AI Coding Summit Asia 2027

In most organizations, there is a limited number of good code reviewers. This is inconsistent, unscalable, and easy to skip under deadline pressure or when reviewers are unavailable. In this talk, I share the concrete 39-rule review checklist my team built by turning every recurring test smell into an explicit, citable rule, and how we paired it with an AI agent reviewer that runs the full rule matrix on every pull request, consistently.

We run production services with monitoring, SLOs, alerting, and postmortems. We do all that to watch the system, improve it, and act quickly with a short feedback loop. With coding agents, we review the final output and monitor token usage and spending, but rarely measure whether new context, skills, or tools improve the output. We almost never review the process, and how can you improve the process if you don't measure?
This talk makes an attempt at introducing a method for monitoring and improving ADLC, making the process visible by scoring it end to end: intent clarity, context quality, local tool calls, CI runs, PR outcome, and how the change survived production. Each failure signal maps to a treatment (context, skills, tools, guardrails), giving engineers a way to improve ADLC with the same rigor they apply to production systems.

When a human calls an API, identity is well understood, OAuth tokens, JWTs, API keys. When an AI agent calls that same API via MCP, the identity model breaks down. Who is the caller? What should it be allowed to do? How do you revoke access when something goes wrong? As MCP adoption moves from dev laptops into production systems, the absence of a consistent identity and authorization layer for agent-initiated traffic is becoming a critical gap. This session walks through the auth problem from first principles covering OAuth flows for non-human callers, per-agent rate limiting, JWT validation at the MCP proxy layer, and policy enforcement patterns that don't require rewriting your backend services. Attendees will leave with a concrete mental model and implementation patterns for securing MCP tool calls in production.

Modern developers have more tools than ever, but consistency still matters more than motivation. This talk explores “habit hooks”: simple triggers, systems, and environmental cues that make useful developer habits easier to start and repeat. Whether you code traditionally or build through AI-assisted vibe coding, you’ll learn practical ways to turn learning, building, testing, documenting, and shipping into habits that happen almost automatically.

Writing a spec and having a coding agent follow it to generate an implementation is the easy part. But if we still have to read every line of generated code to determine whether it matches the spec, we have realized only part of the benefit of Spec-Driven Development. We may have improved our own clarity by articulating our intent through the spec. But can we trust the implementation to be aligned with it?