Azimut
An engineering team loses focus to interruptions that are almost never urgent: the same question about a deployment, the same question about where a procedure lives, the new engineer who needs someone next to them for weeks. Each one costs the person asking a few minutes and the person answering half an hour. Azimut makes the repository, the wiki and the incident history answer before anyone has to be interrupted.
What Engineering gains
Protecting focus
Recurring questions have a written answer somewhere, but it's faster to ask in the team channel. When an agent answers right there in that same messaging tool, with the documentation and the repository in front of it, the question gets resolved without anyone looking up from their code. Whoever knew the answer still knows it; what they stop doing is repeating it.
Closing incidents sooner
Almost no incident is entirely new. Someone saw something similar a year ago and the procedure is in the wiki, but at three in the morning there's no time to search. The agent finds past incidents and procedures on the spot, then drafts the report and the communication to those affected. The on-call engineer resolves it; the paperwork afterward comes out already done.
Onboarding faster
The new engineer today learns by asking a colleague, and that colleague stops producing in the meantime. With an agent that guides them through the repository, the processes and the decisions on record, the new hire asks the system and only escalates what genuinely needs a person. Days to first contribution shrink without anyone on the team having to mentor them full time.
The agents you turn on
| Agent | What it does | What gets measured |
|---|---|---|
| Question deflection | Answers in the team's messaging tool from the documentation and the repository. | Percentage of questions resolved without interrupting anyone. |
| Incident management | Searches past procedures and incidents and drafts the report and the communication. | Resolution time and hours per incident. |
| Technical documentation | Generates and maintains documentation from the code and recorded decisions. | Documentation coverage. |
| Developer onboarding | Guides the new engineer through the repository and the team's processes. | Days to first contribution. |
You start with question deflection. It's the agent with the least friction: it lives in the channel where people already ask, changes no workflow, and the whole team uses it from day one with no training. Its metric, how many questions get resolved without interrupting anyone, is easy to count and shows up in the first week. And the sources it connects, the repository and the wiki, are the same ones the other three agents use afterward.
Which systems it works with
Engineering agents read from the code repository, the team wiki and the messaging tool. The code connector is GitHub: if your team works on a different system, that gets said before starting, and how to proceed gets decided together, not discovered halfway through rollout. Each person sees only what they could already see in the source system, so repository permissions are respected on the platform. The specific integrations are confirmed with your team during rollout, and only the ones tested end to end get activated.
Which model fits
Almost all engineering work is well served by a mid-tier model: it understands code, follows a procedure and drafts an incident report accurately at a reasonable cost. Simple lookups, where a file is or what a command does, get resolved with a lightweight model, faster and cheaper. In Azimut, every user picks the model per task from OpenAI, Anthropic, Google and Mistral, and the team's agents don't depend on the model they were built with: switching providers is configuration, not development.
Where to start: the baseline
Before turning on any agent, you measure how the team works today. In Engineering that's two numbers: weekly interruptions per engineer and the average time to resolve an incident. The first is counted over a normal week, no embellishing; the second comes from the incident history you already have. You also note who's already using some AI tool on their own, with what and at what cost, because engineering is usually the department with the most loose licenses floating around.
Week 0. You collect weekly interruptions per engineer, the average resolution time for recent incidents, onboarding days for the last developer hired, and current AI use on the team: how many people, with what tool, and at what cost.
Month 3. You compare those same numbers against Azimut's usage panel by user, team, model and tool. As an order of magnitude, Minsait has published two hours a week per user, 80% adoption and 5% productivity, and Repsol more than three hours a week per employee and 18% higher quality; these are deployments of another tool, published by those companies and unaudited, not Azimut results.
Frequently asked questions
Does it replace my engineering team?
No. Building, deciding on the architecture and resolving the incident are still work for people. The agent answers questions, searches the history and drafts what has to get written anyway. What changes is that the team's knowledge stays on the platform instead of leaving with whoever changes projects.
Do I need an integration project?
No. Azimut connects to GitHub, the wiki and messaging with integrations from its catalog, which are tested before being activated. What it needs is single sign-on and permissions inherited from the source systems, configured in the first two weeks of rollout.
Who builds the agents?
At first, Azimut together with your team: the first agent is configured during rollout and shared with the whole company. From the second month on, engineers themselves build their own, one per service or on-call rotation, and publish them for the rest.
Measure how often you get interrupted today
Request a demo and we'll come away with your engineering team's baseline numbers.
Request a demo