5 Agentic AI Design Patterns

...explained visually

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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.

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​

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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.

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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​

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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – Part 1.

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware.

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.