AGNTCon + MCPCon Japan 2026

MCP makes it easier for AI agents to connect to enterprise systems, but moving from experimentation to production raises a deeper question: is controlling what an agent can access enough? An agent may have permission to update customer data, but that doesn't mean every authorized action should be executed in the same way.

This talk introduces the distinction between Access Control — what an agent is allowed to do — and Execution Control — how an authorized request should actually be carried out. We'll explore how mechanisms such as business rules, deterministic logic, and auditability, among others, can work together behind MCP tools to shape how execution happens in the enterprise.

The goal is to provide a practical architectural lens for teams taking MCP beyond adoption and into production: enterprise trust requires controlling not only what agents can access, but how their actions are executed.

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Presentation Language: Japanese

As AI agents move from experimentation to production, authorization is becoming a core architectural concern. The MCP Authorization specification provides a standardized approach for protecting MCP resources and obtaining access tokens, but many developers have not yet seen these mechanisms in action.

In this hands-on workshop, participants will build a secure MCP environment using Keycloak, agentgateway, and MCP Inspector. Starting from an MCP server where all tools are publicly accessible, attendees will progressively introduce authorization controls using Keycloak and agentgateway, and observe how access to MCP tools changes.

Participants will use MCP Inspector to follow authorization flows step by step, including resource discovery, authorization server discovery, user authentication, token acquisition, authorization failures, and authorized tool invocation.

The workshop also explores Step-Up Authorization scenarios introduced in the latest MCP Authorization specification.

By the end of the session, participants will have a working MCP Authorization environment and practical experience securing MCP-based AI agent systems.

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Presentation Language: English

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

Enterprise adoption of the Model Context Protocol is accelerating, and MCP has become the primary way agents connect to enterprise tools and data. But MCP is only part of how agents act. Agents also run CLIs, execute Skills, and generate code that calls APIs directly. Governing MCP well matters. Governing everything else agents can do matters just as much.

Building MCP servers and writing Skills isn't particularly hard. The real challenges are deciding which actions agents are allowed to take, controlling who can take them, and proving it all later. These are architectural questions, and they need answers before agents scale across an organization.

In this workshop, we will:

1. Show how to control agent actions with policies that apply across MCP servers, CLIs, Skills, and agent-generated code — including allowlists, access control by users and groups, and human-in-the-loop approvals.
2. Explain why enterprises need managed registries for MCP servers and Skills, and how admin review and approval change the trust model.
3. Work through audit and compliance requirements: capturing complete logs of agent and tool activity, exporting to enterprise storage, and generating reports.
4. ⁠Demonstrate how to discover shadow AI — unmanaged agents, MCPs, and Skills already running in your organization — and how to block them or bring them under management.
5. Look at token usage and spend visibility by agent, user, and group.

You'll leave with a clear picture of the architectural decisions ahead of you, and a better sense of what your security team will require before signing off on scaling AI agents across your organization.

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

As multi-agent systems move to production, managing persistent state and data lineage across independent tools is a critical engineering bottleneck. This session explores the architectural gaps in current agent workflows and introduces an open-source approach to building structured, tiered datastores using the Model Context Protocol (MCP).

We will present the concept of an "agent-native data layer" and the "datapack" framework—designed to decouple and layer data specifically for multi-agent environments. Attendees will learn how an open-source MCP server can act as a unified abstraction layer to securely bridge public and proprietary data. You will walk away with actionable design patterns to ensure independent tools seamlessly retain state and context without vendor lock-in.

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Presentation Language: English

Step into the future of enterprise automation by building production-ready agentic AI systems using Java. This hands-on lab guides you through designing scalable multi-agent architectures that seamlessly connect with enterprise infrastructure. You will gain practical experience implementing key design patterns such as sequence, parallel, routing, loop, and supervisor models while enabling direct agent-to-agent (A2A) collaboration. We will also address real-world deployment challenges by integrating human-in-the-loop control points and robust observability features. Leave equipped with functional code and the architectural blueprints needed to bring robust agentic AI into your organization.

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Presentation Language: English