20 min watch

Treat Your AI Agent Like a New Engineer

  • codebase-readiness
  • agents-md
  • codex
  • rails
  • context-engineering

Most AI coding agent failures are not prompt problems. They are onboarding problems.

I walk through how I set up a real Rails repo so a coding agent has the context it needs before it starts changing code. Using CreatorSignal as the example, I show the repo artifacts that make Codex more reliable, then use a small API key debounce bug to show how much better the work gets when the codebase tells the agent what matters.

Article

Related article

Four Dimensions of Agent-Ready Codebase Design

AI agents produce better output when the codebase is ready for them. Here are the four dimensions of codebase readiness that account for most of the gap.

Read the article

Work with me

Need senior technical guidance?

As a fractional CTO, I help teams make confident architecture and AI integration decisions.

View services

More on this topic

Video

My Agentic Coding Workflow on a Real Feature, Start to Finish

I take a real feature from spec to merged PRs with Claude Code, Codex, and Pi: shaping, stacked PRs, a local model for slices, and review.

agentic-codingai-workflowstacked-prs
Watch the video
Video
9 min watch

WebMCP: How to Make Your Site Agent-Ready Without the Middleware

WebMCP lets a website give browser agents typed tools instead of forcing them to reverse-engineer the DOM. No separate MCP server required.

webmcpmcpai-agents
Watch the video
Video
9 min watch

Rails Gives AI Agents What They Actually Need

Rails gives AI agents what most stacks don't: conventions, strong defaults, and less custom infrastructure to verify.

railsagent-readyai-agents
Watch the video
Video
5 min watch

The Engineering Headcount Math Just Broke

AI coding agents broke the old headcount-equals-output math. Small senior teams now outship larger orgs, and verification is the new bottleneck.

production-aiai-agentsteam-structure
Watch the video