LLM Agent Planning

Introduction

Planning is a core capability that elevates LLMs from simple predictive text generators to sophisticated agents that can solve complex tasks. This document explores how LLM agents approach planning, the various planning strategies, their implementations, and their limitations.

What is Agent Planning?

Agent planning refers to the ability of an LLM-based system to:

  1. Break down complex tasks into manageable subtasks

  2. Determine the optimal sequence of actions

  3. Adapt plans as new information emerges

  4. Reason about dependencies between subtasks

  5. Handle failures and unexpected outcomes

Key Planning Approaches

Task Decomposition

Description: Breaking down complex goals into simpler, more manageable subtasks

Implementation Patterns:

  • Few-shot examples of task breakdowns

  • Chain-of-thought prompting to encourage step-by-step planning

  • Tree-of-thought approaches for exploring multiple decomposition paths

Example:

Goal: "Create a data visualization dashboard for sales data"

Decomposition:
1. Understand the data schema and available fields
2. Determine key metrics and KPIs to visualize
3. Select appropriate visualization types for each metric
4. Design the layout of the dashboard
5. Implement each visualization component
6. Connect visualizations to the data source
7. Add interactive filtering and drill-down capabilities
8. Test with sample data and refine

Hierarchical Planning

Description: Using multiple levels of abstraction, from high-level strategies to low-level actions

Implementation Approaches:

  • Recursive task refinement: Start with a high-level plan, then decompose each step

  • Manager/Worker patterns: One agent creates plans, others execute them

  • MRKL/HuggingGPT approach: Planning which specialized models to use for subtasks

Example Hierarchy:

  1. Strategic Level: "Build a customer churn prediction model"

  2. Tactical Level: "Perform data cleaning, feature engineering, model selection, training, evaluation"

  3. Operational Level: Specific code and operations for each tactical element

ReAct: Reasoning + Acting

Description: Interleaving planning, acting, and observation steps

Key Components:

  • Reasoning: Explicit thinking about what to do next

  • Acting: Taking a concrete action (using a tool, API call)

  • Observing: Processing the results of actions

  • Revising: Updating plans based on observations

Typical Prompt Structure:

Thought: I need to find information about X
Action: Search for "X definition and examples"
Observation: [search results]
Thought: Now I understand X, I should check for Y
Action: ...

Reflection-Based Planning

Description: Incorporating self-critique and reflection into the planning process

Techniques:

  • Plan verification: Critiquing initial plans before execution

  • Execution monitoring: Reflecting on success/failure during execution

  • Post-execution analysis: Learning from completed tasks

Example Process:

  1. Generate initial plan

  2. Reflect: "Is this plan complete? Are there edge cases?"

  3. Refine plan based on reflection

  4. Execute plan steps

  5. Reflect on outcomes and adjust remaining steps

Planning Frameworks and Implementations

LangChain Agents

Planning Capabilities:

  • Zero-shot and few-shot agent planners

  • Tool selection and execution

  • ReAct-style reasoning traces

  • Various tool combinations

Example Planning Types:

  • ZERO_SHOT_REACT_DESCRIPTION: Plans based on tool descriptions without examples

  • STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION: More structured planning with JSON output

AutoGPT Planning

Approach: Long-running autonomous agents with persistent goals and planning

Planning Components:

  • Goal breakdown

  • Memory incorporation

  • Command selection

  • Self-critique

BabyAGI / Task-Driven Autonomous Agents

Approach: Task queue management with dynamic prioritization

Planning Features:

  • Task prioritization

  • Task creation based on results of completed tasks

  • Memory integration for context

ReAct Variants

  • ReWOO (Reasoning Without Output): Keeps reasoning internal before generating responses

  • Reflexion: Incorporates episodic memory and reflection for improved reasoning

  • SayCan: Grounds planning in physical feasibility for robotics

Advanced Planning Techniques

Task-Specific Planning Strategies

Mathematical Problem Solving:

  • Break down complex problems into steps

  • Validate intermediate results

  • Apply mathematical principles methodically

Code Generation:

  • Design high-level architecture first

  • Implement critical components

  • Test and debug incrementally

Research Tasks:

  • Formulate specific questions

  • Gather relevant information

  • Synthesize findings into coherent output

Multi-Agent Planning

Description: Distributing planning and execution across specialized agents

Frameworks:

  • CrewAI: Role-based multi-agent framework

  • AutoGen: Conversational agent framework with multi-agent support

  • MetaGPT: Software development team simulation

Planning Patterns:

  • Specialist/Generalist: Different agents for different domains

  • Critic/Creator: Separate agents for generation and evaluation

  • Hierarchical Teams: Management agents delegating to worker agents

Challenges and Limitations

Planning Failures

Common Failure Modes:

  • Hallucinating steps: Planning using non-existent tools or capabilities

  • Circular reasoning: Repeatedly planning similar approaches that don't work

  • Insufficient decomposition: Not breaking tasks down enough

  • Over-decomposition: Creating too many trivial subtasks

Evaluation Challenges

  • Difficulty in measuring planning quality independently of execution

  • Plans may seem reasonable but contain subtle flaws

  • Lack of standardized benchmarks for planning capabilities

Current Research Areas

  1. Verifiable planning: Ensuring plans are executable and sufficient

  2. Uncertainty handling: Incorporating probabilistic reasoning into plans

  3. Temporal reasoning: Planning with time constraints and dependencies

  4. Resource-aware planning: Considering computational and time costs

References

  1. Yao, S., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. https://arxiv.org/abs/2210.03629

  2. Shinn, N., et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. https://arxiv.org/abs/2303.11366

  3. Yao, S., et al. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. https://arxiv.org/abs/2305.10601

  4. Zhou, Y., et al. (2023). Language Agents with Planning and Reasoning Abilities: A Survey. https://arxiv.org/abs/2310.12823

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