AGNTCon + MCPCon Japan 2026

In the agentic AI space, standards like MCP and A2A and a growing OSS ecosystem are evolving. Yet building a real multi-agent system requires more than a single framework or protocol. It involves integrating multiple OSS components and adjacent specifications across areas such as agent-to-agent communication, orchestration, authentication, and observability. There is limited shared understanding of how to combine them and what architectural considerations and integration gaps emerge.

This session shares our work on organizing the landscape of OSS-based agentic AI stacks, and on designing architectures built around MCP, A2A, and representative OSS, including prototyping through reference implementations. We explore several architectural patterns from a whole-system perspective. With enterprise use in mind, our scope also covers cross-cutting concerns such as authentication and observability. (LLMs are currently proprietary.)

Through this work, we examine what each OSS layer solves and does not solve, and highlight cross-layer concerns that emerge only when components are integrated as a multi-agent system. We share these observations with a demo of the reference implementation.

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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.

AI coding assistants are becoming part of everyday development, but many enterprises cannot send source code, internal APIs, or project context to external services. They need the productivity of coding agents while keeping models, repositories, prompts, and actions under their control.

In this session, we’ll show how to build an open-source, Claude Code-like assistant using local or privately hosted LLMs, Models as a Service, OpenCode, and AI guardrails. Developers will be able to explore a codebase, generate changes, review explanations, and trigger controlled actions without exposing sensitive data.

We’ll also cover the guardrails needed for real use: secrets protection, data leakage checks, unsafe command blocking, insecure code detection, and policy-based action control.

You’ll leave with a practical blueprint for building a private, secure coding assistant that improves developer productivity without compromising trust, privacy, or compliance.

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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.

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.