The problem

Most AI agents fail in production because they are tested against happy-path demos rather than rigorous datasets. Teams lack a systematic way to measure reliability, leading to unpredictable behavior, tool misuse, and unmonitored costs.

What it does

• Generates complete reliability layers including behavior contracts, tool boundaries, and evaluation datasets.
• Performs adversarial testing and failure taxonomy audits to identify high-risk edge cases.
• Designs deterministic checks and rubric-based grading systems to replace subjective quality opinions.
• Builds tool safety matrices with clear read/write permissions and human-in-the-loop escalation rules.
• Produces production readiness checklists covering latency targets, cost budgets, and regression suites.

Why this beats prompting it yourself

DIY prompting usually results in longer, more confusing system instructions without measurable outcomes. This skill treats agent development as an engineering discipline by creating evidence-based benchmarks and safety gates that actually survive production traffic.

Use cases

• Auditing a customer support agent before deploying it to a live chat interface.
• Setting up tool-use boundaries for a financial agent capable of executing transactions.
• Creating a golden dataset to evaluate if a prompt change improved or damaged performance.
• Designing a rollback plan and monitoring suite for a multi-agent RAG system.

Known limitations

Requires a clear description of the agent mission, tools, and target workflow to generate meaningful evaluations. Does not provide the execution environment for tests.