Agent-Driven Development

What's included

• Agent-driven engineering on your project: features shipped faster without lowering the review bar
• Team adoption: workflows and guardrails for Claude Code, Codex, and Cursor, tuned to your codebase
• Review process design for a world where generation is fast and merging is the bottleneck
• Internal tooling: custom agents, hooks, and automation for your team's specific workflow

How we approach the work

1. 01

   Find the review bottleneck

   Look at how work moves from task to merged change today: where context is lost, where generated code needs the most scrutiny, and which checks already catch regressions.

2. 02

   Give agents bounded work

   Define tasks with clear inputs, files, constraints, and a completion check. Keep architecture and acceptance decisions with experienced engineers while agents handle the repeatable implementation work.

3. 03

   Make the workflow repeatable

   Turn the useful patterns into repository instructions, review gates, and tool permissions that the team can maintain. The goal is a better delivery process, not a one-off prompt trick.

Who this is for

• Engineering teams that want to adopt AI coding tools without a quality regression
• Companies that want Omnihash shipping inside their existing codebase and process
• CTOs evaluating how much of the roadmap agent-driven development can actually absorb

Related work

• A production AI agent platform, zero to live in a month — A custom, production-grade AI agent platform built from scratch and shipped to production in one month — after a previous team spent 8+ months and shipped nothing.

Related engineering notes

• AI Pilled: A Case Study — How I built Cold Reach, a procedural sci-fi dungeon crawler, with semi-autonomous AI agents, and why experienced engineers remain essential.
• Agent-driven development: how we actually use AI coding tools — Agent-driven development multiplies what a senior engineer can ship without giving up code quality. How we use Claude Code, Codex, and Cursor in practice, and how to adopt it on a team without creating technical debt.
• Agent orchestration and delegation: when one AI agent should become many — Agent orchestration is powerful and overused. When to split one AI agent into an orchestrator and specialized workers, and when not to.
• The true cost of an employee now includes their token spend — In the AI era the cost of a hire is salary plus benefits plus token spend. Why a cheap, wasteful hire can cost more per unit of output than an expensive one.