I've Built 4 AI Agent Systems. Here's What Actually Works.

Most agent tutorials are lying to you#

Every tutorial shows you a single agent calling a function and goes "look, AI agents!" That's not an agent system. That's a chatbot with a tool.

I've built 4 production agent systems over the past year. Here's what I actually learned.

The four real patterns#

Pattern 1: Single agent + tools#

One LLM with function calling. Fine for chatbots, simple automation, prototypes. This is where everyone starts and where most people stop.

When it breaks: When the task has more than 3 steps. The LLM loses context, hallucinates intermediate steps, or just does something random.

Pattern 2: Orchestrator + workers#

This is the one that actually works in production. A boss agent plans the work and delegates to specialist workers.

• Orchestrator breaks the task into subtasks
• Code worker writes and reviews code
• Research worker searches the web
• Writing worker generates content

This is how Claude Code, Cursor agent mode, and Devin work under the hood.

Pattern 3: Pipeline (DAG)#

Agents arranged in a chain. Output of one feeds into the next. Good for content pipelines, data processing, anything with clear sequential steps.

Input → Research → Analyze → Write → Review → Output

Simple. Debuggable. But inflexible — if step 3 needs to go back to step 1, you're screwed.

Pattern 4: Swarm#

Multiple agents operating independently with shared memory. No central coordinator. This is the frontier stuff — OpenClaw uses this approach.

Cool in theory. Nightmare to debug. I wouldn't use this in production unless you have a team dedicated to observability.

The mistakes everyone makes#

1. No observability. If you can't trace why an agent did what it did, you can't fix it when it breaks. And it WILL break. Use LangSmith, or at minimum, log every LLM call with inputs and outputs.

2. No rate limiting. I once left an agent running overnight. It burned through $400 in API calls doing absolutely nothing useful. Set hard limits.

3. No fallback. When the LLM returns garbage (and it will), your system needs a graceful way to say "I couldn't do this" instead of crashing.

4. Shared state without locks. Two agents writing to the same database row at the same time = corrupted data. Ask me how I know.

What a production agent system actually looks like#

That diagram has components most tutorials never mention: the observability layer, the human approval gateway, and the shared memory. Those are the difference between a demo and a product.

Just build it#

Stop reading agent frameworks docs and start building. The only way to learn this stuff is to ship something, watch it break, and fix it. That's the whole process.