AGENTIX · OWNED PRODUCTDEVELOPER TOOLING · 2026
§ Owned product · proprietary

Autoagentix Dev

The Autonomous Development Framework

A provider-agnostic, deterministic CLI you inject into any repository. It detects the AI coding agent you already use, upgrades that setup to best practice, and drives development through a full phased lifecycle with human approval gates.

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0+tests · offline
0data contracts
0coding-agent providers
Honest status: this is early

Autoagentix Dev is in active development (Early · v0.0.1). It is not yet a finished, generally-available product. What's below describes what it does today, not a roadmap dressed up as a result.

§ Who it is for

A defined user.
A specific problem.

Audience

Engineering teams already using a supported AI coding agent who want a repeatable, provider-agnostic development lifecycle around it.

Pain

Open-ended agent sessions couple the workflow to one provider, start editing before they understand the repository and produce free-form hand-offs that are difficult to verify.

§ What it is

The Autonomous Development Framework.

Autoagentix Dev is a command-line framework, not another AI agent. It detects which coding agent is already installed in your repo, whether that's Claude Code, Codex, Cursor, Copilot, Gemini, OpenCode, Aider or Devin CLI, and upgrades that setup to best practice rather than replacing it. It learns the repository, then runs development through a phased lifecycle, init, plan, implement, testing, monitor, with human approval gates at each step. A tracker/PR/bugfix/learning layer stays in sync throughout. Every phase is contracts-first: 32 JSON-schema data contracts define what each phase produces, verified by 1,000+ tests.

§ The change

Before → system → after.

01 · Before

A coding agent works through open-ended prompts with provider-specific setup and inconsistent phase outputs.

02 · Autoagentix Dev

Autoagentix Dev detects the installed provider, learns the repository, enforces 32 contracts and pauses at human approval gates.

03 · After

The existing coding agent runs inside a phased, testable workflow whose contracts and gates remain when the provider changes.

§ How it works

A visible path
through the system.

The steps below describe the current product behaviour from the documented source of truth.

  1. 01

    Detect the existing agent

    Recognise Claude Code, Codex, Cursor, Copilot, Gemini, OpenCode, Aider or Devin CLI rather than installing a model of its own.

  2. 02

    Learn the repository

    Six knowledge-base scanners read Python, TypeScript, JavaScript, Go, Java and C# code before a development phase runs.

  3. 03

    Run the phased lifecycle

    Move through init, plan, implement, testing and monitor with a human approval gate between phases.

  4. 04

    Verify the contracts

    Check each phase's structured output against 32 JSON-schema contracts and the 1,000+-test offline suite.

§ What it does

Built, not planned.

Provider detection, not replacement

Detects the AI coding agent already installed in the repo (Claude Code, Codex, Cursor, Copilot, Gemini, OpenCode, Aider, Devin CLI) and upgrades that setup to best practice. It never embeds its own AI, the intelligence stays your installed provider.

Repo learning via KB scanners

Six knowledge-base scanners read the codebase across Python, TypeScript, JavaScript, Go, Java and C# to build a working understanding of the repo before any phase runs.

Phased lifecycle with approval gates

Development moves through init (detect → kb → analyze → improve → scaffold → validate), plan, implement, testing and monitor, each phase gated on human approval before proceeding.

Contracts-first architecture

32 JSON-schema data contracts define the shape of every phase's output, checked by a 1,000+-test offline suite.

Always-on tracker & bugfix layer

A tracker/PR/bugfix/learning layer runs continuously alongside the phased lifecycle, keeping work items, fixes and learnings in sync as the repo evolves.

§ How it runs

A phased lifecycle,
gated on your approval.

Each phase hands off to the next only after a human approves it. A tracker/PR/bugfix/learning layer stays in sync alongside every phase.

  1. 01init (detect → kb → analyze → improve → scaffold → validate)
  2. 02plan
  3. 03implement
  4. 04testing
  5. 05monitor
§ Stack

Deterministic core.
Provider-agnostic edge.

PythonTyperjsonschemaRich
§ Practical fit

Deployment, with
the boundaries visible.

Deployment

Self-host it in your infrastructure, with K3s on Hetzner as the documented self-hosted option. Inject the CLI into each repository; it uses the supported coding agent already installed there and embeds no model provider of its own.

Limitations

  • Autoagentix Dev is v0.0.1 and in active development; no measured production outcomes are claimed.
  • Provider detection currently covers eight named coding agents, and repository scanning covers six named programming languages.
  • The framework does not supply its own model or replace the installed coding agent.
  • Progress between phases requires human approval; bypass is not the default behaviour.
Next step

See whether Autoagentix Dev
fits your system.

We'll show what exists today, state the limits plainly and decide whether an early product is the right fit.

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AGENTIX TECH · owned product · In active development