Query decomposition doesn’t fix context starvation — it just moves it
I have a small example that would best communicate the message I am trying to convey: say you built a The post Query decomposition doesn’t fix…
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Why human oversight is shifting from writing code to defining requirements
This walks through the pipeline our agents operate inside—from a recorded scoping meeting through unit specs, spec review, generated code, The…
Stop AI code sprawl before it destroys your software design
While AI code generators help teams ship faster than ever, that speed brings a hidden killer: Comprehension Debt. As soon The post Stop AI code spra…
The systems guide to production token optimization
When enterprise AI applications scale, they inevitably hit a wall. For many engineering teams this wall is initially diagnosed as The post The syste…
Why basic RAG fails at multi-hop reasoning (and how GraphRAG fixes it)
The current approach to designing LLMs within AI engineering is oversimplified. According to the echo chamber’s view, solving LLM hallucinatio…
Stop the token bleed: building token-efficient multi-agent systems
Every engineering team deploying AI agents eventually discovers an uncomfortable truth: the model isn’t the biggest expense. The hidden cost T…
Can prompt caching tame RAG costs without sacrificing accuracy?
The AI ecosystem is drowning in tutorials on how to build a retrieval-augmented generation (RAG) app in five minutes. The The post Can prompt cachin…
Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
This article explains why traditional CI/CD gates are not enough for production AI systems. I share a practical release-gating approach The post Why…
Beyond the stack trace: why AI requires a new debugging paradigm
For some time, debugging has relied on the assumption that software is deterministic. It’s expected that with the same input, The post Beyond…
From Jupyter Notebook to production: How to ship AI systems that actually work
Moving from experimentation to production in AI requires a transformation of mindset, architecture, and engineering discipline. There’s no API…
Debugging the undebuggable: building observability into probabilistic AI systems
Debugging used to be straightforward: A service failed, you checked the logs, followed the stack trace, and fixed the bug. The post Debugging the un…
How to build your first end-to-end AI workflow in n8n
n8n is one of the most powerful workflow automation platforms available today, and the fastest way to learn it is The post How to build your first e…
How AI-native systems are built
The core problem: the “brittle” enterprise Traditional enterprise software is deterministic; composed of a series of “If-Then” statements that break…
A nine-point checklist for shipping production-ready AI
Starting from square one Most teams can build an AI prototype. A notebook answers a few prompts, a demo agent The post A nine-point checklist for sh…