* AI

Agentic AI Patterns That Actually Work

Most "agents" are a for-loop with anxiety. Here are the patterns we use to ship autonomous workflows that survive contact with reality.

April 10, 2026 1 min readBy Cerebrix Studio

"Agent" is a loaded word

The hype version: a single LLM call in a while loop, picking tools at random until it runs out of tokens or patience.

The version that ships: a small graph of typed steps, where the LLM makes narrow decisions inside well-defined seams, and a durable runtime owns the control flow.

The four patterns we reach for

  1. Router — one LLM call picks one of N specialized chains. Simple, debuggable, often enough.
  2. Plan-and-execute — the model writes a plan once, then a deterministic executor runs each step. The model only re-enters on exceptions.
  3. Reflect-and-revise — the model critiques its own draft against a rubric before returning. Great for content, code, and structured outputs.
  4. Tool-use with typed contracts — every tool has a Zod schema for inputs and outputs. The model can't "hallucinate" a malformed call.

What we never ship

  • Unbounded reasoning loops with no step limit.
  • Tools that take free-text and "figure it out."
  • Agents without traces, replays, or a kill switch.

The shape of a good agent

It looks suspiciously like a state machine with one or two LLM-shaped holes. That's the point. The boring parts should be boring; the model should only be the magic where magic is required.

KEEP READING

All notes ->
▲ READY WHEN YOU ARE

TELL US WHAT
TO SHIP.

One short message gets a real plan back, usually within 6 hours. No decks. No "let's hop on a call to scope a call."

↳ Avg response time: 6 hours

Doraemon pointing — ready to build