Describe
Send the current steps, users, inputs, exceptions and desired outcome.
Applied AI · engineering notes
Build smarter.
Automate the repetitive.
Field-tested patterns for developers and operators building AI systems that work beyond the demo.
INPUT / TASK
</>Repository
◎Agent core
⋮Retrieval
⌘Tools
◉Human gate
✓Verified
OBSERVE / EVALUATE / APPROVE
Research / Build / VerifyScroll to inspect ↓
01 / The operating principle
Less hype.
More evidence.
GAGCoders is an applied-AI engineering publication. Every serious guide starts with a real task, names the environment and cost, records human intervention, and shows where the system fails.
Read our editorial standard02 / Knowledge map
Six systems.
One practical lens.
From a coding prompt to an auditable operations workflow, follow the whole system—not just the model.
01
AI coding
Real repositories, review loops, testing discipline and safer agent workflows.
Explore system 02Agentic AI
Architecture, permissions, evaluations and the human checkpoints production needs.
Explore system 03RAG systems
Ingestion, retrieval, grounding and the quality–cost decisions behind useful knowledge tools.
Explore system 04MCP
Practical integrations with explicit trust boundaries, authentication and tool safety.
Explore system 05Automation
n8n, Make and Zapier workflows designed for inspection—not invisible magic.
Explore system 06Manufacturing
Back-office and knowledge workflows for SMEs, with engineering review where it matters.
Explore system03 / Latest dispatches
Notes from
the workbench.
01
EDITORIAL FRAMEWORK
02How we will test AI coding agents on a real maintenance task
Planned test · Owner verification required
SYSTEMS NOTE
03Designing a RAG pipeline that can explain what it knows
Architecture preview · 8 min read
FIELD GUIDE
Where human approval belongs in a business automation
Editorial guide · 11 min read
04 / Interactive labs
Make the
trade-offs visible.
Explore deterministic tools for comparing platforms, planning agent permissions, sketching RAG architecture and estimating workflow cost. No pretend AI.
Open the labs05 / Prototype desk
Bring one repetitive
workflow.
Start with a bounded problem, not a vague transformation programme. We will manually review whether it is suitable for a responsible prototype.
Assess
We identify feasibility, data sensitivity, risks, human gates and suitable tools.
Prototype
If the fit is right, agree a small working proof with tests, costs and clear handover.
Useful starting points: document routing, reporting, internal knowledge search, AI coding workflows and back-office automation.
Discuss a prototype