Elizabeth Fuentes Leone, developer advocate at AWS, explains why agents break when tools return huge outputs, why bigger context windows do not fix the problem because models lose the middle of long contexts, and how four context engineering strategies (externalize, select, compress, isolate) can be implemented with the open-source Strands Agents framework.
Eli Cohen of Snyk argues that AI-generated code and AI-powered attackers have broken the traditional security model. He calls for continuous offensive security built on AI pentesting on every code change, agent red teaming, and a multi-agent system that validates exploits. He compares SAST, DAST, and human pentesting, and closes with four questions to ask any AI pentesting vendor.
Vladimir Keil, co-founder and CEO of Lio, joins Seema Amble and Elena Burger to explain why AI-native startups can beat incumbent software vendors even when those vendors own the customer, the data, and the system of record. The discussion covers the work that happens outside systems of record in enterprise procurement, Lio's multi-agent architecture spanning retrieval through principal agents, how buyers learn to trust agents with consequential negotiations, and what happens when suppliers and buyers both run agents.
Halluminate, a Y Combinator S25 company, builds RL environments and benchmarks that teach AI models to do non-coding knowledge work, starting with financial services. In this fireside with YC Partner Jon Xu, co-founders Jerry Wu and Wyatt Marshall trace their pivot from evals to training environments, explain why verification quality matters more than task volume, and outline a roadmap toward multi-agent simulated companies. They also address reward hacking, alignment as an industry-wide responsibility, and what they look for in new hires.
Vlad Luzin, co-founder and CTO of Band, argues that connecting AI agents is a distributed systems problem that current tools such as MCP, A2A and messaging platforms do not solve. He lays out what is required for real agent collaboration, including ordered real-time transport, persistence, runtime binding, agent-first abstractions and governance, then demonstrates Codex, LangGraph and Claude Code agents finding and working with each other under full human-in-the-loop visibility.
A walkthrough of CoreWeave's three inference paths (Serverless, Dedicated, and Inference on CKS), covering when each fits, how a multi-agent research assistant uses OpenAI-compatible endpoints across model types, and live demos of gateways, deployments, and Kubernetes-based vLLM, Dynamo, and LLM-D serving.