Event-Driven AI Agents: Orchestrating Long-Context Legal Processing at Scale

Building AI agents that process legal documents with 10,000+ pages requires more than throwing context at an LLM. This talk dissects our production event-driven architecture. You'll learn: - Why synchronous LLM calls fail at legal document scale - the cold start and a timeout problem - Event-driven orchestration patterns for multi-step agent workflows (planning, dynamic loading, context assembly) - Long-context chunking strategies that preserve legal reasoning chains - Cost optimization: how we reduced per-request costs 73% through intelligent caching and selective context loading - Production failure modes: what breaks when agents plan incorrectly Concrete architecture, real metrics, battle-tested patterns. No theoretical frameworks - this is what actually runs in production, processing legal contracts for 10M+ users.