Agents do not need more intelligence, they need narrower mandates
Agents stall when their authority is undefined. Narrow mandates make scope, ownership, audit and reversal clear.
ReadInsights
Practical notes on AI-first engineering, software architecture, security and delivery, written for the people who have to decide what gets built and who pays for it when it goes wrong.
Agents stall when their authority is undefined. Narrow mandates make scope, ownership, audit and reversal clear.
ReadConstruction software fails when it assumes a signal. On site, the device must record work locally, sync later and keep evidence intact.
ReadEducation software fails when it is designed on fast office fibre but used by whole classes on shared school connections and older devices.
ReadProduction LLM failures usually come from the system around the model: truncation, retries, schema drift, monitoring gaps and weak evaluation.
ReadGeneral AI assistants shift responsibility to buyers when outputs are wrong, so production use needs a defined result and remedy.
ReadPeak capacity has to be agreed, tested to failure, and designed around before the campaign, because the busiest minute breaks systems.
ReadPayment systems earn trust when they can prove the ledger matches the outside world, especially when retries, timing gaps and partial batches hit.
ReadA defined AI job needs a written input, output, evaluation, confidence rule and remedy before anyone pays.
ReadSlow clinical software pushes staff into workarounds, breaking live records, timestamps and audit trails when safety questions arise.
ReadAI buyers need finished work with a price, deadline, quality measure and recourse, rather than open-ended chat access.
ReadNext step
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