acp-ai 0.2.8

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

Make sure Docker Desktop is already running before you start ACP.

pip install acp-ai
acp init          # creates starter policies in ~/.acp/policies
acp up
acp dashboard     # open governance UI

Step	Command
Install	pip install acp-ai
Initialize policies	acp init
Start stack	acp up
Open UI	acp dashboard → http://localhost:3090/dashboard/
Stop	acp down

For a real local integration, the usual sequence is:

1. acp init
2. start Docker Desktop
3. acp up
4. export ACP_INTERCEPTOR_URL=http://localhost:8080
5. export ACP_BEARER_TOKEN=...
6. run your governed agent

If Docker is not already running, acp up will fail with Docker daemon / compose connection errors.

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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 runtime -> ACP SDK -> Interceptor / Gateway -> OPA / Rego -> allow | deny | escalate -> governed execution

ACP sits at the execution boundary:

• agent frameworks keep reasoning and orchestration
• the interceptor validates identity, routes policy, and records audit traces
• OPA / Rego returns deterministic allow, deny, or escalate decisions
• only allowed or approved requests reach tools, APIs, MCP servers, or other agents

Request sequence

Typical governed flow:

1. the agent calls a governed function through the ACP SDK
2. the interceptor validates JWT identity and normalizes the request
3. OPA / Rego evaluates policy for the tool, action, and claims
4. ACP records the decision and exposes it in the dashboard
5. the SDK executes only on allow, waits for review on escalate, and blocks on deny

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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

---

Example: governed tool

Set ACP_INTERCEPTOR_URL and ACP_BEARER_TOKEN before defining a @governed_tool.

The decorator constructs its ACP client at decoration time, so setting those env vars later inside main() is too late.

import os
from acp import governed_tool

# Set env vars before decorator evaluation.
os.environ.setdefault("ACP_INTERCEPTOR_URL", "http://localhost:8080")
os.environ.setdefault("ACP_BEARER_TOKEN", "<dev-jwt>")

@governed_tool(
    agent_id="texas-weather-agent",
    tool="weather_api",
    action="read_weather",
)
def get_texas_weather(city: str):
    return {"city": city, "status": "pending_review"}

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

tool="weather_api" is the governed ACP tool id. It does not have to match the Python function name (get_texas_weather). ACP policy keys on tool, not the Python symbol name.

The JWT agent_id claim must match agent_id="texas-weather-agent" in @governed_tool(...).

---

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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Adding a new governed tool

acp init creates starter policy files in ~/.acp/policies/, but you still need to wire new tools into policy yourself.

For a brand-new governed tool, update all of these:

1. choose an ACP tool id, for example weather_api
2. add tool-to-role mapping in ~/.acp/policies/rbac.rego
3. route the tool in ~/.acp/policies/router.rego
4. add or reuse a domain policy file such as weather.rego
5. mint and export ACP_BEARER_TOKEN
6. make sure the JWT agent_id claim matches @governed_tool(agent_id=...)
7. run the agent

Two valid patterns:

Pattern 1: reuse an ACP tool bucket

Use one ACP tool id for multiple Python functions in the same policy domain.

@governed_tool(
    agent_id="texas-weather-agent",
    tool="weather_api",
    action="read_weather",
)
def get_texas_weather(city: str):
    ...

~/.acp/policies/rbac.rego

"weather_api": ["weather"],

~/.acp/policies/router.rego

decision = weather.decision {
    input.tool == "weather_api"
    rbac.decision.decision == "allow"
}

~/.acp/policies/weather.rego

package acp.weather

default decision = {
    "decision": "allow",
    "reason": "weather_action_allowed",
    "policy": "acp/weather",
}

decision = {
    "decision": "deny",
    "reason": "weather_action_not_permitted",
    "policy": "acp/weather",
} {
    not input.action == "read_weather"
    not input.action == "read_weather_batch"
}

Pattern 2: add a brand-new ACP tool id

If you want policy to key directly on a new id such as get_texas_weather, add that id to both rbac.rego and router.rego, then route it to the right domain policy file.

If your starter router.rego or rbac.rego references a tool id, make sure the corresponding domain policy file also exists.

---

Local JWT dev example

Local governed /tool-call requests require a bearer token when JWT auth is enabled. ACP_INTERCEPTOR_URL alone is not enough.

Set:

• ACP_INTERCEPTOR_URL=http://localhost:8080
• ACP_BEARER_TOKEN=<signed-jwt>

If ACP_BEARER_TOKEN is missing, governed calls may fail with 401 Unauthorized / missing bearer token.

One dev-friendly way to mint a token is:

python -m pip install pyjwt cryptography

export ACP_BEARER_TOKEN="$(
python - <<'PY'
from pathlib import Path
import time
import jwt

private_key = (Path.home() / ".acp" / "jwt" / "private.pem").read_text()
token = jwt.encode(
    {
        "sub": "agent:texas-weather-agent",
        "agent_id": "texas-weather-agent",
        "roles": ["weather"],
        "iss": "acp-dev",
        "aud": "acp-interceptor",
        "exp": int(time.time()) + 3600,
    },
    private_key,
    algorithm="RS256",
)
print(token)
PY
)"

Use these claims for local development:

• sub=agent:texas-weather-agent
• agent_id=texas-weather-agent
• roles=["weather"]
• iss=acp-dev
• aud=acp-interceptor

agent_id in the JWT must match the agent_id passed to @governed_tool(...).

---

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