Arca, an AI-native financial services firm, runs its client business through long-horizon agents that own goals end to end across days of stop-start work. Aman Singh, who leads AI there, explains the signal system, operational context, harness, persistent memory, and shared spaces that let agents pull in people at the right moment, and why steering agents becomes the way to steer the business.
Nacho Martinez argues that the model is fixed while the harness (agent loop, memory, context engineering, tools and skills, continual learning) is where engineers can make real gains. He walks through the agent stack and applies it to an autonomous AML agent that flags suspicious bank transactions, then demonstrates the workshop environment and answers questions on memory design, skill retrieval, caching, and hybrid search.
Gil Feig, co-founder and CTO of Merge, explains why agents need a company-wide context graph rather than a few MCP connections. Using the question of which customers were upset last week, he shows why live API lookups fail across large datasets and why synced local copies, routing, summarization, skills, memory, and traceable provenance are needed to build a reliable company brain.
MasterClass engineers Mandar and Priya explain how they built NCX, a multi-agent AI tutoring system, and the CoreWeave-based tracing, MCP server, and AgentOps loop that keeps it reliable and self-improving in production.
Kamalakannan Nandagopal from Postman explains why coding agents struggle beyond a single repo and how an API context graph, grounded in code and production telemetry, gives agents system-wide awareness. The talk covers evals built from real PRs, wins on API discovery, redesign and impact assessment, a stale-graph failure, and a 21-page architecture review generated by the agent.
Neo4j's Will Lyon argues that embedding-based retrieval gives agents recall but not actionable knowledge. He presents a context graph model of agent memory with short-term, long-term, and reasoning memory, then shows how to distill grounded, governed skills from that graph using APE typed execution graphs, with grounding, coverage, and coherence checks, staleness alerts, and a live demo of the Neo4j Agent Memory Service building a patient-intake skill.