The intelligence of an agent comes from its ability to plan multi-step tasks, reason about complex situations, and make good decisions under uncertainty.

Planning Strategies

Upfront planning: Generate a complete plan before executing any steps.

• Advantages: Coherent strategy, predictable behavior
• Disadvantages: Plans may not survive contact with reality
• Best for: Well-understood tasks with predictable steps

Incremental planning: Plan one step ahead, execute, then plan the next step.

• Advantages: Adapts to unexpected results
• Disadvantages: May lose sight of the overall goal
• Best for: Exploratory tasks with uncertain outcomes

Hybrid planning: Create a high-level plan, then plan details for each step as you reach it.

• Advantages: Strategic coherence with tactical flexibility
• Disadvantages: More complex to implement
• Best for: Most real-world agent tasks

Implementation with LangGraph

from langgraph.graph import StateGraph
from typing import TypedDict, Annotated

class PlanExecuteState(TypedDict):
    input: str
    plan: list[str]
    current_step: int
    results: list[str]
    final_response: str

def planner(state: PlanExecuteState):
    """Generate a multi-step plan."""
    plan = llm.invoke(f"""
    Create a step-by-step plan to accomplish: {state['input']}
    Return as a numbered list. Each step should be a single, actionable task.
    """)
    return {"plan": parse_steps(plan)}

def executor(state: PlanExecuteState):
    """Execute the current step."""
    step = state["plan"][state["current_step"]]
    result = agent_with_tools.invoke(step)
    return {
        "results": state["results"] + [result],
        "current_step": state["current_step"] + 1,
    }

def replanner(state: PlanExecuteState):
    """Review progress and adjust plan if needed."""
    assessment = llm.invoke(f"""
    Original plan: {state['plan']}
    Completed steps and results: {list(zip(state['plan'][:state['current_step']], state['results']))}
    
    Should we continue with the remaining plan, modify it, or are we done?
    """)
    return update_plan(state, assessment)

Reasoning Techniques

Chain-of-thought: Force the agent to think step by step before acting. Dramatically improves complex reasoning.

Self-reflection: After completing a subtask, ask the agent to evaluate its own output:

• "Is this result correct and complete?"
• "Did I miss anything?"
• "Should I approach this differently?"

Decomposition: Break complex problems into simpler sub-problems:

• "What are the independent components of this task?"
• Solve each component separately
• Combine results

Handling Uncertainty

Agents must handle situations where they're not sure what to do:

1. Confidence scoring: Have the agent rate its confidence (1-10) and escalate when low
2. Fallback strategies: Define what to do when the primary approach fails
3. Ask for clarification: When the task is ambiguous, ask the user rather than guessing
4. Bounded exploration: Set limits on how many approaches to try before giving up