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Scaling to 120+ AI Agents Without Losing Control
How two-tier orchestration keeps multi-agent systems debuggable
Mar 5
•
Lucian Lature
32
3
5
How to Design Evaluators That Catch What Actually Breaks
The practical guide to code-based checks, LLM judges, and rubrics for real-world AI apps
Mar 3
•
Paolo Perrone
17
5
3
February 2026
Start Here: Your Map to Decoding AI
The AI Engineering Command Center
Feb 28
•
Paul Iusztin
23
8
3
I Spent 9 Months Building an Agentic AI Engineering Course
Google is already recommending it alongside Coursera, DeepLearning.AI and Oxford.
Feb 26
•
Paul Iusztin
20
2
2
Generate Synthetic Datasets for AI Evals
5 strategies from cold start to 450 diverse inputs in minutes
Feb 24
•
Paul Iusztin
24
7
4
Thank You for Supporting Decoding AI
Roadmap updates and the new DAI chat.
Feb 19
•
Paul Iusztin
1
No Evals Dataset? Here's How to Build One from Scratch
Build evaluators to signal problems that users actually care about. Step-by-step guide.
Feb 17
•
Paul Iusztin
23
3
Integrating AI Evals Into Your AI App
The holistic guide: From optimization to production monitoring
Feb 10
•
Paul Iusztin
29
6
4
Behind the Scenes of AI Observability in Production
What actually works after 6 months of trial and error
Feb 3
•
Alejandro Aboy
27
5
6
January 2026
Your Agent's Reasoning Is Fine - Its Memory Isn't
Using GraphRAG to build a Production Engineer agent that knows dependencies, incidents, and ownership.
Jan 20
•
Anca Ioana Muscalagiu
63
5
15
How to Design Python AI Projects That Don't Fall Apart
A framework-agnostic approach to modular agents, workflows, and LLM apps using the pragmatic clean architecture design.
Jan 13
•
Paul Iusztin
60
4
12
The Realistic Guide to Mastering AI Agents in 2026
From zero to production in 6–9 months. What to learn, what to skip, and why most tutorials fail.
Jan 6
•
Paolo Perrone
89
5
12
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