AI Engineering in 2025 requires more than prompting or code generation—it requires a repeatable, spec-driven system that aligns humans and AI agents on what to build and why before any code is written.
GitHub’s Spec Kit provides a lightweight, practical foundation for this: a standardized workflow that uses structured specifications to guide AI agents, reduce rework, and eliminate “vibe coding.”
This bootcamp framework extends that foundation into a three-phase operating model, helping future AI architecture & engineering leaders create teams where humans and AI work together effectively, predictably, and safely across a GitHub Organization.
Establish GitHub as the center of collaboration, and make high-quality specs the primary artifact that aligns all contributors—human and AI.
- GitHub Org setup: teams, permissions, roles
- Repository structure aligned to value streams
- Standardized templates for specs, issues, PRs, and documentation
- GitHub Projects functioning as planning & alignment boards
- Spec Kit as the intake engine for new features and product ideas
- Clear mapping: Specs → Work Items → Code Tasks
- Reduces ambiguity and re-work through specification-first alignment
- Establishes clarity for AI agents (they perform best with structured inputs)
- Creates shared language for teams, improving onboarding and knowledge transfer
- Provides a repeatable, low-ceremony mechanism for planning features
Define how work flows through the system—using Spec Kit artifacts to drive agentic execution while maintaining human oversight and architectural intent.
- Role taxonomy for human + AI contributors
- Routing matrix: which tasks are automated vs. reviewed vs. human-only
- Plan Mode and branch isolation for safe experimentation
- GitHub MCP Registry to activate specialized AI tools
- Spec Kit tasks → agent implementation → test generation → PR creation
- Automated PR gates (security, drift detection, correctness validation)
- Dramatically increases velocity without sacrificing safety
- Turns AI agents into predictable executors, guided by specs—not ad-hoc prompting
- Ensures code changes remain aligned to user stories and architectural goals
- Enables repeatable workflows across every repo in the org
Establish the pipelines, packaging standards, and feedback loops that make engineering scalable, auditable, and continuously improving.
- Artifact distribution via GitHub Packages (e.g.,
ghcr.io, npm, PyPI) - MCP Registry updates to expose internal agent tools to the whole org
- Automated evaluation: correctness, cost, performance, RAG quality (if applicable)
- BMAD-style feedback loops integrated into GitHub Projects
- Reporting dashboards for transparency and iteration planning
- Creates a secure and repeatable release process
- Ensures measurable quality and performance improvements each cycle
- Enables AI agents to tune, refactor, and optimize systems continuously
- Provides enterprise-ready governance with minimal overhead
- Specs, not vibes, drive development and agent behavior
- Humans focus on intent, architecture, and decisions
- AI agents execute structured tasks at scale
- Knowledge lives in GitHub—not lost in prompts or chats
- Velocity increases without creating chaos or technical debt
- Accelerates readiness to lead AI-augmented engineering teams
- Builds real expertise with GitHub-native agentic workflows
- Teaches disciplined thinking around specs, governance, and architecture
- Demonstrates how to operationalize AI in complex organizations
- Provides templates that translate directly to industry practice