AI Authority Gateway
A single choke point that controls, approves, audits, and enforces AI actions before they touch the real world.
The problem
AI systems are increasingly wired directly into production systems.
But there is no authority layer.
No approvals. No cost ceilings. No kill switch. No idempotency. No audit trail.
This is how companies end up with runaway costs, silent data corruption, duplicated actions, and irreversible mistakes.
What this is
AI Authority Gateway is an execution control plane for AI.
It sits between:
your application
your LLM
real-world systems (email, CRM, billing, etc.)
Nothing executes unless it passes policy.
This is not about better prompts. This is about control.
What it enforces
Out of the box, the gateway provides:
Environment-based execution control (dev / prod) Global kill switch Policy-based allowlists (models, environments, action types) Cost ceilings (pre and post execution) Human approval workflows Simulation-only execution mode Idempotency (prevents double execution) Append-only audit log Every request flows through a single authority boundary.
What it is not
This project is not:
a chatbot framework an agent playground a prompt engineering tool an orchestration library
It does not try to make AI “smarter”. It makes AI safe to execute.
Mental model
Client ↓ AI Authority Gateway ↓ (allowed / approved) LLM ↓ (controlled) Action Executor
The gateway is the only place where execution decisions are made.
Key concepts
Action vs decision
LLMs generate decisions. The gateway controls actions.
An action is anything with side effects:
sending an email writing to a CRM issuing a refund mutating production data
Decisions without authority are suggestions. Actions require enforcement.
Approval modes
Each action can run in one of three modes:
auto_execute Executes immediately if policy allows.
require_human_approval Creates an approval request. Nothing executes until approved.
simulation_only Runs the LLM, but never executes the action.
Approval modes can be set globally or per action type.
Idempotency
All action execution supports idempotency.
Same idempotency key + same payload → returns cached result Same idempotency key + different payload → request denied
This prevents:
duplicate emails duplicate CRM writes replay attacks
This is the same safety model used by Stripe, AWS, and Kubernetes.
Audit log
Every decision is recorded in an append-only audit log.
Each entry captures:
request id environment model action type decision (allow / deny / error) reason cost estimate approval status execution result
This is the system of record.
Current status
This is an early infrastructure prototype.
Approvals are stored in memory Idempotency is stored in memory Executors are stubbed (no real side effects)
Why this exists
Every major infrastructure shift creates a missing control layer.
Finance → ledgers Cloud → IAM Payments → Stripe CI/CD → deployment gates
AI currently has none.
This project explores what an authority layer for AI execution should look like.
Who this is for
This is for:
infra engineers platform teams security and compliance teams founders wiring AI into production systems
If you are building “AI agents” that can act, this problem will eventually find you.
Not production-ready (yet) This repository is intentionally minimal.
If you’ve thought about:
approvals for AI safe execution AI cost containment agent control planes
I’d love to hear how you’re approaching it.