Product Managers in the AI Era: Master Git, Markdown, and CLI to Lead DevAiOps Teams

In the DevAiOps era where AI agents become the default developers, product managers’ value no longer lies solely in “writing specs” but in guiding AI to realize product vision, becoming the orchestrator of human-AI collaboration.

The New Playing Field: From Requirements Definition to AI Orchestration

In recent discussions about DevAiOps, we’ve explored how AI is fundamentally reshaping software development workflows. AI is no longer just an auxiliary tool—it’s the primary executor. From SpecAgent breaking down requirements to CodeAgent writing code and TestAgent validating quality, AI agents actively participate in every phase of development.

This transformation redefines the product manager’s role. PMs are no longer just “requirement providers” but are responsible for ensuring AI understands requirements and implements them correctly. This means PMs must develop the ability to communicate with, coordinate, and even “command” AI teams.

This isn’t some distant future scenario. In fact, mastering three core skills—Git, Markdown, and CLI—will position PMs to thrive in this new paradigm.

1. Git: The Version Control Hub and Collaboration Bridge for Product Specs

In the DevAiOps world, “Spec as Code” isn’t just a metaphor—it’s actual practice. When requirements, user stories, and even prompts become executable digital assets, version control becomes essential infrastructure.

This is where Git takes center stage.

Mastering Git enables PMs to:

  • Track and compare versions: Every requirement evolution and decision change is clearly documented with full historical context
  • Collaborate seamlessly with AI: SpecAgent can read Markdown specs directly from Git repositories while CodeAgent generates code accordingly, keeping AI teams and PMs perfectly synchronized
  • Establish cross-functional consensus: Design, engineering, QA, and PM teams all collaborate within the same Git repository, creating a true “Single Source of Truth” from the ground up
  • Drive automation workflows: Git commits or Pull Requests can trigger automated testing, deployment, and even AI-suggested correction processes

For PMs, Git isn’t a programming tool—it’s the control tower for product specification governance.

2. Markdown: The Universal Language AI Understands and Executes

If Git is the skeleton, then Markdown is the linguistic muscle that AI can comprehend.

As a lightweight markup language, Markdown isn’t just human-readable—it’s exceptionally AI-friendly:

  • Clear formatting with semantic structure: Headers, lists, and code blocks enable AI agents to quickly understand hierarchical structure and context
  • Single source, multiple outputs: A well-crafted Markdown document can be transformed into API documentation, tutorials, frontend interfaces, or voice assistant content
  • Version control compatible: Perfect integration with Git makes spec evolution transparent and trackable with clear change reasoning
  • AI-ready format: AI can directly parse structured Markdown documents into executable instructions, serving as input for automation workflows

For PMs, Markdown is the bridge language that enables AI to precisely understand product intent.

3. CLI: The Command Center for PMs to Direct AI Teams

Future AI agent operations will increasingly trend toward “headless mode,” with graphical interfaces gradually replaced by automated processes.

At this juncture, CLI (Command Line Interface) becomes the primary control interface for PMs to operate AI systems:

devai plan        # Launch SpecAgent to break down requirements
devai review      # Review Pull Requests generated by CodeAgent
devai diagnose    # Analyze error logs and invoke AI for troubleshooting
devai status      # Check overall development status and test coverage
devai deploy      # Trigger automated deployment workflows

Understanding CLI semantics and logic empowers PMs to move beyond technical barriers and actively participate in and guide the entire AI SDLC process.

Real-World Case Study: How PMs Leverage the Three Core Skills

Let’s illustrate how these three skills work in tandem with a concrete scenario:

Scenario: Adding a “Smart Recommendation” feature to the product

  1. Define specs with Markdown: Write structured feature specifications including user stories, acceptance criteria, and API design
  2. Version control with Git: Commit specs to the project repository, create branches for iterative discussions
  3. Launch development with CLI: Ask Gemini or Claude Code to read the spec written in markdown and review it, or just ask AI to follow the spec to do a quick poc.
  4. Continuous tracking and adjustment: Monitor progress with devai status, manage spec changes through Git

Throughout this process, the PM maintains strategic control while AI handles execution details.

The Strategic Advantage: Why These Skills Matter Now

The convergence of these three technologies creates a powerful multiplier effect for PMs:

Git + Markdown = Specification assets that AI can directly consume and iterate on Markdown + CLI = Natural language requirements that trigger automated workflows
Git + CLI = Version-controlled automation that maintains audit trails

This isn’t about becoming a developer—it’s about becoming fluent in the language of AI collaboration.

Conclusion: PMs as AI Collaboration Commanders

Product managers in the DevAiOps era are evolving from traditional “requirement providers” to “AI system orchestrators.” You’re no longer just the planner of “what to build” but the commander-in-chief of “how to make AI build our vision.”

Mastering Git, Markdown, and CLI empowers you to:

  • Communicate product intent precisely, preventing AI misinterpretation and implementation drift
  • Build reusable knowledge assets that make each collaboration more accurate and efficient
  • Maintain strategic control over development processes instead of passively waiting for deliverables
  • Become the nexus of human-AI collaboration, unlocking the unique value of PMs in the AI age

Remember: AI won’t replace product managers, but PMs who don’t learn to collaborate with AI will be surpassed by those who do.

📍 Tomorrow we’ll dive deeper into DevAiOps collaboration mechanisms, exploring how to combine human intuition with AI intelligence to create truly “dual-core driven” agile development processes.