The Rule of Two for AI Agents in CI/CD
AI agents in GitHub Actions and delivery pipelines need clear boundaries. Teams should avoid combining untrusted input, secrets, and external communication in one workflow.
AI agents in GitHub Actions and delivery pipelines need clear boundaries. Teams should avoid combining untrusted input, secrets, and external communication in one workflow.
AI coding agents can speed up delivery, but only if teams can see what they cost, where they help, and where they quietly create rework.
AI coding agents can help serious teams move faster, but only when the existing system is legible enough for them to work safely. For many products, the first AI productivity project is making the codebase understandable.
Sandboxes, permissions, and allowlists matter. But serious teams also need to decide how far an AI coding agent mistake can spread.
Coding agents can create more implementation throughput. The serious question for product teams is whether planning, review, integration, and architecture can keep up.
Claude Code, Cursor, MCP tools, and similar agents are becoming more powerful. For serious product teams, the key question is no longer speed. It is control.
Hybrid and on-prem AI coding agents may unlock adoption in regulated companies. They still need architecture, boundaries, review and operational design.
MCP makes AI coding agents more useful by connecting them to tools, data and workflows. It also means teams need production-style security boundaries around those connections.
AI coding agents can accelerate schema work, migrations and backend changes. They should not be allowed near production data without clear operating rules.