AGNTCon + MCPCon Japan 2026

AI coding agents are useful in isolated demos; the hard problem is making them controlled enough to ship production code. At Delphi, we built an agentic software factory that turns product specs and engineering tickets into scoped pull requests, then routes those PRs through reviewer agents, CI, browser verification, and merge gates.

This session breaks down the operating system around the agents: how we package repo context, encode architectural rules, isolate attempts in worktrees, make agents cite source-of-truth patterns, prevent drift from legacy code, and decide what should stay human-controlled. I will also cover failure modes we hit in production: stale context, over-broad diffs, flaky tests, review spam, false confidence, and agents copying the wrong pattern.

Attendees will leave with a practical blueprint for moving from “agent writes code” to a controlled delivery pipeline for agent-generated software

We see how quickly AI coding tools and agent harnesses are improving. But how can the surrounding system keep that autonomy governable once an agent starts planning, executing tools, changing files, and consuming budget on a team’s behalf?

In this talk, I break down a technical case study based on a real AI coding control-plane architecture and show how serious systems structure autonomy through explicit boundaries: plan gates, permission controls, trust review, independent verification, and runtime observability. I will walk through the patterns and production-grade examples, explain why telemetry and quota tracing are integral to code governance, and show why integrity failures can still happen even with strong coding workflows.

This session gives engineering leaders and practitioners a framework for evaluating AI coding tools. The goal is to achieve agent governance that teams can trust, audit, and scale.