How the Engineering Triad helps developers move beyond vibe coding and build environments where AI agents can safely contribute
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AI agents can write code, run tools and move fast through a codebase. But speed alone does not create reliable software. In real development, generated code is not delivered work. A change only becomes valuable when it survives validation, review, acceptance and delivery.

This talk starts with a simple observation: onboarding a developer into a serious software project takes time. They need domain knowledge, architecture, test strategy, team conventions, delivery flow and context. So why do we expect an AI agent to understand the same environment from one vague prompt?

The real problem is not just that models hallucinate. It is that much of our engineering is implicit. Intent lives in conversations. Rules live in people’s heads. Validation lives in habits. Context is scattered across docs, tickets, APIs, tests and architecture decisions. Agents expose those weak spots quickly.

In this session, we explore the Engineering Triad as a practical mental model for agentic development:

Prompt Engineering makes intent explicit.
Context Engineering makes knowledge explicit.
Harness Engineering makes execution explicit.

Using a concrete password reset example, we will show how a vague request like “add password reset” hides security rules, API contracts, token handling, email infrastructure, validation strategy and human acceptance decisions. We will turn that into explicit artifacts such as a SPEC.md, BDD-style scenarios, AGENTS.md rules, scoped custom agents, reusable skills and validation gates.

The key message: agentic development is not about replacing engineering discipline. It makes engineering discipline more important. A good environment for agents is also a good environment for developers.

Target audience

This talk is for software developers, tech leads and engineering-minded architects who have already experimented with tools like ChatGPT, Copilot, Cursor, Claude Code, Codex or similar coding agents.

This is not an introduction to LLMs. It is for people who have felt the magic of AI-assisted coding and now want to make it reliable enough for real software delivery.