What Our Coding Agents Did to Get Past the Build Checks
A coding agent optimises for the check and skips the goal. Real incidents from our own delivery record show how a green check hid a wrong page.
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Notes from the workbench.
Working notes on shipping AI in production. What breaks between demo and deployment. What to buy instead of build. How we test the parts that matter.
What Moved in the EU AI Act's 2026 Deadline Change
Regulation (EU) 2026/1744 pushed the AI Act's high-risk deadline to December 2027. What moved, what didn't, and why the old schedule still pays off.
Read →Prompt Injection Is a Data Problem: A Threat Model You Can Ship Against
Prompt injectionPrompt injection is untrusted input crafted to override a model's system prompt or task. can't be filtered away: the model can't reliably tell instructions from data. This is the threat model and the controls that hold up.
Read →Why Your AI Agent Works in the Demo and Stalls in Production
An agent that nails the demo stalls in production because reliability compounds across steps. We cover the math, the failure modes, and how to ship anyway.
Read →AI Cost per Outcome in Production, by Workload
A reproducible benchmark of cost per successful outcome across four common AI workloads, with every token assumption, price, and overhead multiplier shown.
Read →Model-Agnostic by Design
Models change under you every few months: price, quality, and capability. So we never hardcode a single provider into a client's feature.
Read →Your RAG Demo Lied to You
Retrieval that looks flawless on ten clean PDFs falls apart on a real corpus. We explain why, and what a retrieval eval requires.
Read →What an AI Feature Costs in Production
Token costs that look trivial in a demo compound fast at scale. Make cost a first-class metric instead of a surprise on the invoice.
Read →Evals Before You Ship: Why AI Features Need Tests Too
Shipping an AI feature without an eval suiteAn eval suite is a versioned set of test cases that measures whether outputs are good. in CI means every prompt tweak is a guess. This piece lists what an eval suite needs to cover.
Read →Build, Buy, or Skip: A Framework for AI Decisions
A technically honest framework for deciding whether an AI initiative should be built in-house, bought off the shelf, or skipped entirely this year.
Read →From Notebook to Production: What Breaks
The gap between a working AI demo and a production feature is auth, latency, cost and fallbacks. We close it without a rewrite.
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