How I Use Claude, Codex, and Gemini to Ship Faster: My Agentic Development Workflow

I build four SaaS platforms and deliver independent projects alongside a full-time job. That only works because I have rebuilt my development workflow around AI tools — not as autocomplete, but as agents that own entire subtasks. Here is how I work.

Context engineering: the skill that makes AI useful

The quality of AI output is directly proportional to the quality of context you feed it. I maintain structured project context — CLAUDE.md files, architecture docs, schema definitions, and convention guides — so that any AI agent (Claude Code, Codex, Gemini) can understand the codebase and produce code that fits. Without this, you get generic suggestions that need heavy editing. With it, you get code that reads like a teammate wrote it.

Spec-driven development

Before touching code, I write a spec: what the feature does, how it integrates, what the acceptance criteria are. Then I hand that spec to an AI agent. The spec is the contract — the AI implements, I review. This inverts the traditional workflow: instead of writing code and reviewing diffs, I write intent and review implementation. The result is faster iteration and clearer thinking about what I actually want to build.

Agentic testing with browser automation

Testing is where agentic AI really shines. I use AI agents to drive browser automation — they navigate the UI, fill forms, verify outputs, and catch regressions that unit tests miss. This is not record-and-replay; the agent understands the application and makes intelligent decisions about what to test. Combined with spec-driven development, the spec defines both what to build and what to verify.

Multi-agent orchestration

• Claude Code CLI for deep codebase work — refactoring, feature implementation, code review
• OpenAI Codex for rapid prototyping and exploring approaches
• Gemini for research, documentation, and cross-referencing
• MCP servers to give agents access to project tools, databases, and external services
• Each agent gets the right context for its task — not a dump of the entire codebase

What changed

I ship more, with fewer bugs, in less time. But the bigger shift is in what I spend my time on: architecture, specs, reviews, and product decisions — not boilerplate. The AI handles the mechanical work; I handle the judgment calls.