AI Coding Agent Setup (Claude Code, Cursor, Codex)

Overview

What we deliver

We set up AI coding agents in your repositories, configure guardrails, and train engineers so coding assistants deliver real productivity gains.

Most teams install a coding agent in a week and quietly stop using it a month later. We solve that. Our team configures Claude Code, Cursor, and Codex in your repositories with the right rules, context files, and workflows so engineers actually rely on them. We start by reviewing how your team works today, then write project-level instructions, test patterns, and review checklists that match your codebase. We set up secure access to internal documentation, package registries, and code search so agents have the context they need to be useful on real tickets. We train your engineers through pairing sessions and code reviews, and we set up adoption metrics so leadership can see where the tools help and where they need tuning. The result is a coding agent setup that ships shippable code on real work, not demo branches.

Fit Check

Built for teams like yours

Who it's for

CTOs rolling out AI tools to engineering
Engineering leaders standardizing developer tooling
Platform teams supporting many product squads
Founders shipping with small engineering teams
Consultancies modernizing their dev workflow

Pain points we solve

Low adoption after initial AI tool rollout
Inconsistent quality of agent-generated code
Security concerns about code access and prompts
Engineers unsure how to use agents on real work
Missing guardrails for production-grade changes

What's included

Capabilities

Everything we cover in this engagement.

Repository audit and tooling fit assessment
Claude Code, Cursor, and Codex configuration
Context files, rules, and project instructions
Internal MCP server and tool integration
Secure access patterns for code and secrets
Engineer onboarding and pairing sessions
Code review checklists tuned for AI output
Adoption metrics and reporting

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Assessment

We review your repositories, workflows, and current tooling to size the rollout.

02

Configuration

We set up the chosen agents with rules, context, and integrations for your stack.

03

Pilot squad

We work with a pilot team on real tickets and refine the setup based on what we learn.

04

Rollout

We onboard remaining squads with training, pairing, and shared playbooks.

05

Measure and tune

We track adoption and outcomes, then iterate on rules and workflows.

What you get

Deliverables & outcomes

What you get

Configured Claude Code, Cursor, or Codex setup
Repository context files and project rules
Internal tool and MCP integrations
Engineer training sessions and recordings
Code review checklists for agent output
Adoption metrics dashboard

Outcomes you can expect

Higher daily active use of coding agents
Faster cycle time on routine tickets
Fewer review rounds on agent-generated code
Better consistency across squads
Clearer leadership view into AI productivity

Timeline

Three to seven weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Claude Code, Cursor, GitHub Copilot, OpenAI Codex, Model Context Protocol

KPIs we track

Daily active users, pull requests assisted, review cycles, cycle time, engineer satisfaction

Client stories

What clients say

"

We were drowning in tier-one tickets about password resets and appointment changes. They built a deflection layer on top of our help desk and kept their agents in the loop for anything sensitive. Volume to humans dropped 58 percent in two months and our patient NPS held steady. The hybrid handoff is the part most vendors get wrong. They did not.

P.M.

"

Our old site was a Frankenstein of three previous agencies. We gave them a hard launch date tied to a trade show and they actually hit it. 47 templates, full product catalog migration, no broken redirects on go-live day. Our previous vendor missed the same deadline twice. This time my phone stayed quiet on launch morning.

Marcus L.

Proof