Dominik Kundel from OpenAI's Codex team explains the Codex App Server, the open-source JSON-RPC protocol that wraps the Codex harness and powers the Codex app, IDE extensions, and third-party clients. He covers live protocol events, more than 120 client messages, ChatGPT sign-in, developer instructions, deferred dynamic tools, shipping and binding generation, the Python SDK, and a demo of Codex running inside Doom.
Abhi Arya from Reducto shares how the team went from a one-tool-per-endpoint MCP server that confidently built workflows nobody could explain to a design that makes uncertainty visible, asks instead of guessing, and improves itself through instrumentation. The talk centers on one question: who pays when the agent is wrong?
Rafal Wilinski of Runlayer argues that when one agent solves a hard problem, that knowledge should become a skill shared across the whole company. He covers why skills matter more as agents run longer, three problems with current skills, using MCP as a governed distribution layer, distilling skills from successful and failed agent traces inspired by the Voyager paper, and the organizational flywheel as a durable competitive advantage.
Gumloop co-founders Max Brodeur-Urbas and Rahul Behal discuss how a visual workflow builder evolved into an enterprise agent platform, what they learned from more than 1,100 customer calls, how a power user at Instacart opened the door to enterprise sales, and why the people who understand a task should be the ones automating it. The conversation covers enterprise requirements, pricing transparency, model neutrality, and the IT-led buying dynamics that shape adoption.
Jan Čurn, founder and CEO of Apify, argues that the backlash against MCP targets the wrong layer. Context bloat and token burn come from naive agent harnesses, not the protocol. He reviews the industry's three main fixes (sub-agents, progressive tool discovery and Code Mode), explains why agents work well with CLIs, proposes MCP for remote access and CLIs for local use, and demos mcpc, Apify's open-source universal MCP CLI client, alongside early Connector Evals results.
Alex Hancock discusses the need for a standardized protocol for client-to-agent communication in AI, similar to how MCP standardizes agent-to-tool interaction. He introduces the Agent Client Protocol (ACP), born from Zed and JetBrains, as a JSON RPC-based solution for client software to control agent harnesses, send tasks, and receive updates. Hancock demonstrates ACP's flexibility with multiple clients driving a single agent and highlights how its new remote transport capability, combined with MCP and model APIs, enables independent placement of all agentic stack components, fostering a competitive ecosystem that will significantly enhance user experience.