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!
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.
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.
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.
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.
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.
- 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.
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.
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