2025-06-06 –, Grand Hall
In this tutorial, we will cover basic and advanced agentic design patterns in AG2 and we will go through practical implementations to demonstrate AI agents in action.
Majority of knowledge work nowadays requires comprehensive, integrated research in order to uncover deep insights. While existing technologies have advanced, the data deluge and fragmented complex systems mean extensive resources and specialised teams are still necessary. AG2 AgentOS changes this paradigm by seamlessly enabling multi-agent systems to solve complex tasks and aggregate diverse data sources to achieve outcomes that would usually take even experts a lot of time.
In this session, we will cover:
1. Design patterns and practical implementations to demonstrate AI agents in action such as
- Customized GroupChat
- Code execution
- Deep Research Agent
- Swarm
- Tool using
- Async chats
- Dynamic instructions
- Realtime Agent
- GraphRAG
- Structured Output
2. The anatomy of a Vertical AI agent application and how to seamlessly integrate multiple agents powered by models from OpenAI, Anthropic, Gemini, and open-weight providers, and a diverse range of tools to build your own vertical agent.
3. We will utilise components we’ve learned to collect information from the internet, connect to a data room, and create various modelling functions to replicate analysis done in a technical and commercial deep dive in a startup.
4. Explain how to contribute to the thriving AI agent ecosystem.
Target industries and use cases:
1. Industries that require deep research, such as finance, healthcare, science & engineering. Research/Analysis/Science use cases: Deep Research Agent, SciAgents, Financial Analysis, AutoML Agent.
2. Industries involving customer support, such as e-commerce, education, social media. Customer-oriented use cases: Travel Planner, Order Management, Realtime ToDo Assistant, Email Management, -Social Media Management, Youth Helper.
3. Industries involving heavy software design & development, such as gaming, web, data engineering. Software-oriented use cases: Game Design Agents, Web Agent, Software Testing Agent
At the end of the tutorial, the attendees would gain a better understanding of agent-oriented programming concepts and how to reach production-readiness 10x faster. Through the examples given, they will be able to construct effective multi-agent systems to solve complex tasks. They will have reusable building blocks to customize for their own vertical agent.
Previous knowledge expected
I'm leading Graphcore’s Cloud Solutions ecosystem helping AI & ML software development teams build AI products and deploy ML capabilities in production. I've gained experience taking AI applications from the research lab to large scale deployments. Also a Deeplearning.ai ambassador and founder of AI Hive community, startup advisor and visiting fellow at zinc.vc.
Chi Wang is founder of AutoGen (now AG2), the open-source AgentOS to support agentic AI, and its parent open-source project FLAML, a fast library for AutoML & tuning. Chi runs the AG2 community with 20K+ members. He has received multiple awards such as best paper of ICLR'24 LLM Agents Workshop, Open100, and SIGKDD Data Science/Data Mining PhD Dissertation Award. He has 15+ years of research experience in Computer Science from Google DeepMind, Microsoft Research, Meta, UIUC and Tsinghua.