Engineering consulting

Cursor consulting and team training

For engineering teams that use Cursor, or are about to, and want a shared practice in the editor in place of a private setup per engineer. We work on rules, context and tool access in your repositories and train on your own backlog. Consulting and training come in whatever proportion you need.

Discuss your setup

Project rules and context

Without shared rules, the agent follows whatever conventions someone happened to type into the chat, and reviewers settle them again in every pull request. Rules checked into the repository move that argument to one place. The hard part is keeping them useful: rules that are too long get diluted, rules that are always attached cost context on every request, and rules nobody owns go stale.

We write them with the engineers who review that repository, run them against real tasks, and delete whatever the agent ignores or misreads.

The real build, test and lint commands, so the agent checks its own work before you do
Conventions reviewers already enforce, stated once and scoped to the paths where they apply
A split between always-on rules and rules attached by path or on request, to keep context spend down
An owner and a review step for rule changes

Task scoping and review practice

Once an agent is delivering diffs steadily, review can become the bottleneck. Much of what we practise is aimed there: cutting work so one task yields one reviewable diff, asking for a plan before the first edit on anything cross-cutting, and rejecting a plan that touches more than the task needs.

The other habit is knowing when to stop. Whether to keep steering a session or start fresh with a tighter brief is a judgement call: how far the work has drifted from the original scope, how much of the session's context is now stale, and whether the resulting diff is still reviewable. The team also needs an agreed answer to who runs the tests and who reads the output. An agent's report that the tests pass is not a check.

One task, one diff a reviewer can read in a sitting
Plan first for changes that span modules, with the plan reviewed like a design note
Tests run by the engineer or CI, with the output read by a person
An agreed point at which to discard a session and restart, or take over by hand
Debugging from the failing command and its actual output

Tool access, interactive work and cloud agents

MCP servers and auto-run settings decide what the agent can reach and what it does without asking. Left to individual laptops, they differ per engineer, which makes results hard to compare and incidents hard to reconstruct. We agree an allowlist with you, including which servers carry credentials and what those credentials can do, and put the shared part in the repository or in admin settings where your plan supports it.

Cloud agents work on a branch in a remote environment and come back as a pull request. Before a team relies on them we check three things: whether the environment can build and test the repository, which secrets it would need and whether you are willing to put them there, and how those runs are metered on your plan compared with editor use. Work with moving requirements or expensive wrong steps stays interactive.

What you can configure depends on your Cursor plan and admin settings, so we check those before proposing anything. The same applies to data: we list what is sent where under your current privacy mode and indexing settings, and you compare that with Cursor's terms. We make no privacy commitments on Cursor's behalf.

Training format and what you keep

Training is a working session with one team, in its own repositories and on items from its backlog. The time goes to the places where the team's practice diverges. Feature walkthroughs we leave to Cursor's documentation.

Content depends on where the team is. A team starting out needs a baseline: rules, scoping, review. A team that has used Cursor daily for a while usually needs to settle disagreements, such as how much to delegate, which models are the default and what a reviewer may assume about an agent-written diff. Afterwards the team keeps the rules, the exercises, the tool access notes and a short onboarding runbook.

Questions

When does a task belong with a cloud agent instead of in the editor?

When you could hand it to a colleague in writing, the repository's tests would catch a wrong result, the remote environment can build without secrets you would rather keep local, and someone reviews the outcome as a pull request. If one of those is missing, keep it in the editor.

Does every Cursor plan include what this page describes?

No. Features and admin controls differ between plans and change over time. We check what your plan and settings allow before we scope the work, and leave out anything you don't have.

Which model should be the team default?

The one that holds up on your tasks at a cost you accept, and that differs by repository and task type. Cursor lets engineers pick among the available models per request, so a team default is first of all a convention; which admin controls back it up is something to check against your current plan rather than assume. If the choice matters enough to measure, repository evaluations describe how. What the plan and usage-based charges come to per accepted change is covered under LLM cost optimization.

Related services

Coding agent adoption for engineering teams

For teams adopting coding agents, whether you start with one tool in one team or bring several tools and teams together, and want a rollout you can operate yourselves.

Harness engineering for coding agents

Most of how a coding agent behaves in your repository is decided by its harness: what it is told, what it can run and reach, and what checks its output before a person sees it. We engineer that software runtime with your team so the agent works with your build, tests and review process.

Coding agent evaluations on your repositories

Public benchmarks say little about how an agent performs on your code. We help you evaluate models and harness changes on tasks from your own repositories.

LLM cost optimization for coding agents

We help you work out where your coding agent and LLM spend goes, then implement the changes that hold up when tested against quality.