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317 posts · showing 141–150
2026
10 posts-
What Distributed-Systems Experience Transfers to Agent Loops
An agent run is a distributed system with unreliable participants, so most of the discipline transfers. Four things twist — and one microservice instinct actively hurts, because agent boundaries are lossy.
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Prompt Engineering Was the Beginning, Not the Skill
It got named as a discipline when the prompt was the entire interface. It's now one input among several — and if better prompts have stopped helping, five specific things are usually why.
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The Loop Decisions You Only Make Once
Most agent design choices are cheap to revise. Six aren't — and in every case the answer that ages well is the same one, for the same reason.
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Context Engineering Is the Job Now
Not prompt writing — that's the visible part and the small one. The work is deciding what reaches the model, in what form, at what moment, and it's where most agent failures start.
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Lock-In Risk in Agent Tooling, and How to Keep Optionality
The API call is trivially portable — the coupling forms in four other places, and the worst one isn't technical at all. One question tells you how exposed you are.
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The Moat Isn't the Model — It's the Loop Around It
"The model isn't the moat" is now widely said and stops one step short. What compounds shares a property — it accumulates from watching real usage, which is why it's invisible from outside.
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Will Open-Source Harnesses Eat Commercial Coding Tools?
The premise is right — the loop is a few hundred lines. The conclusion is too fast, because the loop isn't the product, and one of the real advantages is invisible from outside.
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What a Harness Actually Is, and Why It's the Real Product
A model takes messages and returns tool calls. Everything else is the harness — and it explains almost all the variance between agent systems that share the same model.
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IDE Assistants vs Agent Harnesses: Different Products, Different Futures
They get discussed as one category and have opposite requirements on latency, context, failure cost, and model tier. One is commoditizing into a feature; the other's differentiators aren't AI capabilities at all.
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If Intelligence Gets Cheap, What Becomes Scarce?
More useful than asking what gets cheap. Everything on the scarce list has one of two properties — and neither can be produced on demand, which is exactly why they stay scarce.