Autogen Explained: A Comprehensive framework for autonomous agents

The market for autonomous agents is poised to grow at a staggering rate of over 36.5% CAGR, with a 2032 value projection of $88.1 billion in North America alone (The Business Research Company ). Why the excitement? Because this innovation promises a massive productivity boost and efficiency gains, especially at the enterprise level.

“AI agents will be everywhere. Billion-dollar companies will come from a small team that deploys AI agents.” - Ben Tossell, Founder of Ben's Bites AI newsletter

But first, let's answer the big question: what are autonomous agents, and why are they such a big deal?

What Are Autonomous Agents?

Autonomous agents are intelligent systems that operate independently, performing tasks, making decisions, reflecting on their work, and continuously improving with little to no human intervention. Think of them as the overachieving interns of the AI world, minus the coffee runs.

They use advanced natural language processing (NLP) and computer vision to process speech, text, and visual information around them, interacting with humans—or even other AI agents. We got a peek at this at the OpenAI demo day recently with GPT-4o, where two AIs chatted away like old friends. Mind-blowing stuff!

Why Do We Care? The Benefits

• Enhanced Efficiency: Perform repetitive and time-consuming tasks quickly and accurately. No coffee breaks needed.
• Cost Reduction: Lower labor costs and minimize errors through automation. Fewer humans making mistakes.
• Improved Customer Experience: Provide personalized and instant interactions. No more hold music.
• Advanced Data Analysis: Analyze vast amounts of data in real-time for better decision-making. Like Sherlock Holmes, but faster.
• Safety: Operate in hazardous environments, reducing risks to human workers. Robots can handle the danger zones.
• Reliability: Improve consistency and reduce human error in various processes. Machines don't have off days.

Applications Expected to Disrupt

• Customer Service: Automating support with intelligent chatbots and virtual assistants. Never talk to a grumpy agent again.
• Healthcare: Enhancing diagnostics, personalized treatment plans, and patient monitoring. Dr. AI is in the house.
• Finance: Automating trading, fraud detection, and customer interactions. Wall Street, meet AI Street.
• Retail: Streamlining supply chain management, inventory control, and personalized shopping experiences. Shop 'til the bots drop.
• Manufacturing: Improving production efficiency, predictive maintenance, and quality control. Factory floors with fewer mishaps.
• Transportation: Enabling autonomous vehicles and optimizing logistics and delivery routes. Self-driving everything.
• Energy: Managing smart grids, optimizing energy distribution, and predictive maintenance of infrastructure. Keeping the lights on, smartly.
• Education: Providing personalized learning experiences and automating administrative tasks. AI tutors at your service.
• Real Estate: Automating property management, smart home systems, and predictive market analysis. Smart homes get even smarter.
• Security: Enhancing surveillance, threat detection, and incident response systems. Robo-guards on patrol.

Although, autonomous agents will significantly boost productivity, but we must ensure a human in the loop ALWAYS especially in critical scenarios like healthcare where human lives are involved.

Making It Tangible in Your Organization

If you are an AI leader, the big question you may have is how to make this tangible in your organization. Enter Autogen, a framework developed by Microsoft Research that helps you create and manage these autonomous agents. It's like having a toolkit for building your own overachieving AI wizards, ready to tackle tasks and make your operations smoother and smarter.

Autonomous agents are set to bring immense value across various sectors, driving significant advancements and efficiencies in the coming years. Buckle up, the future is autonomous!

Autogen Explained - Framework to build Autonomous Agents

Autogen, an open-source multi-agent conversation framework designed to create and manage autonomous agents. It enables these agents to perform tasks, make decisions, and learn independently, reflect on its actions and improve with minimal human intervention.

Basic Concepts

Below I have mentioned, few basic building blocks of Autonomous Agents using Autogen.

• Agents

In AutoGen, an agent is a unit that interacts with other agents in its environment. Here’s what powers an agent:

1. LLMs (like GPT-4): These provide the ability to understand and generate human-like text.
2. Tool Executors: These allow the agent to run specific pre-written code.
3. Code Executors (like an IPython kernel): These enable the agent to execute code in real-time.
4. Humans in the Loop: Humans can step in to guide or correct the agent's actions.

• Roles and Conversations

In AutoGen, you can give roles to agents and have them chat with each other. A conversation is just a series of messages exchanged between agents. These conversations help to move forward on a task.

For instance, in a customer service scenario, you can assign roles to virtual customer service agents and technical support agents to chat with each other, resolving customer issues efficiently through their conversation.

• Chat Termination

In AutoGen, Chat Termination lets you end the conversation between two agents. You can also set a parameter to allow for human intervention. AutoGen provides various mechanisms to control AI agent chats, helping to moderate resource consumption and manage costs related to the conversation.

