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What is universal-earned-autonomy-layer?

mmvfirm/universal-earned-autonomy-layer — explained in plain English

Analysis updated 2026-05-18

13PythonAudience · developerComplexity · 4/5LicenseSetup · moderate

In one sentence

A Python control plane that lets AI agents earn more autonomy over time, enforcing hard limits on risky actions instead of just advising against them.

Mindmap

mindmap
  root((repo))
    What it does
      Earns AI agent autonomy
      Enforces hard limits
      Signs every decision
    Tech stack
      Python
      Postgres
      Docker
    Use cases
      Agent permission control
      Audit trail
      Risk ceilings
    Audience
      AI agent developers
      Security teams

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Add graduated permission controls to an AI agent that writes to a database or calls external APIs.

USE CASE 2

Enforce hard limits on irreversible actions like deletions or payments regardless of agent trust level.

USE CASE 3

Track a signed, tamper evident audit trail of every agent decision for compliance review.

USE CASE 4

Run the same control plane across multiple app replicas that share one Postgres database.

What is it built with?

PythonPostgresSQLAlchemyDockerHTTP APIPrometheus

How does it compare?

mmvfirm/universal-earned-autonomy-layer1lystore/awaek47cid/wp2shell-lab
Stars131313
LanguagePythonPythonPython
Setup difficultymoderatemoderatemoderate
Complexity4/52/54/5
Audiencedevelopervibe coderresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Full production use needs a real key vault and identity provider, local demo runs with make dev and make demo.

Apache 2.0: free to use, modify, and distribute, including commercially, as long as you keep copyright and license notices.

So what is it?

Universal Earned Autonomy Layer is a Python library that manages how much freedom an AI agent is allowed to have when taking real actions. Instead of trusting an agent from the start, it makes the agent climb an eight step ladder for each combination of task type and authority level, earning more trust only after it produces clean decisions that a human has checked. High risk or irreversible actions are capped by fixed ceilings, so no amount of accumulated trust ever lets an agent skip human review for the most sensitive tasks. This is a rewrite of an earlier version. The first version only reasoned about what authority an agent claimed for itself and returned a status message, but it never actually stopped the agent from proceeding. This new version binds every decision to a signed token, checks that token against a policy enforcement point before anything real happens, and verifies each agent's identity with cryptographic signatures rather than trusting a self reported name. The project includes a signed and tamper evident audit log, support for running multiple copies of the system safely against a shared Postgres database, and tools for measuring how trustworthy an agent has proven itself using statistics rather than guesswork. It ships with a command line tool, an HTTP API with login support, a software development kit for building agents, Docker setup, and automated tests, all runnable with a handful of make commands like make dev, make test, and make demo. The README is unusually direct about what is and is not included. The core safety logic, enforcement, and audit trail are described as tested and ready to run. Genuinely production grade deployment work, such as storing private keys in a proper hardware vault, connecting to a real login provider, and running a security review, is explicitly left for whoever deploys the system. This project would suit developers and teams building AI agents that take real world actions, such as writing to a database or calling external services, and who want a way to gradually and safely expand what those agents are allowed to do. It is released under the Apache 2.0 license.

Copy-paste prompts

Prompt 1
Show me how to register an agent and build an enforced event using earned_autonomy's EarnedAutonomyControlPlane and PolicyEnforcementPoint.
Prompt 2
Walk me through setting up Ed25519 keys and the eal init-db command to get Universal Earned Autonomy Layer running locally.
Prompt 3
Explain how the MAX_AUTONOMY_BY_RISK ceilings work in this repo and how to configure them for my own risk categories.
Prompt 4
Help me wire this control plane's HTTP API with OIDC login for my existing agent service.

Frequently asked questions

What is universal-earned-autonomy-layer?

A Python control plane that lets AI agents earn more autonomy over time, enforcing hard limits on risky actions instead of just advising against them.

What language is universal-earned-autonomy-layer written in?

Mainly Python. The stack also includes Python, Postgres, SQLAlchemy.

What license does universal-earned-autonomy-layer use?

Apache 2.0: free to use, modify, and distribute, including commercially, as long as you keep copyright and license notices.

How hard is universal-earned-autonomy-layer to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is universal-earned-autonomy-layer for?

Mainly developer.

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