20 posts tagged with "Agent Engineering"

A skill is a self-contained Markdown directive with a little YAML frontmatter on top, sitting in its own directory. That description makes it sound like documentation, which is exactly the trap. The interesting part is not what a skill contains. It's when the agent pays for it.

Here is the sentence we keep in our own skill registry, and it is the whole design: a skill is a directive the agent reads on demand via a read_skill tool, rather than something injected into every prompt — so the agent pays the token cost only when the skill is actually relevant to the turn.

"Run this agent every morning at 9" sounds like a one-line feature. There's a cron expression, there's a job table, there's a thing that reads the table. Ship it Friday.

Cron is the easy 5%. The other 95% is not firing twice, not losing a fire, not holding a database transaction open across a task that runs for minutes, and deciding whose 9am you actually mean. A cron expression is a trigger. A scheduler is everything you build around it.

"What can your agent do?" is two questions wearing one coat. The first is about the action space: once the agent is awake and thinking, what can it reach, call, read, and change? The second is about the trigger space: what makes it wake up in the first place? Teams conflate them constantly, and the conflation is expensive, because the two want opposite answers.

From the outside, creating an agent looks like filling in a form. You describe what you want, you look at a preview, you click a button. Thirty seconds, maybe less. The interface is doing everything it can to feel like nothing much is happening.

Every team that builds seriously with AI walks the same road, usually without noticing. It starts with a chat window and a good feeling, and — if the work survives contact with production — it ends somewhere far more disciplined. This is a field note on that road: the three stages we see teams pass through, what each one is genuinely good at, and the specific failure that forces the jump to the next.

"Give the agent memory" is one of those phrases that sounds like a feature and is actually an architecture. Everyone agrees agents should remember things across sessions. Almost no one agrees on what that means — and the default implementation, a vector database that everything gets dumped into, produces agents that are confidently wrong about their own past.

Every team that has tried to ship an AI feature has hit the same wall. The demo works. A stakeholder asks, "is it good?" and the honest answer is a shrug. You feel like it's good. You changed a prompt and it feels a little better, or a little worse, and you cannot say which, because "feels" is the only instrument you have.

The most expensive misconception in applied AI is that performance lives in the model. Pick the best model, the thinking goes, and the rest is glue. So teams spend their attention on which model to call and treat everything around it — the tools, the loop, the context they assemble, the way they check the output — as plumbing.

There's a tidy argument going around: skip the Model Context Protocol, just give the agent a shell and a CLI. No server to run, no schemas to maintain, and — the clincher — it saves tokens, because a command is short and a tool definition is long. It's a good argument. It's also only half right, and the missing half is where most of the cost actually lives.

When you build software with an AI agent day to day, the thing you run out of first isn't intelligence — it's context. The window is finite, attention inside it isn't free, and a session that starts sharp gets duller as it fills with the exhaust of the work: verbose command output, whole files read to find one function, stale documentation, the residue of three tasks ago.