AutoGen

AutoGen is Microsoft's framework for building multi-agent conversational systems where AI agents can collaborate, debate, and solve complex problems together through structured conversations.

Why we're assessing AutoGen:

• Multi-Agent Conversations: Native support for agent-to-agent communication and collaboration
• Role-Based Agents: Define specialized agents with specific roles and capabilities
• Group Chat Dynamics: Multiple agents can participate in structured conversations
• Human Integration: Seamless human-in-the-loop patterns for complex workflows
• Code Generation: Specialized agents for code writing, review, and execution

Potential advantages:

• Complex Problem Solving: Break down complex tasks across multiple specialized agents
• Quality Assurance: Agent peer review and validation processes
• Diverse Perspectives: Different agents can approach problems from different angles
• Scalable Collaboration: Add new agent roles without rewriting existing workflows

Evaluation considerations:

• Learning Curve: More complex than single-agent patterns like ReAct
• Token Costs: Multi-agent conversations can be token-intensive
• Coordination Overhead: Managing agent interactions and preventing conflicts
• Integration Complexity: How well it integrates with our Knative/LangChain stack

Potential use cases:

• Code Review Workflows: Multiple agents reviewing and improving code
• Business Analysis: Agents with different expertise analyzing business problems
• Content Creation: Collaborative content development with specialized roles
• Quality Assurance: Multi-agent validation of agent outputs and decisions

Next evaluation steps:

• Pilot project comparing AutoGen vs. LangGraph for multi-agent workflows
• Performance and cost analysis for agent collaboration patterns
• Integration testing with our existing monitoring and deployment infrastructure