How to Master Agent-Driven Development

I just read an interesting article How to Write a Good Spec for AI Agents article on using AI to write software. That article is pretty long, so I decided that a compact overview would be useful. I started to pick what I think was most important and decided to turn my notes/picks to this blog post The Art of the AI Spec: How to Master Agent-Driven Development (AI helped of course in this).

We’ve all been there: you hand an AI agent a massive, detailed document—something — only for the model to hallucinate, ignore half your instructions, or collapse under the weight of its own context.

Simply put, more context doesn’t always mean better results. To get the most out of tools like Claude Code or Gemini, you need a framework that respects the “attention budget” of the model.

Here is how to write smart, executable specs that keep your agents on track.

1. Start Small and Co-Create
Don’t overengineer on day one. Start with a high-level vision (your “Product Brief”) and ask the AI to draft the detailed technical specification (SPEC.md).
– The Workflow: Use “Plan Mode” (read-only) to iterate on the document first.
– The Benefit: This turns the spec into a shared “source of truth” that both you and the AI helped build, ensuring the model actually “understands” the mission before it writes a single line of code.

2. Structure Like a Professional PRD (Product Requirement Document)
AI agents are literal-minded. They thrive on structure. According to research into thousands of successful agent configurations, your spec should focus on these six core areas:
- Command: Exact executable strings (e.g., npm test, pytest -v).
- Testing: Frameworks, locations, and coverage expectations.
- Structure: Explicit file paths (e.g., src/ for code, tests/ for QA).
- Style: Provide one code snippet rather than three paragraphs of description.
- Workflow: Branch naming and commit message formats.
- Boundaries: Hard “No-Go” zones (e.g., “Never touch .env files”).

3. Modularize to Avoid the “Curse of Instructions”
Research shows that as you pile on rules, an LLM’s ability to follow any of them drops. Divide and conquer is the only way to scale.
– Break it down: Feed the agent only the relevant section of the spec for the task at hand (e.g., just the “Database Schema” section when building the API).
– Use an Index: For large projects, have the AI create a “Spec Summary” or Table of Contents. This acts as a bird’s-eye view that stays in the prompt while the heavy details stay in the file.

4. Implement Three-Tier Boundaries
Don’t just give the AI a list of “Don’ts.” Use a tiered approach to balance autonomy with safety:
– Always Do: Actions the agent takes without asking (e.g., “Always run linting before committing”).
– Ask First: High-impact changes (e.g., “Ask before adding a new library”).
– Never Do: Hard stops (e.g., “Never commit secrets or API keys”).

5. Treat the Spec as a Living Artifact
A spec isn’t “write once, run forever.” It’s an iterative loop. If the agent discovers a better data model or hits a technical wall, update the SPEC.md first, then resync the agent.
– Self-Verification: Instruct the agent to compare its output against the spec checklist before it considers a task “done.”
– The Human Filter: Remember the “House of Cards” rule—AI code can look perfect but collapse under edge cases. Never commit code you couldn’t explain to someone else.

The Bottom Line

Effective “Agent Experience” (AX) is about providing a clear “what” and “why” while setting firm guardrails on the “how.” By treating your spec as an executable artifact rather than just a notes file, you move from “vibe coding” to genuine AI-assisted engineering.

For more information read full article to get into the details:
How to Write a Good Spec for AI Agents
https://www.oreilly.com/radar/how-to-write-a-good-spec-for-ai-agents/