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Methodology for language model agents to organize and execute complex, multi-step tasks

Core Idea: Agent planning enables LLM agents to break down complex tasks into logical sequences of steps, reason about dependencies and constraints, decide which tools to use at each stage, and adapt their approach based on intermediate results and feedback.

Key Elements

Planning Processes

• Task Decomposition: Breaking complex goals into manageable subtasks
• Dependencies Analysis: Identifying which steps must precede others
• Tool Selection: Determining appropriate tools for each subtask
• Progress Monitoring: Tracking completion status of planned steps
• Plan Adaptation: Modifying plans based on new information or obstacles
• Reflection: Evaluating effectiveness and learning from experience

Core Planning Mechanisms

• Chain-of-Thought Reasoning: Breaking down problems into sequential reasoning steps
• ReAct Framework: Cycling between reasoning (planning) and acting (execution)
• Reflexion: Adding self-critique and improvement to the planning process
• Explicit Working Memory: Maintaining awareness of current state and plan progress

Planning Architectures

• Fixed Plan Execution: Predetermined sequence of tools or actions
• Dynamic Planning: Real-time decision-making about next steps
• Hierarchical Planning: High-level plans with nested sub-plans
• Collaborative Planning: Multiple agents coordinating on shared plans
• Human-in-the-Loop: Systems that incorporate human feedback into planning

Implementation Techniques

• Plan Verbalization: Explicit articulation of planned steps
• State Tracking: Maintaining representations of the environment and progress
• Goal Stack: Managing hierarchies of goals and subgoals
• Backtracking: Ability to revisit and revise earlier planning decisions
• Environmental Feedback: Using action results to inform plan adjustments

Challenges and Solutions

• Combinatorial Explosion: Managing complexity in large planning spaces
• Uncertainty Handling: Planning under incomplete information
• Resource Constraints: Managing time, token limits, and tool usage
• Plan Repair: Adapting when initial plans fail or encounter obstacles
• Tool Orchestration: Efficiently coordinating multiple tools and capabilities

Connections

• Related Concepts: ReAct Framework (planning implementation), Reflexion (improving planning), Chain-of-Thought (CoT) Prompting (reasoning foundation)
• Broader Context: LLM Agents (application area), Agentic AI (theoretical framework)
• Applications: Task Automation, Complex Problem Solving, Research Assistants
• Components: LLM Tool Use, Agent Memory Systems (enabling capabilities)

References

1. Yao, S., et al. (2023). "ReAct: Synergizing Reasoning and Acting in Language Models."
2. Shinn, N., et al. (2023). "Reflexion: Language Agents with Verbal Reinforcement Learning."
3. Park, J. S., et al. (2023). "Generative Agents: Interactive Simulacra of Human Behavior."

#AgentPlanning #LLMAgents #PlanningFrameworks #TaskDecomposition #ReasoningAndPlanning #AgentSystemDesign

Connections:

Sources:

• From: A Visual Guide to LLM Agents