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5 Agentic AI design patterns explained visually
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!
On a side note, we started a beginner-friendly crash course on RAGs recently with implementations, which covers:
- RAG fundamentals
- RAG evaluation
- RAG optimization
- Multimodal RAG
- Graph RAG
- Multivector retrieval using ColBERT
- RAG over complex real word docs ft. ColPali
1) Reflection pattern
The AI reviews its 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
In this setup:
- 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 tasks to other agents if needed.
We'll soon dive deep into each of these patterns, showcasing real-world use cases and code implementations.
In the meantime, make sure you are fully equipped with everything we have covered so far like:
- RAG fundamentals
- RAG evaluation
- RAG optimization
- Multimodal RAG
- Graph RAG
- Multivector retrieval using ColBERT
- RAG over complex real word docs ft. ColPali
Thanks for reading!
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