Docs

Your ticket queue already contains the FAQ you haven’t written. To get it out, group the questions before comparing them against what you have — and count not just how many customers asked, but how long they kept asking. The backlog was written by the people who will read it. Someone answers the same question for the fourth time in two weeks, notices, and writes an FAQ article if the week is quiet. That is how most knowledge bases grow. It works, and it has a predictable blind spot: it finds the questions that annoy whoever is on shift, and misses the ones spread thinly enough that nobody noticed — which are exactly the ones a written article serves best. ...

Every week, an automation pulls PagerDuty data and writes a trends report — top services, alert noise by rotation. Humans read it at the weekly review and adjust priorities. The report goes into Notion or the team wiki. A week later, someone asks an agent — “what are the recent PD trends?” The agent has MCP access to both PagerDuty and the team’s docs. Retrieval finds the report. It matches the query perfectly: same words, polished structure, topical headings. The agent answers from the report. ...

Step back to what the customer actually wants when things break, and the answer is short — service works, fix is fast, customer informed, no repeats. PagerDuty, Grafana, Datadog, Sentry, Slack — each does its job, and each does it well. But those are the team’s tools, not the customer’s vocabulary. The implementation reflects a specific constraint: every layer of the existing stack was designed around what humans need to do incident response. When the responder changes from human to agent, the right question isn’t “what does each tool become.” That’s tool-first thinking, and it accidentally preserves the existing shape. The cleaner question is layer by layer: does this layer’s function exist for the customer, or does its form exist because humans need it? Functions survive — customers are permanent. Forms are up for renegotiation when the actor doing the work isn’t a human anymore. ...

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. ...