Blog
Short notes on what I deal with at work: AI, architecture, agents, adoption into real processes.
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Agent Capability Supply Chain
Agentic systems gain a new architectural layer: capability becomes a versioned, verifiable, governed artifact — almost like a software component.
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Agent systems are moving from loop-centric to contract-governed architecture
The first AI agents were built around a simple loop. Once an agent runs long, changes system state and spends resources, a managed architecture grows around the model: intent, capabilities, policy, evidence, validation.
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It's not the model that decides, but the environment around it
A year ago, an agent was assembled from a model and a list of tools. Now a separate layer has grown up around the model, and it is this layer that determines whether the system works or falls apart on the third step.
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AI in business processes: where it pays, where it's hype
Documents, support, monitoring: AI pays back fast there. Full autonomy without control: that is where the losses begin.
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Skills, agents and the future of engineering
Expertise gets packaged into skills, routine goes to agents. What is left for the engineer, and what is worth learning right now.
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ADR and SDD: documents that don't gather dust
Short decision records and specs before code. Why they matter even more in the age of agents, and why fat specifications are dead.
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Where AI actually helps in medicine
No fantasies about replacing doctors: routine work, transcription, a second pair of eyes on scans, and patient follow-up. And why responsibility stays with the human.
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AI on the factory floor: don't start with the model
How to bring AI into production cycles without pain: process and data first, a pilot on a boring bottleneck, and only then model debates.
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One agent is fine, an orchestra is better
Why a zoo of ten agents never works, and what actually fixes it: an orchestrator, context boundaries and quality control.