1d ago · 7 min read · Every agent you've built probably has a system prompt buried somewhere in your application code. A string template in a TypeScript file. Maybe a multi-line constant with some variable interpolation. It works — until it doesn't. The problem isn't the ...
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2d ago · 6 min read · There's a pattern emerging in production agent systems that nobody talks about enough: the agents that ship reliable output aren't the ones with the best models — they're the ones with the best verification layers. If you've built anything beyond a d...
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3d ago · 6 min read · Most teams building agents start with tools. A function to search the database, another to create a ticket, a third to call an external API. They write them inline, wire them up to the model, and move on. Then the second agent shows up. Suddenly that...
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4d ago · 7 min read · Every team building agents ends up building orchestration. Few realize they're building infrastructure. It starts small. You wire up a tool call loop. You add retry logic. You build a way to persist conversation state. You handle the case where the m...
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5d ago · 7 min read · Most teams building AI agents hit the same inflection point. The first prototype works — a single agent, a single prompt, a single conversation. Then requirements pile up: "Can we add a research step?" "Can it hand off to a specialist?" "Can two agen...
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6d ago · 6 min read · The most interesting agents won't have a chat interface. They'll run in the background — processing data, executing multi-step workflows, calling tools, returning structured output — all without a user typing a single message. The industry is startin...
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Apr 16 · 6 min read · AI agent discourse keeps orbiting around model quality, benchmark jumps, and increasingly polished demos. But if you look at what developers are actually wrestling with in production, the center of gravity has moved. The hard part is no longer gettin...
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Apr 16 · 7 min read · You've shipped a bug before where you added a console.log, saw the wrong value, fixed the line, and moved on. Thirty seconds, done. Now try that with an agent. The agent called the wrong tool. Why? Because the model interpreted the user's message dif...
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Apr 15 · 6 min read · Getting an agent to work in a demo takes an afternoon. Getting it to work in production takes months. The gap between those two states is where most teams stall — and it has almost nothing to do with the model. The prototype loop is tight: pick a mod...
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Apr 14 · 8 min read · Most agent tools are written once for one agent and never used again. They're inline lambdas, glued to a specific session, shaped by whatever the LLM happened to need that day. When a second agent needs similar functionality, someone writes a second ...
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