acp-ai 0.2.3

ACP — AI Control Plane: deterministic governance and execution control for enterprise AI agents.

ACP — AI Control Plane

Deterministic governance and execution control plane for enterprise AI agents and autonomous systems.

Note: “ACP” means many things in other industries (medical, political, nonprofit, and more). In this project it always means ACP — AI Control Plane, not a generic acronym.

Add governance, approvals, policy enforcement, and execution visibility to AI agents in minutes.

Works with CrewAI, LangGraph, Strands, Google ADK, MCP tools, and custom Python workflows.

Requires: Docker Desktop (Compose v2).

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Quick start

pip install acp-ai
acp init          # optional: starter policies in ~/.acp/policies
acp up --build    # first run: build + start stack
acp dashboard     # open governance UI

Step	Command
Install	pip install acp-ai
Start stack	acp up (use acp up --build once)
Open UI	acp dashboard → http://localhost:3090/dashboard/
Stop	acp down

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Why ACP — AI Control Plane?

Most agents call tools, APIs, and other agents directly. Teams then scatter rules across Python, workflows, and frameworks:

if supplier_risk_score > threshold:
    require_human_approval()

That becomes inconsistent, hard to audit, easy to bypass, and duplicated everywhere.

ACP — AI Control Plane centralizes governance outside agent code:

Capability	What you get
Centralized governance	One place for rules, not copy-paste per team
Policy enforcement	OPA/Rego evaluates every governed call
Approvals	Escalate high-risk actions to humans
A2A governance	Governed agent-to-agent calls
A2T governance	Governed agent-to-tool calls
Audit & visibility	Decisions, traces, registry in one dashboard

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Architecture

Agent / Workflow  →  ACP SDK  →  Interceptor / Gateway  →  OPA (Rego)  →  Allow / Deny / Escalate  →  Execution

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Dashboard

The ACP — AI Control Plane dashboard is a core differentiator: live allow/deny/escalate decisions, approvals, agent registry, and policy catalog. Open http://localhost:3090/dashboard/ after acp up.

Overview & activity

Decisions & approvals

Registry & policies

Forensics

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Deployment modes

Mode	How	Best for
Local	acp up via pip + Docker	Demos, dev, quickstart
SDK	@governed_tool in your agent code	CrewAI, LangGraph, Strands, custom Python
Gateway	Single origin on :3090 (dashboard + API proxy)	Local unified URL; pattern for prod ingress
Cloud / self-hosted	Docker Compose, Kubernetes, ECS/EKS on AWS/Azure/GCP	Team or enterprise rollout

Local endpoints

URL	Purpose
http://localhost:3090/dashboard/	Governance dashboard
http://localhost:8080	Interceptor API (/tool-call, /api/v1/*)

Self-hosted (example)

https://acp.your-company.example

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Example: governed tool

import os
from acp import governed_tool

os.environ.setdefault("ACP_INTERCEPTOR_URL", "http://localhost:8080")

@governed_tool(agent_id="supply-chain", tool="supplier_approval")
def supplier_approval(supplier_name: str, risk_score: int):
    return {"supplier": supplier_name, "risk_score": risk_score, "status": "pending_review"}

The AI Control Plane intercepts the call, evaluates policy, then allows, denies, or escalates.

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Example: policy (Rego)

package acp.policy

allow {
    input.identity.role == "supply-chain-manager"
    input.action.tool == "supplier_approval"
    input.risk_score < 70
}

Edit policies in ~/.acp/policies/ after acp init.

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Example: governance flow

Supply Chain Agent
    → ACP — AI Control Plane validates identity (JWT)
    → OPA evaluates policy
    → Decision: ESCALATE
    → Human approves in dashboard
    → Execution resumes

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What the AI Control Plane provides

• Policy enforcement — OPA/Rego (Cedar on roadmap)
• Identity — JWT from Okta, Auth0, Keycloak, or your IdP
• Approvals — human-in-the-loop for risky actions
• Observability — dashboard for decisions, traces, agents, tools
• Agent registry — lightweight catalog of agents and capabilities
• Framework-friendly — keep CrewAI / LangGraph / Strands for reasoning; govern execution here

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Philosophy

Orchestration frameworks handle reasoning, planning, and workflows.

ACP — AI Control Plane handles governance, trust, approvals, policy, and auditability.

Reasoning stays autonomous. Execution stays governed.

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Roadmap

• Gateway / proxy execution mode (production hardening)
• MCP-native governance
• Policy studio and replay
• Enterprise topology views
• Multi-cloud deployment templates

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License

MIT License