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5 most popular Agentic AI design patterns, clearly explained (with visuals):
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. Let's understand them below!
1) Reflection pattern: The AI reviews its own work to spot mistakes and iterate until it produces the final response.
2) Tool use pattern 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 ReAct combines the above two patterns: - The Agent can reflect on the generated outputs. - It can interact with the world using tools. This makes it one of the most powerful patterns used today.
4) Planning pattern Instead of solving a request in one go, the AI creates a roadmap by: - Subdividing tasks - Outlining objectives This strategic thinking can solve tasks more effectively.
5) Multi-Agent pattern - We have several agents. - Each agent is assigned a dedicated role and task. - Each agent can also access tools. All agents work together to deliver the final outcome, while delegating task to other agents if needed.
That's a wrap! If you enjoyed this tutorial: I'll soon dive deep into each of these patterns, showcasing real-world use cases and code implementations. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.