OptFor.AI Consulting / Transformation / Development

Autonomous software delivery

From task to verified pull request

You assign a clearly described task. The agent completes it in a prepared, isolated environment, runs the tests, and checks the running application. Your team gets a pull request with verification evidence — and keeps the merge decision.

Example task run

  1. 0:00 Task assigned to the agent
  2. 0:05 Environment ready — dependencies, services, caches
  3. 5:12 Tests passed: 86/86
  4. 8:47 Running application verified
  5. 9:34 Pull request ready for review

The problem

An agent alone is not enough

Autonomous work on code pays off only when the environment, access boundaries, and verification are prepared as carefully as the model itself.

01

Cold starts burn time and context

Instead of implementing, the agent installs dependencies, repairs the toolchain, and boots services — spending its token budget before it touches the actual task.

02

Unverified code shifts the cost to your team

A fast implementation helps little if nobody ran the tests or the application before review. A person ends up doing that work — on their side, on their time.

03

Broad access is a real risk

Repository credentials and open network traffic should not enter a process that executes code and handles task input from outside.

≈2×

fewer tokens spent on the same task

≈4×

faster from task to a merge-ready pull request

Internal tests: identical tasks run in a prepared environment versus a cold start.

How it works

One task, a controlled process, a clear result

The unit of work is a task with a defined scope and acceptance criteria. The result is a pull request that goes through your team’s regular review process.

  1. We connect the repository

    We define the tools, the project startup procedure, the allowed integrations, and the checks that define “done”.

  2. We prepare the execution environment

    Dependencies, toolchains, services, and caches are built and validated before the agent starts. Every task begins with a running project, not an empty machine.

  3. The agent completes a bounded task

    It reads the repository context, implements the change, and iterates on test results and the behavior of the running application. Secrets stay outside its process, and outbound traffic is filtered.

  4. Your team receives a reviewable change

    The pull request contains the code, the check results, and an execution record. A person evaluates the solution and makes the final merge decision.

Principles

Speed comes from preparation, not skipped controls

A prepared environment

A validated agent workspace — dependencies, toolchains, and caches ready before the start, instead of rebuilding the workstation for every task.

Running-application verification

Beyond tests and static checks, the process validates key behaviors of the running system — not just whether the code compiles.

Credentials behind a broker

Secrets never enter the agent process. Services are reached through an intermediary layer whose permissions are limited to the task.

Filtered outbound traffic

Network connections are restricted to allowed destinations, closing off the risk of code and data exfiltration.

Your model, your rules

We match the model, instructions, and execution tools to your stack, risk profile, and organizational standards.

Merge stays with a human

Automation ends with a change ready for review. The deployment decision and accountability remain with your team.

Pilot

Let’s test it on a real task

We will pick a repository, set the access boundaries, and run a pilot on tasks whose outcome can be judged unambiguously. Your team’s review process stays unchanged.

Book a conversation