There is a specific version of this conversation that happens often with developer-facing teams. Someone reads about AI support agents, thinks it sounds useful, flags it to the team, and then nothing happens for three weeks because it is unclear what "getting started" actually means in practice. Is this a two-hour project or a two-month one? Does it require engineering time? What does "training" the agent actually involve?
This article is the practical answer. It covers what the first eight weeks actually look like for teams connecting their docs to Inkeep, based on what we have seen across our early-access pilots. The short version: week one is a half-day. Week eight is your agent handling a material share of inbound questions. The steps between are real but not overwhelming.
Week 1: connecting sources and first test
Initial setup involves three inputs: your docs site URL, optionally your GitHub repository, and optionally a CSV export of closed support tickets from Zendesk, Intercom, or your equivalent. Inkeep ingests and indexes these automatically. No custom ETL work, no schema mapping.
By end of week one, a working agent exists in a staging environment. The first thing most teams do is type in the five or ten questions their support queue gets most frequently. This is not a formal evaluation, just a gut-check. Does the agent answer correctly? Does it cite the right documentation section? Are there obvious gaps?
The answer is almost always: yes to the first two, and yes to the third. There will be gaps. Your docs probably do not cover every common question explicitly. That is actually a useful data point before you go live.
Weeks 2 and 3: calibrating escalation thresholds
Before deploying to users, you need to decide what the agent should do when it is not confident. The default behavior is to escalate: route to your existing queue with a note indicating it could not find a confident answer. The threshold for "confident" is configurable.
Setting this threshold is the most consequential early decision. Too aggressive and you get high deflection but wrong answers, which is worse than not deploying at all. Too conservative and you get an agent that escalates everything, which provides no value. In our experience, teams benefit from starting with the threshold set conservatively and loosening it after seeing actual performance data from week four onward.
You also need to decide which channels to deploy in first. Most teams start with one channel, either their docs site widget or a Slack workspace, not both. Starting narrow gives you clean signal before expanding.
Week 4: first live questions and what you learn from them
Deploying to real users is the step that produces the most information. Week four is typically when you first see the agent answering questions from actual developers. Some of these answers will be excellent. A few will be wrong or incomplete. Some escalations will seem unnecessary in retrospect.
The workflow at this stage is to review escalations weekly. Not to rewrite the agent manually, but to identify which escalation categories are consistently pointing at documentation gaps. If ten questions about your authentication flow escalated in week four, that is a signal to improve the relevant documentation section. When the docs improve, the agent improves on the next sync cycle.
This feedback loop is the mechanism that drives most of the improvement over weeks four through eight. It is not tuning the AI. It is improving your documentation, which happens to make the AI more accurate as a side effect.
Weeks 5 and 6: expanding channels and sources
Once you have a week or two of live data and have addressed the first round of documentation gaps, most teams expand either the channel coverage or the knowledge sources. If you started with the docs widget, this is when you add the Slack or Discord integration. If you have not yet connected your closed support tickets, this is when that adds value: the ticket history contains phrasings and edge cases that the documentation does not explicitly address, which meaningfully improves answer coverage.
GitHub repository indexing is worth adding at this point if your product has a public or semi-public repo with significant README content, inline comments, or example files. We have found that codebases often contain answers to "how do I actually use this" questions that the formal documentation does not have, particularly around authentication setup, error handling, and SDK configuration.
Weeks 7 and 8: reading the steady-state signal
By week eight, the initial learning curve is largely done. The agent has been through several sync cycles. The obvious documentation gaps have been addressed. Your escalation rate has likely dropped from its week-four baseline as the most common gaps got filled.
This is when deflection rate as a metric starts being meaningful. Earlier than this, the rate is still reflecting your initial documentation state and threshold calibration rather than a steady-state picture. By week eight, you can look at what is escalating and assess whether those are genuinely hard questions (good, the agent is working correctly) or a category of question that keeps recurring because a documentation section still needs work (fixable).
We would not say eight weeks is when the agent is "finished." There is no finished state for a knowledge source that keeps changing. But eight weeks is when it transitions from a project that requires active attention to a system that runs in the background and surfaces maintenance signals when your documentation drifts.
What does not fit this timeline
Private or access-controlled documentation changes the setup meaningfully. If your docs are behind authentication or in a Confluence instance with strict permissions, the ingestion configuration requires additional steps and often coordination with your IT or security function. We handle private doc access in the product, but the timeline above assumes public documentation. If your docs are private, plan for an additional week or two in the setup phase.
The timeline also assumes your documentation is reasonably current. If you have a docs site that has not been substantially updated in over a year and contains known inaccuracies, the agent will reproduce those inaccuracies confidently. Deploying to users in that state is not recommended. The audit you do in week one, when you test with your most common questions, is designed to surface this, but it is worth knowing going in.