• Human in the loop?

This feature enables a human to have conversation with the AI agents by providing feedback on the output to steer the agents in the right direction and specifying goals.

However, you have the option to make the interaction between AI agents to be completely autonomous by setting the parameter human_input_mode=NEVER.

• Code Executors

Code executors provide agents the ability to execute free-form code if needed during the conversation. Also, the ability to augment its free-form code with pre-defined code. Based on the conversation, they can generate a code on the fly and then execute it and take actions.

For instance, if an AI agent is asked to generate a stock market chart for Microsoft, it can first plan, then generate necessary free-form code in python, and then execute the code to create a chart and store it as .png file.

• Tools Use?

Tools in AutoGen are pre-defined functions or code that agents can call and use. Instead of writing code from scratch, agents can call tools to perform tasks like searching the web, doing calculations, reading files, or calling remote APIs.

For instance, an agent can use a search tool to find information online or a calculation tool to solve a math problem. By controlling which tools are available to an agent, you control what actions the agent can perform.

• Reasoning and Reflection

AutoGen supports various LLM prompting and reasoning strategies, like ReAct and Reflection/Self-Critique. This notebook shows how to implement general LLM reflection with AutoGen.

Reflection is a strategy where LLMs analyze outputs and provides feedback.

For example, if you are creating a team for automating the process of blog post writing. You can create a that provides critique of the generated writeup, which the writing assistant uses to improve the content.

• Conversation Patterns

Autogen provides several conversation patterns to enable effective productive communication between a team of agents and humans.

1. Two Agent Chat: It is the simplest form of communication between two agents. For example, simulating a student and teacher interaction when solving a math problem. It has inbuilt functionalities to summarize the conversation too.
2. Sequential Chat: This type of chat is beneficial in task-based scenarios where a complex task can be broken down into interdependent sub-tasks. This is great for scenarios where tasks need to be performed sequentially. For example, in a customer support setting, resolving a technical issue might involve a series of steps: diagnosing the problem, providing troubleshooting instructions, and then confirming the solution with the user. Each step depends on the completion and results of the previous one, making sequential chat an ideal approach to ensure a smooth and efficient resolution process.
3. Group Chat: This type of interaction requires more than two agents and is orchestrated by a Group Chat Manager. The Group Chat Manager selects an agent to speak. The chosen agent sends a message back, which the manager then broadcasts to the other agents. The Group Chat Manager can select the next speaker using various strategies, including round-robin, random, manual, and automatic modes. For instance, this approach can disrupt financial advisory applications by enabling coordinated interactions among multiple AI financial advisors, ensuring structured and comprehensive investment strategies and personalized advice for clients through collaborative decision-making. They are useful for scenarios requiring broad collaboration, such as team discussions, brainstorming sessions, or multi-agent customer service interactions.
4. Constrained Speaker Selection: Group chats can be challenging to manage with many participants. AutoGen makes this easier by providing a way to control who speaks next, ensuring smoother transitions between speakers
5. Nested Chat: Nested chats are used to handle complex tasks by breaking them down into a series of sequential interactions among different agents. In nested chats, an initial message triggers a sequence of subordinate chats, each involving different agents performing specific roles. The nested chat handler coordinates these interactions, ensuring that the output from one agent is passed to the next in the sequence. Ideal for scenarios where a task requires multiple steps or specialized processing, such as writing and reviewing a document, performing a series of calculations, or generating a complex report
6. Cost Optimization: The interactions between agents can become costly since each interaction involves an API call to the LLMs, which charges based on tokens used. Therefore, AutoGen provides features to monitor and manage the costs of these interactions effectively as shown in image below.

Future of Autogen

The future of AutoGen is incredibly EXCITING! This framework is set to continuously evolve, introducing more multimodal features into interactions. Imagine agents not just understanding text, but also seeing and speaking, comprehending visual and audio inputs. This leap will transform interactions, making them more dynamic and immersive than ever before!

Conclusion

I hope you enjoyed my blog post on Autonomous Agents and AutoGen! If you're itching to roll up your sleeves and get your hands dirty with Azure, check out my GitHub repository - Link

This repository is inspired by Andrew NG's Deeplearning course "AI Agentic Design Patterns with AutoGen" which I highly recommend.

By the way, my awesome coauthor Paul Singh and I recently had the pleasure of publishing a book on Generative AI. If you liked what you read here, feel free to get your copy and don't forget to leave a review on Amazon! Thanks for your support.

Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments: Karuparti, Anurag, Maeda, John, Singh, Paul: 9781835084786: Amazon.com : Books

References

AutoGen | AutoGen ( microsoft.github.io )

DLAI - AI Agentic Design Patterns with AutoGen ( deeplearning.ai )