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5 Agentic AI Design Patterns

Reflection, tool use, ReAct, planning, and multi-agent collaboration—framework-ready patterns with CrewAI callouts where relevant (PDF 200–206; shared boundaries on 200 and 206).

5 Agentic AI Design Patterns

5 Agentic AI Design Patterns Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration! The following visual depicts the 5 most popular design patterns employed in building AI agents.

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

1) Reflection pattern

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

The AI reviews its own work to spot mistakes and iterate until it produces the final response.

2) Tool use pattern

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

Tools allow LLMs to gather more information by:

● Querying a vector database

● Executing Python scripts

● Invoking APIs, etc.

This is helpful since the LLM is not solely reliant on its internal knowledge.

3) ReAct (Reason and Act) pattern

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

ReAct combines the above two patterns:

● The Agent reflects on the generated outputs.

● It interacts with the world using tools.

A ReAct agent operates in a loop of Thought → Action → Observation, repeating until it reaches a solution or a final answer. This is analogous to how humans solve problems:

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

Note: Frameworks like CrewAI primarily use this by default. To understand this, consider the output of a multi-agent system below:

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

As shown above, the Agent is going through a series of thought activities before producing a response. This is the ReAct pattern in action! More specifically, under the hood, many such frameworks use the ReAct (Reasoning and Acting) pattern to let LLM think through problems and use tools to act on the world . For example, an agent in CrewAI typically alternates between reasoning about a task and acting (using a tool) to gather information or execute steps, following the ReAct paradigm . This enhances an LLM agent’s ability to handle complex tasks and decisions by combining chain-of-thought reasoning with external tool use .

4) Planning pattern

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

Instead of solving a task in one go, the AI creates a roadmap by:

● Subdividing tasks

● Outlining objectives

This strategic thinking solves tasks more effectively. Note: In CrewAI, specify `planning=True` to use Planning.

5) Multi-Agent pattern

Illustration from the AI Agents chapter of the course deck.
Illustration from the AI Agents chapter of the course deck.

● There are several agents, each with a specific role and task.

● Each agent can also access tools.

All agents work together to deliver the final outcome, while delegating tasks to other agents if needed.

Key takeaways

  • Patterns compose: reasoning loops, tools, planning, and specialization address different failure modes.
  • Many orchestration stacks default to ReAct-style think → act → observe loops.