AI Orchestration
Directing AI models, agents, and sub-agents through structured workflows — not casual prompting. OpenCode, skills systems, MCPs, and persistent context management work together as a coordinated production system.
Technical Focus
Structured methodology, AI-orchestrated execution
I don't prompt AI and hope for the best. Every project goes through full planning, specification, design, and validation before a single line of code is directed. Then AI executes — under constant quality control and with anti-hallucination systems keeping it on track.
Methodology
Five gates. No shortcuts.
Every project passes through five mandatory gates. Each gate must pass before the next begins. Click any step to see what happens inside.
01
Plan
Define objectives, constraints, dependencies, and risks. Understand the problem before proposing solutions. Identify information gaps and resolve them.
Key outputs
Requirements document Constraint analysis Risk assessment
The methodology behind the work
Every project starts with thorough planning and specification — defining scope, requirements, and constraints before touching architecture. Design and architecture follow. Then validation: does this plan actually hold up? Only after that does execution begin, with AI agents and sub-agents directed through OpenCode, skills systems, and MCPs — with context management and anti-hallucination protocols running throughout.
- Full planning and specification before any execution
- Architecture and design validated before build
- Agents and sub-agents directed through structured orchestration
- Anti-hallucination and quality control at every stage
// Orchestration Engine — Active
Orchestrator
Plan validated — dispatching to sub-agents
Spec & Design Agent
Context loaded — requirements locked
Implementation Agent
Executing within validated spec...
Verification Agent
Security & quality checks — awaiting output
// Anti-hallucination: Active | Context: Managed
Core Disciplines
Stack-Agnostic Delivery
No default stack. For every project, the optimal technology is evaluated and selected based on requirements — prioritizing security, quality, and performance. The stack serves the project, not the other way around.
Product Thinking
Engineering decisions are made in service of the product. Reficera started as a real personal need and was built — spec to production — through the same methodology applied to every project.
Continuous Learning
Self-taught, studying every day. Every architecture decision, every pattern, every tool choice is grounded in real building — not theory. Reficera is the evidence.
Engineering Habits
Plan Before You Build
Execution only happens after planning, specification, design, and validation are complete. Skipping steps creates debt that compounds — and bad AI output starts with bad direction.
Quality Over Speed
Security, correctness, and maintainability come before delivery speed. Verification gates exist at every phase — not as formality, but as real quality checkpoints.
Security First
Security is a first-class concern evaluated at the design stage — not an afterthought. Stack selection, architecture decisions, and code review all run through a security lens.
The proof is in the product
Reficera is live at reficera.com — a real personal finance app with real users, built entirely through AI orchestration. This portfolio was too.
AI Models OpenCode Agents & Sub-agents Skills Systems MCPs Context Management Anti-hallucination Systems
View the Reficera Case Study
Want to build something with AI orchestration?
Open to the right conversations — whether that's a project, a contract, or a role where this methodology matters.