AI Agents in Production 2026: My CAPCOM/MCC Architecture
How to architect a multi-agent system that maintains deterministic state, prevents context drift, and operates reliably in production across 4 tech brands.
I do this in production across 4 tech brands, not as a textbook consultant. Practical code, database schemas, and autonomous AI agent architectures without marketing fluff or vendor lock-in.

Engines and cloud platforms where I design architecture, migrate data, and optimize queries with zero performance compromises.
“I don't write articles about what is ‘worth considering’. I write about what I deployed on Tuesday in DBAdmin, what schema I stress-tested in LabAI, and how many tokens I saved in the CAPCOM architecture.”
Everything I recommend is battle-tested in production across my own companies first.
Production-grade autonomous AI agents, CAPCOM multi-agent systems, and business process automation for engineering teams.
Cognitive performance, deep work operating frameworks, and hyper-focused routines for technical founders and leaders.
Modern web architecture, headless CMS engineering, Astro, and edge performance optimization.
Enterprise PostgreSQL & Oracle database administration, high-availability clusters, query tuning, and zero-downtime migrations.
Real case studies: PostgreSQL, pgvector, autonomous agents, and high-performance web.
How to architect a multi-agent system that maintains deterministic state, prevents context drift, and operates reliably in production across 4 tech brands.
An architectural comparison between Astro and Next.js for content platforms, headless CMS integration, and eliminating JavaScript bundle bloat.
Facing an architectural challenge with PostgreSQL/Oracle databases, multi-agent AI integration, or modern edge web? Drop me a message — your inquiry goes straight to my Chatwoot system.