Keeping Humans In The Loop With AI Coding Agents
Vibe coding can significantly cut time to a prototype, but it can also create an unmaintainable mess. Gojko presents a practical performance path for adopting AI agents in a controlled, deliberate way, with concrete steps at every stage
Key Takeaways
- Identify your current AI adoption stage
- Apply practical improvement at your level
- Design feedback loops that keep humans in control
Who Is This For?
- Developers and team leads experimenting with AI coding agents
- Teams looking to adopt AI in a structured, controlled way
Level
All levels
What This Session Covers
- A performance path map for AI coding agents
- Research from early adopters
- Concrete tips at each maturity level
- Team process integration
What It’s Not
- Not a motivational AI talk
- Not about deep technical implementation
- Not about replacing engineers
Full Description
AI coding agents are becoming increasingly popular. They can dramatically reduce the time to a working prototype.But without structure, speed quickly turns into long-term maintenance risk. Based on industry research with early adopters, this talk introduces a performance path for AI coding agents. You’ll see the different stages AI adopters move through, what benefits each stage unlocks, and the specific practices that help you and your team progress. Expect to learn how to add guardrails and constraints to automated AI coding agents so they can produce code as good as humans, but significantly faster. You’ll explore patterns and practices for keeping humans in the loop, making key decisions deliberately, and creating effective feedback cycles that keep AI agents on the right path.
