Service S.01 · AI Staff Augmentation · AGENTS IN THE TEAM

AI agents, embedded in your engineering team.

We configure and deploy agents into your repos, queues, and pipelines. They write code, review pull requests, fix bugs, and run infrastructure around the clock, through the same review process your engineers use.

30 minutes · an engineer, not a salesperson

24/7
Coverage on your backlog
100%
Output through your review process
0
New tools for your team to learn
Days
To a first merged pull request

The problem · 01

The backlog grows faster than headcount.

Bug fixes pile up. Documentation goes stale. Infrastructure tickets sit for weeks. Much of that work is routine (it needs care and context, not creativity) and it consumes your most senior engineers.

Hiring is slow and expensive. Contractors need constant management. Agents are a third option: software teammates that take the routine work, operate inside your existing workflow, and leave the judgment calls to your team.

They pick up tickets, open pull requests, and answer to the same review process as everyone else.

Agent roster · 06 ROLES

Six roles, one review process.

Purpose-built agents for each function. Deploy one or several. Every line of output lands as a pull request your team can inspect.

A.01

Dev Agent

Implements features from specs and writes production code across your stack, in your patterns, against your tests.

  • Feature implementation
  • API development
  • Test writing
A.02

Debug Agent

Triages bug reports, traces root causes, and ships fixes through your test suite. Watches logs to catch issues early.

  • Bug triage & diagnosis
  • Root-cause analysis
  • Hotfix pull requests
A.03

Infra Agent

Writes and maintains infrastructure as code, manages Kubernetes configs, and keeps deploy pipelines healthy.

  • IaC · Terraform, Pulumi
  • Kubernetes management
  • CI/CD pipelines
A.04

Review Agent

Reviews every pull request against the same standard: logic errors, security issues, and style, before they reach main.

  • Code review
  • Security scanning
  • Convention enforcement
A.05

Docs Agent

Keeps documentation current with the code it describes: API references, READMEs, and runbooks that stay true.

  • API docs from code
  • README maintenance
  • Runbook creation
A.06

PM Agent

Keeps tickets, status reports, and stakeholders in sync with what actually shipped: no chasing, no stale boards.

  • Ticket updates
  • Status reporting
  • Sprint preparation

Calibration · 02

Configured on your codebase, not a generic corpus.

Generic AI tools autocomplete the average of the internet. We configure agents on your architecture, your conventions, and your domain, so the code they write looks like your team wrote it and passes the checks you already run.

Tech stack

React, Vue, Node, Python, Go, Rust: agents learn your specific versions, frameworks, and patterns.

Coding standards

Your linting rules, naming conventions, file structure, and comment style. Code that passes your existing checks.

Workflow

Your branching strategy, PR templates, review process, and CI/CD pipeline. Agents follow your team's flow.

Domain knowledge

Your business logic, industry requirements, and compliance constraints. Context, not just syntax.

How it works · 04 STEPS

Autonomy is earned, not assumed.

Agents start narrow and supervised. Their scope grows only as the track record does, and you decide the pace.

STEP 01 Week 1

Discovery

We read your repos, map the architecture, and document the conventions and standards agents must follow.

STEP 02 Week 1–2

Configuration

Agents are set up for your stack and wired into source control, tickets, chat, and CI, the tools you already run.

STEP 03 Weeks 2–4

Pilot

Low-risk tasks first, every output human-reviewed. Each iteration sharpens the agents' grasp of your codebase.

STEP 04 Ongoing

Scale

Responsibilities expand as agents prove out: more throughput, with review checkpoints where they matter.

Wired in · YOUR TOOLCHAIN

Plugged into the tools you already run.

Agents work where your team works. No context switching, no new surface to learn.

Source control

GitHub, GitLab, Azure DevOps, Bitbucket

Project management

Jira, Linear, Azure Boards, Asana, Trello

Communication

Slack, Microsoft Teams, Discord

Cloud & infrastructure

AWS, Azure, GCP, on-prem environments

CI/CD

GitHub Actions, GitLab CI, Jenkins, CircleCI

Everything else

Custom integrations built for your stack

What changes · 03

Agents don't replace engineers. They return their time.

The point isn't fewer people; it's senior engineers spending their hours on architecture and product instead of the queue.

Always on

Agents work nights and weekends. Mornings start with reviewed-and-ready pull requests instead of a longer backlog.

Elastic capacity

Deadline coming? Add agents. Project done? Scale back. Capacity changes in days, without hiring or layoffs.

Consistent standards

The same conventions applied to every pull request, every time. Quality stops depending on who was tired that day.

Engineers on engineering

Humans keep the architecture decisions, product judgment, and hard problems. Agents take the routine rest.

FAQ · 05

Common questions

Is our code and data secure?+
Agents can run entirely inside your infrastructure, with private model deployments and data isolation. Your code never leaves your environment, and every agent action is logged and attributable.
Do agents need constant supervision?+
During the pilot, every agent output goes through human review. Autonomy widens deliberately as the track record builds. Most teams move to spot-checking within weeks.
What happens when an agent gets it wrong?+
The same thing that happens when a human does. Agents work through your existing review process: they open pull requests, run the test suite, and flag uncertainty instead of guessing. Unreviewed code doesn't ship.
Will this replace our developers?+
No. Agents take the routine work (bug fixes, documentation, infrastructure tickets) so your engineers spend their time on architecture, product decisions, and the hard problems.
What stacks do you support?+
Any language or framework. Agents are strongest in TypeScript, Python, Go, Rust, and cloud infrastructure on AWS, GCP, and Azure, and they're configured on your codebase, not a generic corpus.

Get started · 30 MIN

Talk to an engineer,
not a salesperson.

A free 30-minute technical consultation: your goals, your constraints, and a straight answer on whether AI is worth it for your case.

No commitment. No deck. Just engineering.