Agentic Patterns

Design patterns and architectures for building effective AI agent systems.

Common Agentic Patterns

Several architectural patterns have emerged as effective approaches for building AI agent systems. Each pattern offers different trade-offs between reliability, transparency, and capability.

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ReAct (Reason + Act)

Interleave reasoning traces with actions for better transparency and control.

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Plan-and-Execute

Create a high-level plan first, then execute steps sequentially.

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Multi-Agent Systems

Multiple specialized agents collaborate to solve complex tasks.

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Reflection

Agents review their own outputs and iteratively improve them.

ReAct Pattern Deep Dive

ReAct (Reasoning + Acting) interleaves chain-of-thought reasoning with action execution. The model explicitly states its thinking before each action.

How it works

1.Thought: The model reasons about what to do next
2.Action: The model calls a tool or takes an action
3.Observation: The model sees the result
4.Repeat until task is complete

Pros

  • • Highly transparent—you can see exactly why the agent did what it did
  • • Easier to debug and understand failures
  • • Natural error recovery through explicit reasoning

Cons

  • • More tokens used (reasoning takes space)
  • • Can be slower due to explicit reasoning steps
  • • May overthink simple tasks

Plan-and-Execute Deep Dive

This pattern separates planning from execution. A planner creates a high-level plan, then an executor carries out each step.

How it works

1.Planner analyzes the task and creates a step-by-step plan
2.Executor carries out each step in sequence
3.Replanning happens if execution fails or new info emerges

Pros

  • • Better for complex, multi-step tasks
  • • Can use different models for planning vs execution
  • • Plans can be reviewed before execution

Cons

  • • Plans may become outdated as execution proceeds
  • • Harder to handle unexpected situations
  • • Replanning adds latency and cost

Choosing the Right Pattern

Select a pattern based on your specific needs and constraints.

For simple, well-defined tasksDirect tool calling (no pattern needed)
When you need transparency and debuggabilityReAct pattern
For complex, multi-step tasksPlan-and-Execute
When output quality is criticalReflection pattern
For diverse task typesMulti-Agent with specialized agents
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Pattern Comparison

Explore different agentic architectures

R

ReAct

Reasoning + Acting

01
Observe
→
02
Think
→
03
Act
→
04
Loop

Observe: Receive input or tool result

pseudocode
while not done:
  observation = get_current_state()
  thought = llm.reason(observation)
  action = llm.decide_action(thought)
  result = execute(action)
  if is_final_answer(result):
    return result

Key Takeaways

  • 1Choose patterns based on task complexity and reliability needs
  • 2ReAct is great for transparency but may be slower
  • 3Multi-agent systems add complexity but enable specialization
  • 4Reflection patterns can significantly improve output quality