Building an AI Colleague

What You'll Learn

• ›What an AI colleague is, and where an autonomous agent beats AI assistance inside the editor
• ›How to design an agent that picks up a task, plans it, implements it, and opens a reviewable pull request
• ›How to give the agent the context it needs: repository conventions, coding standards, and guardrails
• ›How to connect the agent to real sources of work, such as code quality findings and the issue tracker
• ›How to keep humans in control: review gates, permission boundaries, and limits on what the agent may change
• ›How to tell when an AI colleague is doing good work — and when to stop it

How It Works

Participants build a working AI colleague during the day and let it loose on a real codebase. The first job we give it is paying down real technical debt: the agent picks up findings from static analysis, fixes them one at a time, and opens pull requests that a human reviews and merges.

• ›From AI assistant to AI colleague: what changes when the agent works on its own
• ›Building the agent: task intake, planning, implementation, and pull request
• ›Guardrails, permissions, and review gates
• ›Putting the AI colleague to work on technical debt
• ›Running it unattended: scheduling, retries, and failure handling
• ›Measuring the AI colleague's output and quality

Outcome

By the end of the training, participants will: Understand which development work can be delegated to an autonomous AI colleague, Have built a working agent that completes development tasks end to end, Know how to set the guardrails and review gates that keep its output safe, Be ready to introduce an AI colleague into their own team's workflow