ReAct, Plan-Execute & Reflexion

The Agent Orchestration Patterns Taxonomy structures production-grade autonomous LLM systems across varying task horizons, latency budgets, and reliability constraints; the 4 primary patterns include: 1) ReAct (Reasoning + Acting: iterative Thought $\to$ Action $\to$ Observation loop, ideal for dynamic exploration but prone to wandering in long-horizon plans); 2) Plan-and-Execute: decoupling macro-planning from execution where a Planner synthesizes a multi-step DAG and an Executor runs tools, invoking dynamic Re-planning upon failures; 3) Reflexion (Self-Correction): capturing failed execution traces for a Critic model to generate linguistic Self-Reflections stored in episodic memory to prevent repeating mistakes; 4) Multi-Agent Teams (Supervisor-Worker hierarchies, Router-Specialist routing, Jury Voting).

Autonomous software engineering agents (Devin-style), enterprise multi-system robotic process automation, and multi-tool workflow synthesis.

Single-turn prompting collapses on multi-step workflows; structured orchestration decomposes complex goals into robust, self-healing, and stateful execution graphs.

Contrast ReAct vs Plan-and-Execute across execution latency, token consumption, and multi-step goal tracking reliability?

Explain how Reflexion's Actor, Evaluator, and Self-Reflection components achieve gradient-free self-improvement across iterations?

How does LangGraph's StateGraph model nodes, conditional edges, and human-in-the-loop breakpoints for deterministic agent workflows?

How to resolve multi-agent infinite chatter loops and consensus polarization using deterministic Judge arbitration and token bounds?

How to build an adaptive router dynamically dispatching between low-latency ReAct and deep Plan-and-Execute workflows?