Planning and Decomposition¶
Planning is the "System 2" component that allows agents to solve multi-stage problems without "wandering." Production agents have moved from simple "Chain-of-Thought" to Recursive Decomposition and Tree Search, with reasoning-native models (Claude Opus 4.7, GPT-5.5 extended thinking, DeepSeek-R2) doing the heavy planning internally.
Table of Contents¶
- The Planning Spectrum
- Static vs. Dynamic Planning
- Chain-of-Thought (CoT) and o1 Reasoning
- Recursive Task Decomposition
- Tree Search (MCTS) for Agent Paths
- Interview Questions
- References
The Planning Spectrum¶
| Method | Strategy | Complexity | Best For |
|---|---|---|---|
| Linear | One step at a time | Low | Simple tools |
| Branching | If-Then-Else logic | Medium | Conditional flows |
| Hierarchical | Master-Plan -> Sub-Plans | High | Software engineering |
| Search-Based | Try multiple paths internally | Max | Scientific Research |
Static vs. Dynamic Planning¶
Static (Plan-and-Solve)¶
The agent writes a 10-step plan and follows it strictly. - Pros: High performance, easy to parallelize. - Cons: Brittle. If step 2 fails, steps 3-10 are useless.
Dynamic (Adaptive)¶
The agent writes a plan, but Re-evaluates after every tool call. - Best practice: Use Checkpointed Planning. The agent is forced to "Commit" its progress to a state store after every major sub-goal to allow for recovery and "Backtracking" if the plan fails.
CoT and o1 Reasoning¶
The model's internal "Thinking" window (Inference scaling) acts as a Hidden Planner. - Instead of using a separate "Planner LLM," we use a reasoning model (Claude Opus 4.7, GPT-5.5 extended thinking, DeepSeek-R2) to generate a "Mental Draft." - This draft is translated into a Task DAG (Directed Acyclic Graph) that the orchestrator executes.
Recursive Task Decomposition¶
For massive tasks (e.g., "Build a full-stack app"), we use Sub-Agent Spawning. 1. Master Agent: Decomposes "Project" into "Frontend," "Backend," and "DB." 2. Sub-Agents: Each receives a "Sub-Goal" and performs its own decomposition. 3. Consolidation: The Master Agent merges the results.
Critical Nuance: Each sub-agent is given a Minimal Context (only what it needs) to prevent token bloat and hallucination.
Tree Search (MCTS)¶
For high-stakes decisions, we use Monte Carlo Tree Search (MCTS) within the agent loop. - The agent "Simulates" 10 possible tool calls. - A Reward Model (or a separate LLM prompt) scores each simulation. - The agent follows the path with the highest reward.
Interview Questions¶
Q: How do you prevent an agent from "Infinite Recursion" during task decomposition?¶
Strong answer: We implement Decomposition Depth Limits (usually 3 levels) and Granularity Checks. Before spawning a sub-agent, we ask the Supervisor model: "Is this task small enough to be solved by a single tool call?" If yes, we execute. If no, we decompose. We also use a Global Controller that tracks the total "Agent Count" to prevent a recursive bomb (fork bomb) that could drain the API budget.
Q: Why is "Plan Revision" often more expensive than "Plan Generation"?¶
Strong answer: Plan generation is a "Fresh Start." Plan revision requires Context Re-evaluation—the model must understand what was already done, why the previous step failed, and how to fix it without undoing previous successes. This requires a much higher "Reasoning Density." In production, we often use a larger model (e.g., Sonnet 3.7 or o1) for the Revision step, while using a smaller model for the initial plan generation.
References¶
- Silver et al. "Mastering the game of Go with deep neural networks and tree search" (Applied to LLMs, 2024/2025)
- Wang et al. "Self-Consistency Improves Chain of Thought Reasoning" (2022/2025 update)
- LangGraph. "Multi-Agent Planning Patterns" (2025)