ABOUT
Bridging Industrial Automation with Agentic AI
Jaime Calvente Mieres
Industrial Automation Engineer specializing in scalable PLC architectures for complex manufacturing systems.
I design control systems using TwinCAT, TIA Portal, and CODESYS, with a strong focus on robust software design (OOP, SOLID) applied to industrial control. Experienced in robotics integration and real-time communication protocols including OPC UA, MQTT, and EtherCAT.
Currently focused on agentic AI patterns for engineering workflows — exploring how AI can support analysis, diagnostics, and reasoning in industrial environments safely, read-only, and with clear boundaries.
"Always learning. Always building."
THE MISSION
AgenticControl.dev bridges the gap between traditional industrial automation and modern agentic AI patterns.
This platform teaches automation engineers — people who already understand PLCs, SCADA, and industrial protocols — how to integrate AI agents into their workflows safely and responsibly.
Core Philosophy: Before agents can safely act, they must first learn to safely think.
We start with advisory-only patterns (Technician Track), progress to multi-agent coordination (Developer Track), then move to production deployment (System Track), and finally explore distributed architectures (Architect Track).
Every tutorial includes working code, real industrial scenarios, and honest assessments of what works, what doesn't, and what it costs.
WHY THIS MATTERS
🎯 For Automation Engineers
You already know how to control machines. Now learn how to integrate AI into your workflows without compromising safety, determinism, or reliability.
🔧 Real Industrial Context
No abstract AI theory. Every tutorial uses actual PLC code (IEC 61131-3 ST), alarm logs, and scenarios you'll recognize from the factory floor.
⚡ Code-First Learning
All tutorials include runnable Python code, cost estimates, and time requirements. You'll build working implementations, not just read about concepts.
🚧 Safety First
We start with read-only, advisory-only patterns. Autonomous action comes only after mastering validation, monitoring, and rollback strategies.
WHAT YOU'LL FIND HERE
From foundations to distributed systems, organized into 4 learning tracks
Canonical agent interaction patterns with failure modes, observability hooks, and implementation checklists
IEC 61131-3 ST examples, alarm log analysis, documentation extraction, and more
No hype. Clear boundaries. Realistic cost estimates. What works, what doesn't, and why
GET IN TOUCH
Questions about a tutorial? Found an issue? Want to suggest a topic? Reach out — I read everything.