 28-case OWASP-Agentic pack: https://weiseer.gumroad.com/l/dcipxt