2026-06-06 –, Grand Hall 2
Multi-agent AI systems promise autonomous reasoning, but most tutorials stop at prototypes. This talk shares hard-won lessons from deploying a production multi-agent RAG platform on serverless AWS , covering agent orchestration patterns, cross-region LLM routing, vector search cost optimisation, and the observability strategies that keep it all running reliably.
You'll learn concrete patterns for coordinating multiple RAG-enabled agents via SQS and Lambda, the cost/latency trade-offs between managed and self-managed vector search (including how to achieve 90% storage savings), and practical observability strategies using Langfuse and dead-letter queues. Whether you're scaling your first RAG system or architecting multi-agent workflows, you'll leave with actionable patterns you can apply immediately.
Modern AI applications increasingly require multiple specialised agents working together, but orchestrating them reliably at scale is challenging. This talk walks through the architecture and lessons learned from building a production multi-agent financial analysis platform.
Outline:
- Minutes 0-5: Why single-agent RAG hits limits — the case for multi-agent orchestration
- Minutes 5-15: Architecture deep-dive — SQS-based agent coordination, Lambda handlers, and how RAG retrieval integrates across agents
- Minutes 15-22: Cross-region Bedrock routing and why latency geography matters
- Minutes 22-30: Cost lessons — achieving 90% vector storage savings with S3 Vector Search vs managed alternatives
- Minutes 30-37: Observability and failure handling — Langfuse tracing, DLQs, and debugging distributed agent calls
- Minutes 37-40: Key takeaways and Q&A
Target audience: ML engineers, data scientists, and developers building production AI systems. Familiarity with RAG concepts and basic AWS services assumed; no multi-agent experience required.
Samuel is a Gen AI Engineer at Capgemini UK, building production multi-agent systems for enterprise clients. He is also the founder of Atlasync AI Ltd, an early-stage AI startup focused on compliance automation. He founded and organises PyData Hull, the UK's newest NumFOCUS chapter. Samuel holds an MSc in AI and Data Science with Distinction from the University of Hull and is AWS certified. His work focuses on multi-agent architectures, RAG pipelines, and agentic observability.