Before You Ship an AI Agent, Test the Rules

Most AI skills ship after a demo or two and a gut check. Someone runs through a few happy-path cases, it looks good, and it goes live. That’s not testing — that’s hoping.

The gap between “worked in the demo” and “works in production” is where agentic systems fail. Closing it requires recognizing that an AI skill has two fundamentally different testable surfaces.

Two Layers, Two Strategies

The first layer is deterministic. Every AI skill has rules baked into its logic that don’t require model inference to validate: format constraints, scoring formulas, threshold comparisons, classification codes, required output fields. These are checkable with code. A Python script can run hundreds of representative cases against these rules in seconds, flag every violation, and return a clean pass/fail — no LLM call required.

The second layer is the reasoning layer. This is where the model actually thinks: fuzzy matching against ambiguous input, inferring intent when context is incomplete, calibrating tone, deciding when to pause for human review. These behaviors can’t be unit tested with a script. They require the LLM.

The methodology is simple: keep these layers separate and test them differently.

Test the Deterministic Layer Exhaustively

Extract the deterministic rules from the skill’s logic and write a test script that runs them against a representative dataset — ideally real data, not synthetic examples. Run every edge case you can think of. Run malformed inputs. Run boundary values. This is cheap: no model calls, no latency, no cost per run. Do it hundreds of times.

Most teams skip this step because it feels like overkill for what seems like simple logic. It isn’t. Format violations, off-by-one scoring errors, and misclassified threshold cases are exactly the bugs that surface in production and are invisible in demos.

Test the Reasoning Layer Surgically

For the reasoning layer, don’t run a sweep — target. Pick 5 to 6 cases that represent the hardest judgment calls the skill has to make: the most ambiguous inputs, the scenarios where tone matters most, the cases where a HITL pause should trigger but might not. Run those through the LLM. Review the outputs manually.

This is where you find the gaps the deterministic tests can’t catch. In practice, this phase will surface at least one inconsistency — an ambiguous input the skill handles unpredictably, or a boundary case where the model’s inference doesn’t match the intended behavior. That inconsistency gets fixed before the skill ships.

That’s the argument for this methodology: the reasoning phase will find something. It always does. The question is whether you find it before your users do.

What to Take Away

Exhaust the deterministic layer first — it’s fast, cheap, and surfaces the obvious failures. Then run targeted LLM evals on 5 to 6 edge cases that actually stress the reasoning.

And if you’re building AI agent skills from scratch, writing the rules explicitly is a prerequisite — you can only test what’s been specified.

Further Reading

• PUBLISH BUNDLE STRESS TEST

• AI agent evaluation: A practical framework for testing multi-step agents

• LLM Agent Evaluation Metrics in 2026

• The AI Evaluation Gap: Why AI Breaks in Reality Even When It Works in the Lab

• Building AI Agents Part 3B: Testing and Evaluation Strategies for Production AI Agents

AI Disclosure

This document is drafted by an AI skill and is provided for informational and governance support purposes only. It does not constitute legal advice or a formal compliance determination. Do not publish or rely on this notice as a substitute for review by qualified legal counsel or a licensed compliance professional with jurisdiction-specific expertise.