Building AI agents that can reason, plan, and execute tasks requires orchestrating dozens of concurrent LLM calls, tool invocations, and external API requests.
This talk explores how Python's async ecosystem—asyncio, threading, and multiprocessing—provides the foundation for building responsive, scalable agentic systems.
We'll examine real patterns for concurrent agent workflows: managing parallel tool execution, handling streaming responses, implementing graceful timeouts, and coordinating multi-agent collaboration.
Through practical code examples, attendees will learn when to use asyncio.gather() versus TaskGroups, how to avoid common pitfalls like blocking the event loop with synchronous LLM clients, and strategies for mixing async and sync code in production systems.
The talk also compares how popular Python agent frameworks (LangGraph, CrewAI, AutoGen) leverage async patterns under the hood, helping developers make informed architectural decisions.
Attendees will leave with concrete Python patterns they can immediately apply to build faster, more responsive AI applications—whether they're creating simple chatbots or complex autonomous agents.