Driving AI Coding Agents: From Senior Flutter Dev to AI-Native Architect (2026 Mastery)

How do you get an AI coding agent to write code the way you actually intended?

It starts with the brief: an agent writes what you specify, so you state the architecture, constraints, and acceptance criteria before it touches a keyboard rather than correcting it afterward. On long sessions you also need to steer actively, spotting when the agent drifts from the intended design and pulling it back on track. This course drills that briefing-and-steering discipline directly on Claude Code and Cursor.

Can an AI agent realistically write most of a production Flutter feature?

Yes, reaching roughly 90% agent-generated code is achievable when a developer owns the architecture and lets the agent handle the implementation. The human role shifts to defining structure, reviewing output critically, and running quality gates rather than typing every line. The remaining work is judgment: architecture decisions, code review, and acceptance testing that the agent cannot own.

How do you review code that an AI agent generated?

You review it critically rather than trusting it, checking that it matches the intended architecture, holds up under edge cases, and passes the same standards you would demand of a human contributor. Automated PR-review quality gates catch the repetitive issues so your attention goes to design and correctness. This is treated as a core skill throughout the Quality Gates stage.

What are MCPs and plugins in AI coding workflows?

Plugins from the official marketplace and MCPs (Model Context Protocol integrations) extend what an agent can reach and do, wiring productivity tools and external context into the coding session. Used well, they act as force multipliers that raise throughput without lowering standards. The course covers installing marketplace plugins and wiring up MCPs into a working setup.

How do you split work across multiple AI agents at once?

Subagents let you divide a larger task into parallel streams, each handling a defined slice of the work so delivery throughput goes up. This only works when the overall architecture and acceptance criteria are owned by you, so the pieces integrate cleanly. Coordinating subagents for high-throughput delivery is part of the Force Multipliers material.

How do you run UAT on AI-generated features?

User acceptance testing on AI-native features follows the same rigor as any world-class release: you validate the feature against its stated acceptance criteria before shipping, regardless of who or what wrote the code. Because an agent can produce plausible-looking output that misses intent, disciplined UAT is where you confirm the feature does what was actually asked. The course walks through UAT to 2026 acceptance standards end-to-end.