Agent

Most AI agent setups that disappoint their teams don’t disappoint because the model is wrong. They disappoint because the agent was asked to reason about systems it can’t see. A triage agent without PagerDuty access produces a vague analysis. An on-call agent without metrics hallucinates a root cause from alert titles. The agent isn’t bad; it’s undercontexted. Context engineering is the long game, and it has a structure. Specifically, it has four techniques — not a staircase. Each one matches a different shape of context source, and which ones apply depends on what you already have. A team with a CLI-heavy internal stack will spend most of its effort on technique 3. A team whose vendors all expose public MCPs might never touch technique 2. What remains true, regardless, is that technique 4 — bundling — is what turns any subset of the others into a team asset. ...

The limiting factor on how quickly an on-call engineer resolves an incident is rarely the rate at which they make decisions. It is the rate at which they can look at things — logs, metrics, traces, dashboards, SSH sessions — and assemble a picture of what the system is doing. Decision-making is cheap once the picture exists. Building the picture is where the time goes. This work is structurally single-seat. To bring a colleague in, an engineer has to translate what they have already seen: which log lines, when the metric started climbing, and what the network trace confirmed. The translation cost is high enough that most on-call engineers defer involving others until the problem forces it. The team is nominally 24/7; in practice, investigations happen alone. ...