AI Methodologies: Introduction

This is a series on practical methodologies for working with AI agents. These aren’t theoretical frameworks… they’re habits and patterns that help engineers and operators get reliable, verifiable results from agent-assisted workflows.

The core idea: AI agents produce first drafts, not finished work. The value comes from treating them as collaborators that need clear inputs, constraints, and verification… just like any other tool in your stack.

The Series

1. Start with Intent - Work at the intent layer: specs, constraints, and acceptance criteria before work begins.

2. Agents Know Nothing - Make context a first-class input. Agents work best when they start with what a human would need.

3. Create a Library - Build verified templates for recurring tasks to reduce rework and make outcomes predictable.

4. Agents do the Work - Run agents asynchronously with review gates. Define inputs, constrain execution, require evidence.

5. Verification is Important - Build verification into the loop: acceptance criteria, automated checks, and audit trails.

6. Do One Thing - Keep agents narrow and purpose-built. Focused prompts produce cleaner, more verifiable output.

7. Build your Team - Invest in the agentic layer: prompts, tools, guardrails, and verification as infrastructure.

Why This Matters

Agent tooling is improving fast. 2026 is going to be a good year, and will move fast, having good habits will serve you well. The engineers who will get the most leverage are those who build repeatable processes around it… clear inputs, scoped permissions, verification steps, and iteration based on outcomes.

These posts are meant to be practical. Each one includes habits you can try today.