SFS Product

Turn Claude Code and Codex Into Reliable Internal Engineers

Most teams already have powerful coding agents. The missing piece is the company context, guardrails, tools, verification and memory that make a senior engineer reliable. SFS adds that layer around the agents you already use.

Start with a free call See the system

Keep Claude Code, Codex or your existing agent stack. SFS becomes the organizational intelligence and control layer around it.

The missing layer

An AI engineer with first-class organizational context, tools, memory and verification.

The model already knows how to write code. SFS makes sure it understands your company before it acts — and proves the result before it asks for approval.

A Linear issue, Jira ticket, Sentry error, incident or product requirement enters SFS first.

SFS finds the relevant context, applies company rules and learned lessons, defines success criteria and selects the right tools and permissions.

Only then does Claude Code or Codex implement the change.

Linear / Jira / Sentry / Incident / Requirement
                    ↓
                   SFS
     ┌───────────────────────────────┐
     │ Understand the task           │
     │ Find relevant org context     │
     │ Apply rules + learned lessons │
     │ Define success criteria       │
     │ Select tools + permissions    │
     └───────────────┬───────────────┘
                     ↓
          Claude Code / Codex
                     ↓
              Build the change
                     ↓
        Tests + Browser + Evals
                     ↓
             PR + Evidence
                     ↓
              Human Approval

Use the agents you already trust

We don't replace your agents.

Your team can keep using Claude Code, Codex or other coding agents. SFS becomes the control and intelligence layer around them.

Without SFS

The agent sees a task and a repository.

"Fix this issue" + one repository

Engineers then spend time feeding context, correcting assumptions, testing the result and steering the agent step by step.

With SFS

The same agent gets the organization around the task.

Task + relevant repositories + architecture + dependencies + previous PRs + engineering conventions + known mistakes + internal tools + exact success criteria

The underlying model has not changed. The system around it has — which makes the work far more reliable and reviewable.

Reliability loop

Reliability comes from the system, not a better prompt.

SFS drives every agent through a controlled engineering loop instead of letting code generation define “done.”

Understand

Interpret the task and identify what matters.

Get context

Pull the right repos, dependencies, rules and history.

Plan

Define the implementation path and success criteria.

Implement

Let Claude Code, Codex or another agent build the change.

Run + test

Run the real application, automated tests and browser flows.

Evaluate

Check the result against the task's exact success criteria.

Fix failures

Loop back when tests, evals or user flows fail.

Produce evidence

Attach the proof a reviewer needs to make a decision.

Human approval

Hand off only after the system has earned review.

The agent does not decide that it is done because code was generated. It has to produce evidence that the change works.

Shared engineering memory

Every mistake improves the system.

When an agent gets something wrong, SFS turns the failure into organizational memory instead of letting the same mistake recur in the next session.

The important question is not just “what failed?” It is why the system allowed the failure.

Was context missing? Was a rule absent? Did the agent choose the wrong pattern? Was the test weak? Was a tool used incorrectly?

The lesson is preserved in the layer future agents use.

AI mistakeA failure appears in implementation, review, testing or production.

Find the causeMissing context, missing rule, wrong pattern, weak test or bad tool usage.

Preserve the lessonUpdate context, rules, skills, workflows or evals.

Future agents inherit itThe company stops paying for the same AI mistake repeatedly.

The result

Move engineers from constant steering to high-leverage review.

Today

prompt ↓ correct ↓ prompt again ↓ correct again ↓ test ↓ debug ↓ review

With SFS

Assign the work ↓ SFS + Claude/Codex do the engineering ↓ Review the implementation + evidence ↓ Approve

Claude Code and Codex provide the intelligence.
SFS gives them the organizational context, tools, verification and learning loop required to behave reliably inside your company.

Bring it into your engineering system

Give your coding agents the company around the code.

Start with a free call. We’ll look at your current agent setup, engineering workflow and where an SFS-style control layer can create the most leverage.

Start with a free call