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Learn how to build autonomous AI systems using event-driven architectures in this conference talk that addresses the limitations of traditional request/response patterns for LLM-powered agents. Discover the StreamNative Agent Engine, an event-driven framework evolved from Pulsar Functions, designed specifically for real-time autonomous AI systems where perceptions, plans, and actions are represented as events. Explore how declarative agent meshes compile to run as event-driven functions, enabling workflow and agent orchestration through streaming joins and time windows. Master techniques for back-pressure handling and retries that maintain LLM responsiveness while implementing idempotency, observability, and safety patterns for real-time agent systems. Gain insights from real-world lessons learned running autonomous agents at scale, transforming static agent pipelines into resilient, scalable runtimes that support continuous interaction, feedback, and coordination among multiple agents without blocking LLMs or sacrificing reliability.
