Posts about ai-agents
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Your Agent Pays the Cold-Start Tax Every Morning
Agent memory isn't knowledge management. It's performance infrastructure — and the gap between a stateless agent and one that accumulates context is measurable.
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Progressive Trust: How to Give AI Agents Autonomy Without Gambling
The debate about AI agent autonomy is wrong. It's not a binary choice — it's a graduated trust system with observability.
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This Year's DeepSeek
An open-source AI agent framework became the fastest-growing project in GitHub history — mostly in China. The pattern is the same as last year. So is the security panic.
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Enterprise AI Has a Plumbing Problem, Not a Model Problem
Most enterprises are optimising the wrong variable. The gap between 5% and 40% agent adoption won't be closed by better models.
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From Chatbots to Event Loops
The shift from agents you summon to agents that watch. Enterprise AI workflows are becoming continuous loops — and the failure modes are different.
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What MCP Actually Changes for Enterprise AI
Not better function calling — decoupling. When tools expose MCP servers, any agent can compose any system freely. The heterogeneity problem becomes a configuration problem.
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Language Is the Medium, Not the Purpose
We called them language models and spent years confused about why they could reason. The name stuck to the interface, not the mechanism.
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When Intelligence Becomes Infrastructure
What changes when LLMs stop being the special thing and become just another software component? The answer is: everything about how you build.
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Expansion, Not Speedup
The real ROI of AI coding isn't doing the same work faster. It's doing work that wasn't worth doing before.
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Skills as Files
The simplest agent architecture might already be the right one: give the agent a file explaining how to do something, and let it read when needed.
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The Trust Spectrum
Peter Steinberger stopped reviewing AI-generated code entirely. That works for indie software. In regulated environments, it can't. Here's how to think about where you sit.
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Traces Are the New Debugger
When behaviour emerges from both code and model responses, reading source files isn't enough. You debug by examining execution traces.
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Rules Decay, Hooks Don't
The difference between writing down a rule and making the system enforce it — illustrated by a 15-line hook.
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Agentic Engineering: Why Less Is More
Tool enthusiasm is often net-negative. Context pollution degrades performance faster than features improve it. The principles that actually work.
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CLIs Enforce Structure and Save Tokens — Not Just Discipline
The instinct to add a rule to a skill file is usually the wrong abstraction. A CLI wrapper enforces at the tool level: zero deliberation, zero token cost.
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Software Engineering Principles for AI Instruction Files
LLM instruction files are code. They have the same failure modes — with one interesting twist that changes everything.
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Agent-First CLI Design: TTY Detection as Philosophy
The primary user of my CLI tools isn't me anymore. Designing for that changes everything about how output should work.
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Banks Have an AX Problem They Don't Know About Yet
Banks are building AI agents to call their APIs. Those APIs weren't designed for agent callers. The mismatch is subtle, consequential, and almost nobody is talking about it.
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Skills as Behavioral Nudges: The Lightweight Alternative to Fine-Tuning
We fine-tune models with gradient descent. We nudge agents with skill files. Same goal, radically different cost.
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The Contract Pattern: Hard Gates for AI Agents
AI agents know how to start a task. They don't always know when to stop. The contract pattern is the architectural fix.
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AX: Agent Experience Is the New DX
Developer experience became a competitive moat in the API era. Agent experience is next. Most tools aren't designed for it yet.
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AI Agent Frameworks for Enterprise FS: What Actually Works vs. Hype
Most enterprise AI agent pilots in financial services fail at the same point: the second tool call. The problem isn't the framework.