14 Principles of Building AI Agents (Learned the Hard Way)

What I learned by building 50+ AI agents and copying the Multi-Agent Research System by Anthropic. Best practices and mistakes to avoid.

Updated: 7/25/2025 (bonus materials)

Hey, welcome to the archived edition of The Product Compass newsletter.

Every week, I share actionable tips, resources, and insights for PMs.

Here’s what you might have missed recently:

1. The Ultimate AI PM Learning Roadmap

2. The Ultimate Guide to AI Agents for PMs

3. Introduction to AI Product Management

4. Base44: A Brutally Simple Alternative to Lovable

5. How to Figma → Jira epics and stories in 10 min.

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In the recent months, I’ve built 50+ AI agents, experimented with 7+ agentic frameworks, and copied the Multi-Agent Research System by Anthropic.

Here's what I learned (best practices and mistakes to avoid):

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1. Don’t Use Agents If You Don't Have To

Nobody cares if it's an AI agent or a simple script, as long as it works. A good old if/else is faster, cheaper, and more reliable. And it's often all you need.

Save the agents for when you really need them. They might easily become a liability.

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2. Small, Specialized, and Decoupled

Think "team of specialists," not "one agent to rule them all." A planner plans. A summarizer summarizes. A verifier checks. Decoupled agents are cheaper to run, easier to test and fix, and way more predictable.

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3. Enforce Structured Output

I've learned that text is a mess to deal with. JSON is easier to debug, cheaper to parse, and acts like a contract between agents.

Bonus: you can validate it automatically and stop errors before they spread.

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4. Explain the Why, Not Just the What

I've discovered that anthropomorphizing AI works in many contexts. Here, lead with context not control.

When delegating a task, don't just define the objective. Explain why it matters and provide the context in which you need it.

This helps AI agents make better decisions with shorter prompts.

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5. Orchestration > Autonomy

Autonomy sounds great, but what you need more in real life is predictability. Move all known logic (if/then, loops, retries, known procedures) out of agent prompts and into the orchestration layer.

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6. Prompt Engineering > Fine Tuning

Before you jump to fine-tuning, ask: Why is the model failing?

• If it’s missing facts → try RAG.

• If it’s wrong formatting or doesn't follow your brand style → maybe fine-tune. But 80% of the time, it’s just a prompt problem.

Source: AI Product Management Certification by Miqdad Jaffer (OpenAI)

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