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AI Agents – The Next Big Leap in Generative AI

Welcome to Data Science Dojo’s weekly newsletter, “The Data-Driven Dispatch“.

In this newsletter, we’ll dive deep into AI agents and uncover why everyone is talking about them.

Most of us use LLMs in a zero-shot mode, meaning we request the model to complete a task in one attempt.

For instance, you might ask a model to develop a marketing strategy for a new product you plan to launch online.

Interestingly, if you ask a person to perform the same task in one go without back-spacing for once, they’d say you’re crazy. But, despite how difficult it is, LLMs can do quite well.

But here’s the catch. If LLMs are already performing impressively in a zero-shot mode, imagine the possibilities if we enhance them further.

By employing various strategies that enable LLMs to iterate, reflect, and utilize diverse resources—similar to the techniques humans use to handle complex tasks—we could significantly improve their performance, making them even more efficient and effective.

This is exactly why AI agents are here for.

The Scope of LLM Agents | AI Agents
The Scope of LLM Agents

Let’s dig into what are AI agents, different design patterns to create agentic workflows for LLM applications, and the mass benefits they can bring in.

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What are AI Agents?

AI agents leverage the immense language understanding and generation capabilities of LLMs to interpret complex tasks and generate meaningful outputs.

These agents can break down intricate requests into manageable steps, iterate on solutions, gather insights from various sources, and adapt their strategies in real time.

Read more: LLM agents: Empowering language models from text to tasks

How do Agentic Workflows Impact the Performance of LLMs?

Incorporating agentic workflows allows LLMs to have a framework to deal with a complex query and hence helps yield better results.

The effectiveness of agentic workflows is evident from the fact that when GPT-3.5 employs an agentic workflow to address a query, it demonstrates superior performance compared to GPT-4 operating in a zero-shot mode.

GPT 3.5 with Agentic Workflow Vs GPT 4 Zero Shot Mode
Source: DeepLearning.AI

Read: GPT 3.5 Vs. GPT 4: A Comparative Analysis

Design Patterns for AI Agentic Workflows

Now the question is what kind of AI agents will crack the code?

Here’s a framework for categorizing design patterns for building agents.

Design Pattern for AI Agentic Workflow in LLM Applications
Design Pattern for AI Agentic Workflow in LLM Applications

Dive Deeper into Agents: Design Patterns for AI Agents: Key Framework, Benefits, and Challenges

Reflection and tool use in AI agents are already being widely incorporated. However, multi-agent collaboration is an emerging design, yet very promising.

Microsoft has introduced AutoGen, a new framework designed to streamline the development process for complex LLM applications. This framework enables users to build applications that incorporate multiple AI agents. These agents, powered by advanced LLMs like GPT-4, can communicate with each other to tackle challenging and intricate tasks more effectively.

The Benefits of Incorporating Agentic Workflows in LLMs

AI is transcending from good to great, and AI agents will be the bridge.

Here’s how agentic workflows will impact AI:

  1. Scalability: These workflows handle large amounts of data and complex tasks efficiently, even when the workload increases. This makes them great for big projects or businesses.

  2. Enhanced Functionality: Agentic workflows enable LLMs to access and interact with external systems such as databases, APIs, and web services, expanding their capabilities beyond text processing alone.

Recommended Read: Learn How to Develop LLM Agents Using LangChain

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Build AI Applications with LangChain Agents

Want to see how agentic workflows improve the performance of LLMs? We got you!

Here’s a great session where the speaker:

  • Discusses various aspects of LangChain agents, such as their creation, utilization, and integration with different tools and technologies

  • Presents a live demo of how to build an AI tool that has the power to answer questions and provide insights from given CSV data.

Build LLM Applications with LangChain agents
Build LLM Applications with LangChain agents

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Well, AI’s got you covered!

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Shift your Career Trajectory Toward AI

Generative AI is poised to add $4.4 trillion to the global economy. Everyone in the world wants to enjoy the productivity gains from artificial intelligence.

It’s the best time to shift your career trajectory and aim for a profession in AI.

Here are the highest-paying AI Jobs in 2024

10 highest paying AI jobs in 2024Build next-gen AI! Enroll in Data Science Dojo’s LLM Bootcamp.

AI_News_Wrap

Here’s a wrap of the latest news in the AI world:

  1. Sam Altman says helpful agents are poised to become AI’s killer function. Read more
  2. Microsoft bans US police from using AI facial recognition. Read more
  3. Anthropic launches new iPhone app and premium plan for businesses. Read more
  4. The first music video generated with OpenAI’s unreleased Sora model is here. Read more
  5. Major US newspapers sue Microsoft, and OpenAI over alleged copyright violations. Read more
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