Agentic AI, with its suite of advanced capabilities, is at the forefront of transforming everyday business tasks. One of its leading tools, AutoGen, is redefining how enterprises integrate AI into their operations. By enabling seamless collaboration, efficient workflows, and human-in-the-loop processes, AutoGen is setting a new standard for AI-driven productivity [1].
Learn about Microsoft AutoGen 0.2
What is Agentic AI?
Agentic AI refers to artificial intelligence systems designed with the ability to act autonomously, make decisions, and adapt to changing environments based on their objectives and the data they process. These systems exhibit characteristics of agency, including goal setting, planning, and decision-making, making them particularly powerful in complex and dynamic scenarios [2].
What is AutoGenStudio?
AutoGenStudio allows users to explore the capabilities of Agentic AI in a low code and easy to use UI built upon AutoGen, Microsoft’s open-source Python-based AI Agent framework. The UI enables users to prototype agents, skills, and workflows. You can see how AutoGen works in this demo:
AutoGen Agents are conversable, customizable and adaptable [3]. They can integrate with many LLMs, tools and human input. Using python code, they can be equipped with various skills such as natural language processing, predictive analytics, or computer vision. They can learn and adapt over time, improving their performance based on interactions and feedback.
Multiple agents can be orchestrated to work together in teams using tools like GroupChat and GroupChatManager or within sequential workflows [3, 4]. Agents can complete their tasks using advanced AI models and tools [2- 5].
How to Collaborate with Agents
- Set-it-and-leave-it: Create a workflow that can be completed by the agents and integrated into existing business processes.
- Human-in-the-loop: Create a workflow that incorporates frequent checkpoints where the agents require a user’s input before continuing the workflow.
Oversee your Agents: Human-in-the-Loop in Sequential Workflows
One important feature of the AutoGen framework is its ability to implement human-in-the-loop workflows. This approach ensures that AI-powered processes remain grounded in human oversight, striking the perfect balance between automation and human judgment [4]. Users can interact with agents within a workflow during a sequence of events or provide real time feedback to be integrated into a running workflow.
Collaborate with your Agents: Chat with your Team of Agents
You can design a multi-agent team with specialized skillsets and collaborate with your highly specialized team. For example, a group chat can contain agents each with specific roles including a writer, illustrator and editor. These agents share messages in a single conversation thread that a human user can also join and help guide the agents.
Each agent takes turns sending messages, one at a time, in a specific order managed by a Group Chat Manager. This manager decides which agent speaks next based on a set of rules, like taking turns in using a round-robin algorithm or a selector with an LLM [3- 6].
You can also add your agents to contextual discussions linked to specific workflows or tasks. Team members (including agents) can share insights, flag issues, and propose solutions in real-time. Agents can analyze chat discussions, summarize key points and suggest actionable steps which can help save time.
What drives Agent Function?
Agentic AI is built upon state-of-the-art frameworks such as OpenAI’s GPT models for natural language processing, TensorFlow for machine learning, and PyTorch for deep learning [2, 5]. AutoGen, specifically, leverages OpenAI’s GPT architecture for its natural language capabilities and integrates seamlessly with TensorFlow pipelines for workflow automation [5]. This combination offers a robust, scalable, and adaptable platform that caters to a wide range of business needs that we have prototyped using AutoGenStudio.
AutoGen Real-World Applications Across Industries
Industries are already reaping the benefits of AutoGen. For example, the following companies are using Agentic AI:
- DesignMind – Data analyses and data insights
- Novo Nordisk – Enabling clinical data insights for the broader community [8]
- NTT Data - autonomously extracting, organizing, and executing tasks in response to user instructions [9]
- Microsoft - automating sequences of tasks/workflows using Microsoft copilot agents [10]
By combining the power of Agents with human oversight, Agentic AI is transforming how enterprises approach their business processes. Ready to take your business to the next level? Explore the capabilities of Agentic AI with DesignMind today.
Maiyi Tembo, PhD, is Senior Data Science and AI Consultant at DesignMind.
References
- Introducing AutoGen Studio: A low-code interface for building multi-agent workflows - Microsoft Research
- Agent AI Towards a Holistic Intelligence
- All About Agent Descriptions
- AutoGen Studio: Interactively Explore Multi-Agent Workflows
- AutoGen Group Chat
- Automated MultiAgent Chat
- AutoGenStudio Technical Documentation
- What's new in AutoGen?
- NTTA AutoGen Use Case
- Microsoft AI Tour Boston: Exploring Microsoft Copilot and Autogen with Experts