Topic
Agent Planning & Task Decomposition
12 posts on agent planning & task decomposition — part of ai agents & automation on the n4n AI blog.
Why AI agents fail at long-horizon planning
Analyzes why AI agents long-horizon planning failures occur, covering credit assignment, context rot, and tooling limits with concrete engineering fixes.
ReAct vs Plan-and-Execute: two AI agent planning patterns
A practitioner's comparison of ReAct vs Plan-and-Execute agent planning patterns across cost, latency, ergonomics, and limits, with a use-case verdict.
How many steps can AI agents reliably plan ahead?
Analyzes how many steps AI agents can reliably plan ahead, why AI agent planning horizon reliability collapses, and engineering patterns to extend it.
Hierarchical planning for AI agents, explained
Hierarchical planning AI agents decompose complex goals into layered sub-tasks, enabling scalable, reliable autonomy. Learn the architecture and tradeoffs.
Building an AI agent that decomposes tasks automatically
A hands-on tutorial for building an AI agent automatic task decomposition system with LLMs, step by step, with runnable code and expected outputs.
Building a task-decomposition layer for your AI agent
Hands-on tutorial to build task decomposition layer AI agent in Python: LLM-based planning, subtask orchestration, and execution with real code.
AI agent planning: tree search vs chain-of-thought
Engineering comparison of tree search vs chain-of-thought agent planning across cost, latency, ergonomics, and limits with a use-case verdict.
What is task decomposition in AI agents?
Task decomposition AI agents break complex goals into executable steps. Learn how planning, orchestration, and verification work in practice.
How Claude and GPT-5 plan multi-step tasks differently
A practitioner's head-to-head comparison of Claude vs GPT-5 agent planning across capabilities, cost, latency, ergonomics, and limits.
How AI agents plan multi-step tasks
Practical guide to AI agent multi-step task planning: design task graphs, separate planner from executor, manage failures, and dodge common engineering pitfalls.
AI agent planning with tool use: a practical guide
Engineer-focused AI agent planning tool use guide: build task boundaries, tool registries, execution loops, and robust fallback for LLM agents.
AI agent planning failures and how to debug them
Practical steps to debug AI agent planning failures: trace prompts, isolate decomposition errors, and verify tool calls with reproducible test harnesses.
More topics in ai agents & automation
- Function Calling Fundamentals27
- Autonomous Coding Agents: Claude Code, Devin, Cursor15
- Model Context Protocol (MCP) Deep Dives15
- Multi-Agent Orchestration Patterns15
- Agentic RAG14
- AI Agent Cost & Latency Optimization14
- AI Agent Framework Comparison14
- AI Agent Security & Prompt Injection Defense14
- AI Agent Tool Use Design Patterns14
- AI Agents in Customer Support14
- LangGraph for Agent Workflows14
- LLM Workflow Automation: n8n, Zapier, Make14