5 Loop Design Patterns Behind Every Reliable AI Agent

A good agent isn't one big prompt — it's a loop. Which loop pattern you choose decides whether your agent is fast, reliable, self-correcting, or falls apart the moment a task gets complex.
In this session, we go deep on the 5 loop patterns behind every production agent:

• ReAct — reason, act, observe, then plan the next step. The most common loop, alternating between thinking and calling a tool.
• Reflexion — adds persistent memory on top of ReAct. The agent remembers what went wrong last time and carries that forward — genuinely episodic memory for machines.
• Plan-and-Execute — separates the thinker from the doer. A planner model breaks the goal into ordered sub-tasks; a separate executor runs a tight loop on each one and reports back. Planning quality and execution speed can now scale independently.
• Evaluator-Optimizer — turns generation into a multi-model dialogue. A generator produces a draft, an evaluator grades it against specific criteria and gives feedback, and it loops until the draft passes. This is the exact architecture behind our DDS AI App Reviewer's rubric scoring.
• Orchestrator-Workers — built to prevent context saturation. A central coordinator delegates sub-tasks to independent worker agents, each running in its own isolated context window, so no single conversation drowns in the full task's detail.

The real takeaway: you rarely pick just one. Production systems compose them — commonly an orchestrator running ReAct-style workers, gated by an evaluator.