AI Lab
Agentic workflows built for delivery, not demos.
This page shows the practical capability stack I am building with Codex: planning, implementation, QA, and production-ready handoff.
How we execute
01Scope and research: clarify business goal, constraints, and evidence.
02Plan architecture: choose stack, components, and implementation phases.
03Build in iterations: ship visible progress with clean code boundaries.
04Verify with tests and browser checks before delivery.
Custom Agents
Task-focused agents with clear responsibilities and quality criteria.
Tools + Skills
Reusable workflows for coding, docs, QA, and release tasks.
MCP Integrations
Context-aware connections to apps, repos, and production systems.
Research Pipelines
Fast multi-source research turned into implementation decisions.
Debug Systems
Systematic diagnosis for runtime, UI, and integration problems.
Deployment Flow
GitHub + Vercel oriented release flow with strong validation.
Quality Gates
Lint, type safety, build checks, and E2E verification.
Collaboration
Subagent coordination with ownership and execution tracking.