Agentic Planning and Decision Module

An Agentic Planning and Decision Module is an intelligent goal-oriented agent planning component that determines action sequences to achieve specified objectives.

• AKA: Agent Planning Module, Reasoning Engine.
• Context:
  • It can typically generate High-Level Plans through goal decomposition into sub-tasks using chain-of-thought reasoning.
  • It can typically evaluate Alternative Actions based on utility metrics and constraint satisfaction.
  • It can typically update Plan Structures in response to new information and environmental changes.
  • ...
  • It can often leverage LLM Capabilitys through structured prompts for step-by-step reasoning.
  • It can often interact with Memory Modules and Action Modules for comprehensive planning.
  • ...
  • It can range from being a Simple Agentic Planning Module to being a Sophisticated Agentic Planning Module, depending on its reasoning complexity.
  • It can range from being a Reactive Planning Module to being a Proactive Planning Module, depending on its planning horizon.
  • ...
  • It can implement Tree-of-Thought Reasonings for complex decisions.
  • It can utilize Monte Carlo Plannings for probabilistic outcomes.
  • It can employ Hierarchical Plannings for multi-level goals.
  • ...
• Example(s):
  • Framework-Integrated Agentic Planning Modules, such as:
    • AutoGen Planning Module using asynchronous messaging for action coordination.
    • MetaGPT Planning Module generating structured plans for software development tasks.
  • Reflection-Based Agentic Planning Modules, such as:
    • Self-Critique Planning Module reviewing initial plans before execution.
    • Iterative Refinement Module improving plan quality through multiple passes.
  • Hybrid Agentic Planning Modules, such as:
    • Symbolic-Neural Planner combining classical planning algorithms with LLM reasoning.
    • Constraint-Based Planner integrating logical constraints with neural generation.
  • ...
• Counter-Example(s):
  • Deterministic Workflows, which follow fixed sequences without adaptive planning.
  • Simple Scripts, which execute predefined actions without reasoning.
  • Reactive Systems, which respond to stimulus without goal-oriented planning.
• See: LLM-based Agent Planning Module, Reasoning LLM-based AI Model, Chain-of-Thought Dataset, Goal-Oriented AI System.

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