Building With AI Agents: What I've Learned So Far

The conversation around AI agents has shifted from "will they work?" to "how do you actually build with them?" After spending the last few months shipping real products with AI agents as core infrastructure, I want to share what's actually working.

The Mental Model Shift

The biggest mistake I see builders make is treating AI agents like smarter autocomplete. They're not. They're more like junior developers who are incredibly fast, never tired, and have read every Stack Overflow answer ever written — but who still need clear direction and guardrails.

The shift is from writing code to writing intent. You're not coding a solution anymore. You're describing what you want, setting boundaries, and reviewing output.

Patterns That Actually Work

1. Small, Focused Agents Beat General-Purpose Ones

Every time I've tried to build a "do everything" agent, it's fallen apart at scale. What works is decomposing the problem into small, focused agents that each own one responsibility.

Think of it like microservices for AI — each agent has a clear input, a clear output, and a well-defined scope.

2. Context Is Everything

The single biggest factor in agent quality isn't the model — it's the context you provide. I've seen mediocre models outperform frontier models when given better context.

This means investing heavily in:

System prompts that are specific and well-structured
Reference documents that give the agent domain knowledge
Examples that show the agent what good output looks like

3. Human-in-the-Loop is Not a Weakness

There's this pressure to make everything "fully autonomous." Resist it. The best agent workflows I've built have strategic human checkpoints — places where a human reviews, approves, or redirects.

Full autonomy is a goal, not a starting point.

What Doesn't Work

Chaining too many agents together without intermediate validation. Each handoff is a potential failure point. Keep chains short and validate between steps.

Letting agents make irreversible decisions. Always build in undo capabilities or approval gates before destructive actions.

Optimizing for token efficiency too early. Get it working first. Optimize costs after you've validated the workflow actually produces value.

What's Next

I'm currently building a framework that codifies these patterns into something reusable. It's called the Conductor Framework, and the core idea is simple: you're the conductor, the AI agents are the orchestra. Your job is to direct, not to play every instrument.