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Planning & Reasoning: The Agent's Inner Monologue

If tools are the hands of an agent, Planning and Reasoning are its frontal cortex. Without these, an agent would simply react to inputs. With them, an agent can look at a high-level goal—like "Plan a 3-day trip to Tokyo"—and strategically decompose it into actionable sub-tasks.

1. Task Decomposition​

The first step in any agentic workflow is breaking a large goal into smaller, manageable steps. This is known as Task Decomposition.

  • Linear Planning: The agent identifies a sequence (Step 1 → Step 2 → Step 3).
  • Hierarchical Planning: The agent creates a "Master Plan" with high-level objectives, which are then broken down into "Sub-plans" by specialized sub-routines.

2. Key Reasoning Frameworks​

To move through a plan successfully, agents use specific cognitive frameworks to structure their "thoughts."

A. Chain of Thought (CoT)​

CoT encourages the LLM to generate intermediate reasoning steps. By "thinking aloud," the model is less likely to jump to a premature (and often incorrect) conclusion.

  • Prompting Technique: Adding "Let's think step by step" to the instructions.

B. ReAct (Reason + Act)​

ReAct combines reasoning and acting in an interleaved manner. The agent writes a Thought, performs an Action, observes the Result, and then writes a new Thought based on that result.

C. Tree of Thoughts (ToT)​

For complex problems requiring trial and error, ToT allows the agent to explore multiple reasoning branches simultaneously. If one branch reaches a dead end, the agent "backtracks" and tries a different path.

3. Self-Reflection and Error Correction​

An intelligent agent doesn't just execute; it evaluates. Self-Reflection is the process where an agent looks at its own output to find flaws.

  1. Self-Criticism: "Does this plan actually solve the user's request?"
  2. Environment Feedback: If a tool returns an error, the agent reasons: "The API failed because the date format was wrong. I will try again with YYYY-MM-DD."
  3. Human-in-the-Loop: Pausing to ask the user for clarification when the reasoning becomes too uncertain.

4. The Planning Workflow​

The following diagram illustrates how the Planning module sits between the User Input and the Execution Layer.

5. Challenges in Agent Reasoning​

  • Hallucination in Planning: The agent might plan to use a tool that doesn't exist or assume a fact that is false.
  • Infinite Loops: The agent tries the same failing action repeatedly without changing its strategy.
  • Context Window Fatigue: As the plan grows longer, the agent may forget the initial constraints of the goal.

6. Implementation Sketch (Plan-and-Execute)​

In high-level frameworks like LangGraph, the "Plan-and-Execute" pattern is often implemented by separating the Planner from the Executor.

# 1. Planner Agent creates the list of steps
plan = planner.generate("Research the impact of AI on SEO in 2025")

# 2. Executor Agent iterates through steps
for task in plan.steps:
    result = executor.run(task)
    # 3. Re-planner evaluates if the plan needs to change
    plan = replanner.update(plan, task, result)
    if plan.is_finished:
        break

References​

Planning gives the agent a roadmap, but memory allows it to remember where it has already been. How do agents store long-term experiences?