Evan Kormos of Coinbase presents a practical framework for bringing AI agents into policy-driven, regulated workflows. He models processes as DAGs with loops inside each node, argues that a loop is only as trustworthy as its check, and shows how evals and traces create a new confidence mechanism. Agents then move through supervised, sampled, and exception-only stages.
Harish Gaggar of Intuit Credit Karma walks through an internal paved path for taking enterprise AI agents from experiments to production. The talk covers portal-based registration and provisioning, a centralized authenticated MCP gateway with custom reviewed MCP servers, gateway monitoring with OpenTelemetry, Tempo, Prometheus, and Grafana, LangGraph workflows with policy checks and human approval, durable memory through threads and checkpoints, agent tracking and evaluation records, PII filtering, and context optimization for token reduction.
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.
This video tackles the overwhelming choice of agentic AI frameworks by categorizing projects into five types: linear workflows, autonomous multi-agent systems, role-based AI, production orchestration, and rapid prototyping. It details each type with examples and recommends specific frameworks like LangChain, AutoGen, and CrewAI, guiding developers to select the optimal tool based on their system design and real-world needs.
Sandipan Bhaumik from Databricks explains that scaling from one to many AI agents is a distributed systems problem, not an AI one. He details common architectural anti-patterns like shared mutable state that cause race conditions and silent failures. The talk provides a practical framework based on distributed systems engineering, covering crucial patterns like choreography vs. orchestration, immutable state management with versioning, data contracts, and failure recovery using circuit breakers and compensation (Saga) patterns. Bhaumik illustrates how to build a robust, production-grade multi-agent architecture using tools like Databricks, LangGraph, and MLflow.
This panel delves into the strategic implementation of open-source, community-driven agentic stacks, balancing the benefits of open-source frameworks and models for rapid iteration, cost-efficiency, and compliance against the complexities of abstraction and debugging in production environments. Experts from NVIDIA, Stacklok, and Prosus discuss when to adopt open source, challenges in framework choice, the critical need for robust observability, and the role of no-code solutions for diverse user groups, emphasizing the importance of aligning tooling decisions with specific use cases and organizational needs.