The demo worked.
Production didn't.

Practical engineering notes on building production-grade AI systems — written for engineers, not for the hype cycle.

Three series, one discipline: pattern over product.
The series

AI Agents in Practice

Publishing now
8 parts · 5 published

Building the loop that survives production: observe → decide → act → check → repeat, the patterns that extend it, and the boundaries that keep an agent inside its lane.

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  1. The Demo Worked. Production Didn't.
  2. What Makes Something an Agent
  3. How the Control Loop Actually Works
  4. Five Agent Patterns and the Control Surfaces That Make Them Safe
  5. Workflow, Agent, or Single LLM Call — How to Decide
  6. Building the Production Agent Loop coming soon
  7. Two Places Failures Show Up coming soon
  8. The Boundaries That Keep Agents Safe coming soon
Read the full series on dev.to ↗

RAG in Practice

Complete
8 parts

Retrieval past the toy example: chunking that holds up, retrieval that returns the right thing, and the failure modes that only show up once real documents are involved.

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  1. Why AI Gets Things Wrong
  2. What RAG Is and Why It Works
  3. How RAG Works — The Complete Pipeline
  4. Chunking, Retrieval, and the Decisions That Break RAG
  5. Build a RAG System in Practice
  6. RAG, Fine-Tuning, or Long Context?
  7. Your RAG System Is Wrong. Here's How to Find Out Why.
  8. RAG in Production — What Breaks After Launch
Read the full series on dev.to ↗

MCP in Practice

Complete
9 parts

How tools actually connect to models: where the seams are, how authority and scope are decided, and what it takes to wire it up without surprises.

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  1. Why Connecting AI to Real Systems Is Still Hard
  2. What MCP Is and How AI Agents Connect
  3. How MCP Works — The Complete Request Flow
  4. MCP vs Everything Else
  5. Build Your First MCP Server (and Client)
  6. Your MCP Server Worked Locally. What Changes in Production?
  7. MCP Transport and Auth in Practice
  8. Your MCP Server Is Authenticated. It Is Not Safe Yet.
  9. From Concepts to a Hands-On Example
Read the full series on dev.to ↗
Where to start

Not sure where to begin?

New here New to building AI systems? Start with MCP or RAG — they cover the foundations everything else builds on.
Architecture Interested in how agents are put together? Start with AI Agents from Part 1.
Production After production lessons specifically? Jump to the latest AI Agents article.
Across the series, TechNova is used as a fictional company so the examples stay concrete and the trade-offs stay honest.