Autonomous Engineering Loops
Level 3 autonomous coding is production-ready for organizations that have built the right foundation. We design the architecture, security boundaries, and CI integration to make it safe and productive.
There are three levels of AI involvement in software development.
Autocomplete, inline suggestions, chat-based help for specific questions. Most engineering teams are here.
AI that can execute sequences of actions, make file edits, run commands, and iterate. Cursor, Claude Code, and similar tools operate here when used well.
AI agents that receive a task, write the code, run the tests, handle failures, open a pull request, monitor CI, and merge when everything passes. A developer reviews the output. They don’t manage the process.
This is the offering. We build Level 3.
Why now
These are not research projects. They are production systems at companies that took the time to build the right infrastructure.
Stripe
Stripe’s internal Minions system produces over 1,300 PRs per week autonomously.
Ramp
Ramp’s Inspect handles 30% of their pull requests through AI agents.
OpenAI
OpenAI’s internal agent infrastructure has authored more than one million lines of production code.
What enables Level 3
Agents must be able to execute code without access to production systems. We design and implement isolated execution environments where agents can run tests, install dependencies, and make changes without risk to live infrastructure. The sandbox is the prerequisite.
Autonomous agents need carefully defined boundaries: what codebases they can access, what external services they can call, what credentials they can use, and what changes require human review regardless of test results. We define these boundaries and build the enforcement mechanisms.
The system that assigns tasks to agents, monitors progress, handles failures, and surfaces decisions that require human input. Autonomous doesn’t mean unmonitored. The orchestration layer is where humans stay informed without needing to manage every step.
Agents produce better code when they have full context: the codebase structure, relevant documentation, architectural patterns, and the organization’s coding standards. We build the context delivery that makes agents effective on your specific codebase rather than on generic tasks.
The integration with your existing CI/CD pipeline that lets agents run the same test suites, linting, and checks your human developers run. Agents can’t merge without passing CI. The quality gate stays the same regardless of who wrote the code.
Developers review pull requests. They define new tasks. They handle the decisions that require judgment rather than execution. They set the direction and the standards.
What they stop doing: manually writing boilerplate, handling mechanical refactoring, updating tests when interfaces change, maintaining documentation as code evolves, and any other category of well-defined engineering work that AI can execute reliably.
The time freed up goes toward architecture, product decisions, and the engineering work that actually requires judgment. This is the shift from intelligence work (rules-based execution) to judgment work (decisions that require experience and context).
Organizations get the most from Level 3 after they've built Level 1 and Level 2 foundations: their developers are already effective with AI coding tools, they have good test coverage, their CI pipeline is reliable, and they understand how AI interacts with their codebase.
Trying to jump to autonomous loops without that foundation produces autonomous agents making autonomous mistakes with no human catching them in the process.
We assess readiness as part of every engagement scoping conversation. If Level 1-2 foundations need work first, we say so and help build them as part of the same engagement.
Getting to Level 2
Before autonomous loops, your team needs effective AI coding tools. We handle the full deployment: tool selection, context engineering, governance, onboarding, and measurement.
We evaluate Cursor, Claude Code, Copilot, and others against your specific environment. Then we build the context infrastructure — agent rules, memory configs, code indexing — that makes the tools effective on your codebase.
IP policies, security rules, and code review expectations so developers can use the tools fully. Onboarding sessions on their actual codebase, not generic demos.
Baseline metrics before deployment, measurement after. PR velocity, time to review, test coverage. The difference between knowing the deployment worked and hoping it did.
We evaluate your current engineering infrastructure: test coverage, CI reliability, codebase organization, and existing AI tool usage. The output is a specific readiness assessment and an architecture recommendation.
We build the sandbox, security boundaries, orchestration layer, and CI integration. Your engineers participate throughout. The system is built on your infrastructure, not ours.
We run autonomous loops on a scoped set of tasks, monitor outputs, measure quality, and calibrate the system. We establish the task categories appropriate for autonomous handling versus tasks that require human involvement at specific decision points.
Your engineering team can operate, extend, and maintain the system independently. We document the architecture, the security boundaries, and the escalation logic.
These numbers reflect what's happening at organizations that invested in the infrastructure. The investment is the differentiator, not access to the tools.
From autonomous agents
Handled by AI
Production code authored by agents
Public GitHub commits authored by Claude Code
The first question is whether your engineering infrastructure is ready. We assess your current setup and give you an honest answer.