AGNTCon + MCPCon Japan 2026

As AI agents move from answering questions to taking actions across enterprise systems, reliability cannot be achieved through model quality alone. A practical production architecture separates non-deterministic reasoning from deterministic execution.

This session explores how to define that boundary with MCP-enabled tools. Agents can interpret ambiguous requests, gather context, and choose among bounded options. Deterministic workflows, services, and policy checks can then perform repeatable operations such as validation, approvals, system updates, notifications, audit logging, and recovery.

Using common enterprise action patterns, we will examine how this division of responsibility improves output quality while reducing unnecessary reasoning, latency, and token cost. We will also discuss how to design action interfaces with clear intent, constrained outcomes, and recoverable execution.

Rather than treating agents and workflows as competing approaches, this session presents them as complementary parts of a single execution architecture. Attendees will leave with a practical framework and checklist for designing reliable, cost-aware MCP capabilities across open-source agent frameworks and enterprise environments.

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Presentation Language: Japanese

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

The AI industry has moved so fast that we are still evaluating probabilistic software using the same deterministic metrics we applied to traditional code. As a Product Analyst working on AI agents at PagerDuty, I saw a critical need for a new observability standard, one that moves beyond clicks to measure true reasoning and reliability. To address this, I’ve developed and open-sourced a specialized framework designed to help teams decide, with data, when to hire, train, or fire an AI agent. In this session, I will walk through the H.I.R.E. Framework methodology and share the technical architecture of an evaluation pipeline that turns qualitative conversational data into structured, actionable product insights. I will share the open-source repository containing these metric definitions and templates, providing resources for the community to move past agent-washing and toward building verifiable, trustworthy agentic 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.

Most "human-in-the-loop" agent systems assume the human can specify success up front: a passing test, green CI, a structured eval. What if the human's taste is the spec?

This talk presents YouAndOrchestra (YaO), an open-source agentic music composition system on Claude Code. YaO orchestrates seven role-based subagents — Producer, Composer, Harmony Theorist, Rhythm Architect, Orchestrator, Mix Engineer, Adversarial Critic — turning natural language into a score evaluated across six dimensions, every note carrying provenance.

https://github.com/shibuiwilliam/YouAndOrchestra

Three collaboration patterns from iteration:
1. Three-tier feedback. Users rewrite the YAML spec, regenerate a section, or pin feedback to a bar, beat, and instrument. Choosing the level is itself a UX problem.
2. Conductor loop with a critic gate. Generate, evaluate, adapt, regenerate runs up to three iterations, with critics gating before notes are placed.
3. Provenance as trust substrate. /explain queries an append-only causal graph — "why did the chorus modulate to the relative minor?"

Attendees leave with patterns and open-source code for agent systems where the human stays in the seat of judgment.

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Presentation Language: Japanese

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

Open protocols like MCP and A2A let you combine agents and tools from different teams into one system.

But at company-wide scale, new challenges arise: who can access the system, who can call which agent, whether it fails safely, and whether it follows internal governance. Coding agents now write most of the code, so the spec we give them shapes the whole system's quality.

We'll share how we tackled this from two sides on a company-wide MCP × A2A platform.

Architecture side: where to declare per-agent access, how to handle attribute-based authorization, how to split the write and read sides of authorization data, and how to stay fail-closed, mapped to standard authorization-control models.

Development-process side: a multi-stage review of the requirements given to coding agents, covering format, clarity, cross-walk with external standards, and adversarial testing. This catches auth and governance issues, plus input validation flaws and data leakage, before code is written.

You'll come away with ideas for an interoperable MCP/A2A authorization layer, a reusable way to gather enterprise-grade requirements, and a view of specs themselves as security assets.

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Presentation Language: Japanese

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

Coding-agent harnesses such as Claude Code, Codex, and OpenCode are widely used as general-purpose agent platforms, customized with Skills and Tools and run inside sandboxes. But these agents are stateless by design: when a session ends or a sandbox is torn down, context and artifacts disappear. Running them in production for many users over months turns memory and artifact management into hard problems.

This session shares how we run multiple harness-based agents in sandboxes, and introduces two open-source tools we built to externalize agent state. mem9 (github.com/mem9-ai/mem9) records sessions through harness hooks and, at the next session, runs hybrid retrieval over past sessions from the user's instruction, injecting only relevant history—persisting sessions while compressing context and cutting token usage. drive9 (github.com/mem9-ai/drive9) is a cloud filesystem FUSE-mounted into each sandbox, persisting artifacts beyond sandbox lifetimes, auto-attaching metadata, and enabling semantic search over outputs.

Attendees will leave with a vendor-neutral, open-source architecture for agent memory and storage that works across any harness, plus lessons from real operation.

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Presentation Language: Japanese

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