Seven agents run the operating work of my GTM job: reporting, chief-of-staff tasks, marketing and social-selling content, and the internal tooling (wikis, dashboards, the "revenue brain" I operate from) that Emma, my principal engineer agent, builds and maintains under my direction. When something breaks, diagnosing the real problem and directing the fix is my job. Writing the code is hers.
Seven agents can look empty on their own. What sets the work apart is the discipline around it: CI gates that enforce cost and model-routing policy, a security review that caught and fixed a real injection bug, incident postmortems (including a cron pipeline that had gone silently dead), and a record of rejecting work that wasn't good enough, including my own AI's.
Built by Emma, my principal engineer agent, under my direction: I made the architecture calls, she wrote the code. That's what directing engineering looks like as an operator, not a coder. I run my quarterly goals off it, and twice a week it drafts the standup report to leadership. It replaced an unreliable tool I couldn't fix. I chose to replace it rather than patch it.
Seven agents, each its own cloud service with its own repo and CI, later migrated into a monorepo with a documented rollback plan and a health-gated cutover. This is the shape of the actual job.
Turborepo and Bun workspaces, TypeScript 7 RC on the native Go compiler, and Effect Schema enforced at every trust boundary: LLM output, external APIs, HTTP bodies, and the shared memory engine's write path. Oxlint runs correctness-only and blocks CI; Oxfmt handles formatting locally, deliberately not a gate. A supply-chain control sets a seven-day minimum publish age on new dependencies, so a same-day-published, potentially compromised package can't be pulled in by accident.
The shared memory engine every agent builds on is public. Real git history, 75 tests, and it never touched business data to begin with, so there was nothing to redact: severs-agent-shared on GitHub. The rest of the fleet, including the Rocks dashboard and each agent's business logic, stays private.
The fleet shows I can direct engineering, and direction is the real skill. I don't write the code myself, my agents do. What I do is diagnose the actual problem, give clear direction, and hold the line on quality until the fix is real, not just claimed. That's what being AI-forward means: not writing the code, knowing exactly what needs fixing and directing it correctly.
This is the beginning, not a finished portfolio piece: from ad hoc scripts to a documented, cost-governed, security-reviewed system with a real rollback plan, built while running go-to-market full time, not instead of it. There's a lot more to learn and build from here.