AGNTCon + MCPCon Japan 2026

David will discuss the need for connectivity in an era of increasing AI capabilities. Open standards like MCP are at the heart of the AI revolution and the increased ability of models to reach out into the world. We discuss the last two years of MCP and look into the future of connectivity standards in the industry.
___________________________
Presentation Language: English

AI agents are rapidly evolving from chatbots into autonomous systems that use tools, collaborate with other agents, and interact with enterprise data. But as their capabilities grow, so do the operational challenges. How do you know which agent consumed your AI tokens? Which tools were invoked? How do you govern, secure, and observe agent behavior across an increasingly complex ecosystem?

In this demo-driven keynote, Lin invites the audience to become part of a live agent experience, where her AI agent orchestrates multiple services and MCP tools to transform the audience in an unexpected way. Throughout the demo, every request flows through agentgateway, showcasing how an AI gateway enables secure access to MCP servers, intelligent traffic mediation, policy enforcement, cost attribution, and end-to-end observability for production-ready agentic systems.

___________________________
Presentation Language: English

As organizations build more capable agents, those agents will need a common way to communicate and work together across frameworks, vendors, and platforms. This keynote examines the current state of A2A, the problems it is designed to solve, and where the project is headed. We'll also see A2A in action through a production multi-agent system, including what happens when one of its agents is compromised.

___________________________
Presentation Language: English

Captioning will be available for attendees in 50+ languages through Wordly. See instructions in each room to utilize captioning.

Autonomous assistants are easy to demo and hard to make reliable. The problem is usually not tool access. It is memory.

Most assistant architectures still treat memory as a chat log plus vector retrieval. That is fine for document question answering, but it breaks down when the assistant must connect conversations, people, tools, and decisions across multiple tool iterations. For an AI engineer, a single request can depend on a Slack thread, a GitHub PR, a failed CI run, a calendar event, and prior operating preferences or constraints. These are not isolated pieces of context. They form a connected state that changes as work progresses and context grows.

In this talk, I’ll show why knowledge graphs, context graphs, and GraphRAG provide a better foundation for OpenClaw-style assistants. Knowledge graphs capture durable entities and relationships. Context graphs capture the operational layer assistants usually lose, including actions, decision traces, provenance, and recency. GraphRAG turns that structure into task-time context by combining graph traversal, semantic retrieval, and tool use.

Attendees will leave with practical patterns for schema design, retrieval routing, and evaluation, plus a concrete blueprint for assistants that remember more than the last prompt and retrieve more than the nearest chunk.

___________________________
Presentation Language: English

Captioning will be available for attendees in 50+ languages through Wordly. See instructions in each room to utilize captioning.

AGENTS.md is the file that tells any coding agent how to work in your repo. One open AAIF standard, read by Codex, Cursor, Claude Code, and goose. But it is static. Written once, by hand. It drifts as the code moves. Auto-generate it and agents often get worse, following bloated rules and chasing dead paths. The agent fails the same way every run. Nothing feeds those failures back. The text that drives it never learns.

This talk show how to fix that with a layer on top of AGENTS.md that tunes it from the agent's own runs.The loop is small. Run a task. Reflect on the trajectory in plain language. Find what broke. Propose one targeted edit. Validate it against an eval harness before it sticks. Reflect, mutate, validate, select. Borrowed from reflective optimizers like GEPA. No RL. No fine-tuning. Just an agent learning from its mistakes.

The layer is tool-agnostic because AGENTS.md is, and it extends to MCP tool selection. We measure what moved, task success, edits per gain, token cost, regressions on a held-out set. We beat hand-written and auto-generated files. You leave with a layer you drop onto the AGENTS.md you already have.

___________________________
Presentation Language: English

Kubernetes upgrades are high-stakes, deprecation-laden, and still mostly manual — most "AI for K8s upgrades" projects stop at a feasibility check. We went further: we built an agentic system that planned and executed a real cluster upgrade end to end, and in this talk we walk through exactly how it went, with the steps, evidence, and screenshots from our actual runs.


We'll break down the architecture we shipped — a planner → executor → verifier loop, deprecated-API and CRD/Helm compatibility analysis run before anything touched the cluster, a human-in-the-loop approval gate for irreversible steps, and health-gated automatic rollback. Using captured logs and screenshots, we'll show a real multi-node upgrade as it happened, the failure cases that nearly broke it (version skew, stuck drains, webhook deadlocks), and the decisions we deliberately refused to let the model make.


___________________________
Presentation Language: English
Captioning will be available for attendees in 50+ languages through Wordly. See instructions in each room to utilize captioning.

Run the same prompt twice and you get two different agents. Both work. Neither is the same. If you ship agents to customers rather than demos, this is a failure mode you already know, and it is why agent work has not industrialized.

Today's AI coding is 3D printing: brilliant for one-offs, unreliable at scale. Assembly is the alternative, and agents have finally converged enough to make it possible. They have a bill of materials: model, harness, interface layer, state, tool integrations. Open source ships every part. Nobody ships the guarantee that two of them work together.

You will leave with the six-station line we run, why moving the model from author to assembler cuts variance and token spend at the same time, the compatibility matrix we maintain, and the list of component pairs that deadlock, which is the part almost nobody publishes.

We will also name what assembly does not fix, and where reproducibility stops being enough.

---

In order to facilitate networking and business relationships at the event, you may choose to visit a third party's booth or access sponsored content. You are never required to visit third party booths or to access sponsored content. When visiting a booth or participating in sponsored activities, the third party will receive some of your registration data. This data includes your first name, last name, title, company, address, email, standard demographics questions (i.e. job function, industry), consenting to receipt and use of such data by the third-party recipients, which will be subject to their own privacy policies. 

___________________________
Presentation Language: English