From Chain-of-Thought to Tool Use: Prerequisites and Technical Limits of Agent Planning
Agent planning rests on three primitives — chain-of-thought, tool use, and the ReAct loop. Learn the prerequisites and where each named pattern's ceiling lives.
Agent Planning and Reasoning
Agent planning and reasoning is how AI agents break a goal into smaller steps, decide which tool or action to use next, and adjust when something fails.
It covers patterns like ReAct, plan-and-execute, and reflexion, which let agents think before they act, reflect on results, and revise their approach instead of running blindly through a fixed script.
What this topic covers
- Foundations — Planning and reasoning are what separate a real agent from a glorified prompt loop.
- Implementation — Picking a planning pattern is a trade-off between speed, cost, and reliability.
- What's changing — Reasoning quality is where the frontier labs are now competing hardest, and benchmarks shift every few months.
- Risks & limits — An agent that plans its own actions is an agent that can plan the wrong actions.
This topic is curated by our AI council — see how it works.
1
Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Agent Planning and Reasoning: ReAct, Plan-and-Execute, Reflexion
Agent planning is not human cognition — it is token generation conditioned on observations. How ReAct, Plan-and-Execute, and Reflexion actually work.
2
Build with Agent Planning and Reasoning
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Build Planning Agents with LangGraph, CrewAI, and AutoGen in 2026
Planning agents fail when frameworks come before patterns. Match ReAct, Plan-and-Execute, Reflexion, or ReWOO to your task, then build on LangGraph or CrewAI.
3
What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated May 2026
Claude Opus 4.7 vs GPT-5.3 Codex: 2026 Agent Race on GAIA, SWE-bench
Opus 4.7, GPT-5.3 Codex, and Sonnet 4.5 are trading agent benchmark crowns on GAIA and SWE-bench. The pattern reveals where to bet your stack in 2026.
4
Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Autonomous but Unaccountable: Ethics of Agents That Plan and Act
Autonomous AI agents plan, call tools, and act before humans can review the result. The accountability chain stays thin. The ethical stakes only grow.