[MUSIC]
FRIEDERIKE NIEDTNER, Principal Technical Research Program Manager, Microsoft Research AI Frontiers:
The following talk invites us to follow the journey of AutoGen from a leading open-source
framework for multi-agent applications to a complete redesign that lays the foundation
for the future of agentic AI research and applications with the release of AutoGen
0.4. The framework's new layered architecture provides flexibility and scalability and includes
an ecosystem of extensions and applications, some created by the same team, such as Magentic-One, a
team of generalist agents, and Studio, a low-code developer tool. AutoGen 0.4 is also a story about
collaboration between MSR, partners within Microsoft, and a vibrant open-source community.
[MUSIC]
GAGAN BASAL:
Hi, I am Gagan Bansal and I am a researcher at Microsoft Research AI
Frontiers. And today I'll talk about some exciting technical updates to AutoGen,
a leading open-source framework for agentic AI. And although I am presenting,
this is joint work with many incredible colleagues and interns at Microsoft over the last year.
AutoGen is a leading open-source framework for multi-agent applications that we released in
fall 2023. It enables developers and researchers to create intelligent applications using large
language models, tool use, and multi-agent collaboration patterns. With AutoGen,
our goal has been to lead the innovation in agentic AI research. When we first launched
AutoGen in Fall 2023, it quickly became the leading open-source framework for agentic AI,
and it continues to empower developers and researchers in many, many domains,
including business process automation, marketing, finance, security, and others.
Since AutoGen's launch, we've not just been maintaining it. We've been listening closely
to feedback from developers and researchers, and in this rapidly evolving landscape of AI progress,
their expectations were high. Users told us that they needed
greater modularity and the ability to reuse agents seamlessly. They also asked for better
support for debugging and scaling their agentic solutions. And finally,
there were many apps to enhance the code quality and maturity of the platform.
Pursuing these needs required us to question our assumptions and even possibly reimagine
the platform. So, in early 2024, we used these learnings to experiment with alternate
architectures, and we ended up adopting an actor model for multi-agent orchestration. The
actor model is a well-known programming model for concurrent programing and high use systems. Here,
actors are the computational building blocks that can exchange messages and
also perform work. In Fall 2024, we announced a preview of this version and this new year,
we're thrilled to announce a full release. In summary, AutoGen v0.4 is our response to address
our users’ feedback in this evolving landscape of AI research. AutoGen is now not just a framework,
but it's a whole ecosystem for agentic AI. It provides you with a framework
that lets you build sophisticated agents and multi-agent applications,
and it also provides you with developer tools and many well-defined applications.
Let me first tell you about the AutoGen framework. At the heart of this release
is a layered architecture that is designed for flexibility and scalability. At the base
is AutoGen Core. This layer implements the actor model for agents. Building on
core is AutoGen AgentChat. This layer provides a simple and easy to use API
that is perfect for rapid prototyping. And building on Core and AgentChat is Extensions.
This layer provides advanced clients, agents and teams, and integrations with third party
software. This layered architecture is nice because whether you are an advanced
developer or a researcher prototyping new ideas, AutoGen provides you with
the tools you need for your project’s stage of development. The Core implements an actor model
for agentic AI. At the highest level, this implementation provides two key features.
The first is asynchronous message exchange between agents. It does so by providing a runtime,
and then it also provides event-driven agents that perform computations in
response to these messages. There are several implications of this design,
and one of them is that it decouples how the messages are delivered between the
agents from how the agents handle them. This naturally improves the modularity
and scalability of agentic workflows built with AutoGen, especially for deployment.
The Core’s event-driven architecture provides several other benefits. For example,
it provides affordances to observe and control agent behavior, which is crucial for responsible
development of agentic technology. It also enables running multiple agents on different processes and
even implementing them using different languages. Finally, it enables developers to implement
a large class of multi-agent patterns, including static and dynamic workflows.
When we released AutoGen, one of the first things that the developers absolutely loved
about it was its simplicity and the many pre-built agents and teams that it provided,
such as the user proxy agent and the assistant agent, and the group chat between multiple
agents. With the AutoGen AgentChat layer, we are maintaining these features and adding tons of
more essential features such as streaming support, serialization, state management
and memory for agents, and finally full-time support for a better development experience.
Please check out the link below for the migration guide. Finally,
the Extension layers provide advanced runtimes, tools, clients, and ecosystem integrations
that continuously expand the framework's capabilities. In addition to the framework,
this new release also provides upgrades to essential developer tools and applications
built using AutoGen. And here I'll briefly mention two of them. In late 2023, we also
released AutoGen Studio, which is a low code tool for authoring multi-agent applications.
And we are excited to announce that with version 0.4, Studio has received massive upgrades. It now
supports a drag and drop, multi-agent builder. It supports real time updates as agents solve tasks,
flow visualizations and execution controls, so that the users remain in control,
and component galleries so that the community can discover and build on each other's work.
We've always believed that the framework should enable state-of-the-art applications
for solving complex tasks with agents, which is why we've been building applications with
the framework ourselves and using that to guide the framework's development.
Last year, we released Magentic-One, a state-of-the-art multi-agent team for
solving file- and web-related tasks built using AutoGen. And now its developer API,
and general capabilities, such as sophisticated orchestrators and specialized agents such as
the web server and the file server, are now available in the AutoGen ecosystem. For us,
this new ecosystem is only the beginning and sets the stage for future innovation in agentic AI.
Over the past two years, our team has made early progress in AI agents and we continue to deeply
think about the changing landscape of current AI research and continue to invest in taking steps
to help lead the innovation on agents. And by the way, we're also working closely with
our colleagues at Semantic Kernel, to provide an enterprise ready multi-agent runtime for AutoGen.
Thank you for attending Microsoft Research Forum.
Please check out these links to learn more about AutoGen.