AI Agents for Engineering Teams

Adding more developers doesn't make you faster.

Adding intelligent, context-aware automation does. The linear scaling of headcount is broken.

Productivity vs Headcount

Team

With Agents (O(n²))

The Review Bottleneck

Senior devs lose 30% of their week reviewing trivial patterns. Innovation stalls while PRs sit in limbo.

The "Bus Factor" Risk

Knowledge is trapped in Slack threads. Onboarding takes months because documentation is rarely a priority.

The Legacy Fear

Vital parts of your system become "do not touch" zones. Upgrading feels too risky without perfect coverage.

Vision

"Imagine your devs deploying 30 pull requests a day instead of 3."

Move Beyond Basic Linters

We don't just add tools; we implement intelligent agents that understand your architecture.

Rule Agents

Zero Architectural Drift. Agents enforce your specific architectural patterns and business logic during the coding process.

Fuzzing Agents

90%+ Test Coverage. Don't wait for bugs. Generate thousands of edge-case scenarios and synthetic user data.

The Review Bot

50% Faster Cycle Time. A custom bot that learns from your history to handle style and patterns before a human sees it.

Definition

What is an agent? A set of tools and instructions to perform a specific task. The agent is responsible to call tools, add the right context (your rules and code) to create a clear structured output.

Capabilities & Services

Smart PR Reviews

Review pull requests based on rules you want to apply and your team's comment history.

Tackle Legacy Code

Refactor very quickly with high quality. Deploy more often with automated refactoring agents.

Faster Onboarding

Generate clear visual docs on the obscure parts of your codebase automatically.

Bug Analysis

Find potential bugs before they arrive. Provide immediate solution when a bug occurs in production.

Centralize Knowledge

Active agents that centralize team knowledge into rules that every developer can read and modify.

Admin Panels

Create advanced admin panels so devs can focus on product and the team can read data independently.

Advanced Capabilities

Worker

Legacy Refactoring

Automated agents systematically upgrade old class components or legacy syntax file-by-file.

Pipeline

Auto-Updating Docs

CI pipelines that auto-update your API specs and READMEs whenever logic changes.

Correction

Auto-Correct Bugs

Pipelines that create PRs with elegant solutions instantly when a production bug occurs.

Why Invest in Agents?

✓
Financial Efficiency Avoid hiring senior devs just for maintenance. Increase Revenue Per Employee (RPE).
✓
Permanent Assets (CapEx) The agents you build stay with the company forever. Developers may leave; agents don't.
✓
Code Privacy Run agents on private servers with open source models. Your code never leaves your infrastructure.
✓
Team Health Give devs time for internal projects and open source contributions.

"Compress a 12-month roadmap into 2 months."

Battle-Tested Scale

Real-world production Rails application metrics

Metric	Count
Lines of Code	1,287,971
Controllers	157
Models	141
Views	497
Services	211
Specs	514
API Serializers	50
Form Objects	69
Presenters	15
Database Tables	83

Agents perform flawlessly at this scale.

The Process

1

30 min Call

Discovery

Understand pain points and tech stack. We map the chaos to clarity.

2

~8 Hours

The Audit

Analyze codebases. Write detailed implementation plan. We sit with your devs to understand the friction.

3

16h - 64h

Implementation

Implement agents. Create docs. Train your team. Deep focus execution.

4

~1 Month After

Maintenance & Evolutions

Verify results, adjust rules, and update context based on feedback.

About the Expert

Senior Software Engineer with 14 years of experience.

Key Skills: Ruby on Rails (Expert), Javascript, iOS (Objective-C), Python, Bash, Admin Sys.

"I know the industry from the inside. I've been working on complex codebases that all suffered the same pain points. And today, AI can solve all of them."

Tech Stack

Ruby On Rails

JavaScript

ReactJS

Vue.js

Next.js

Phoenix

Elixir

FastAPI

Flask

Django

Python

Go

Rust

Laravel

PHP

Java

Kotlin

Swift

Objective-C

React-Native

"Current employees with a bit of training and AI agents assistant will output 20x more than regular employees."

Frequently Asked Questions

We have a unique stack. Will this work?

Yes. The "Rules" and "Agents" are framework-agnostic. We build them to understand your specific patterns, whether you use Rails, JavaScript, Elixir, Python, Go, Rust, etc.

We already tried many tools but the code is garbage.

It was the case 6 months ago, but the models are evolving very fast. The reason the code is often bad is because of bad directions, not bad models. We have super powerful models that just need better rules and data to work on. It's all about adding context in a smart way.

Why custom agents and not external services/MCPs?

You don't depend on external API changes or price hikes.
Data privacy: Your code stays on your infrastructure.
Maintainability: Agents are mostly Python scripts you control.
Performance: Direct integration without MCP middleware latency.

Do we have to implement all agents at once?

No. We can start step by step and tackle the biggest bottleneck first. It's normal to start with only 2 or 3 agents and then create new ones.

Can we do all the process remotely?

Of course! I'll schedule the calls and meetings whenever is best for you.

Ready to scale your engineering without scaling headcount?

Book your free discovery call now. By the way, you're talking to me directly not to an AI agent ;)

Stop Using AI Just for Code Completion.