What is an AI agent control panel?
An AI agent control panel is the dashboard a team uses to see every AI agent it runs in production (runs, tokens, cost, errors and health) and to act on those agents: pause or stop one, switch its model or roll out an approved prompt, without redeploying the app it runs in.
Control panel or control plane?
The two terms describe layers of the same thing. The control plane is the system underneath: the server that stores agent state, the SDK inside your app, signed command delivery, permissions and the audit log. The control panel is the interface on top of it, where people see what their agents are doing and decide what happens next. Agent Control Panel (ACP) is both: a control plane with the panel your team operates from.
A note on the name: “ACP” is also the abbreviation of unrelated agent protocols, such as the Agent Client Protocol that connects code editors to coding agents. Agent Control Panel is a product for operating production agents, not a protocol.
What an agent control panel shows and does
- Inventory: every connected app and agent, its model and its status (idle, running, paused or stopped).
- Health: heartbeats every 30 seconds, so a silent agent is visible, plus a health score per agent.
- Runs and errors: each run with duration, tokens, model, input and output, and the error when it failed.
- Cost: token usage and spend per agent and app.
- Commands: pause, resume, stop, restart and model changes; prompt updates that an owner or admin approves first.
- Automation: alert rules on a daily spend cap, error rate, spend anomalies or a lost heartbeat that notify you and can attempt an automatic pause, within the app’s control scope.
- Audit: who sent which command, when, and whether it was delivered.
The details are in the documentation: monitoring, runs and errors, tokens and cost, controlling agents and alerts.
How it compares with other agent tooling
Teams often search for “agent monitoring”, “agent observability”, “agent orchestration” and “agent control plane” as if they were one category. They are different jobs, and most production teams end up using more than one.
| Category | Main job | In the model call path? | What it changes at run time | Examples |
|---|---|---|---|---|
| LLM observability | Record traces, runs and cost; evaluate quality | No: an SDK or OpenTelemetry export | Nothing directly; some also serve versioned prompts your code fetches | Langfuse, LangSmith |
| AI gateway or proxy | Route, limit and cache model requests in one place | Yes: every request passes through it | The requests that pass through it | LiteLLM Proxy, Portkey AI Gateway |
| Orchestration framework | Define the agent: its steps, tools and hand-offs | It is the agent | Behaviour, through code and a deploy | LangGraph, CrewAI, OpenAI Agents SDK |
| Runtime policy engine | Check outputs and tool calls against policies at each step | Yes: at each checked step | Individual steps: block, steer or warn | Agent Control (open source, by Galileo) |
| Agent control panel | See and operate agents already in production | No: an SDK beside the call that fails open | Agent state: pause, stop, resume, model, approved prompt | Agent Control Panel |
These layers fit together. A framework builds the agent, observability gives deep traces and evaluations, a gateway centralises provider traffic, a policy engine checks individual steps, and a control panel lets the people responsible for the agent stop it, switch its model or roll out a fix in seconds. Agent Control Panel works with agents built on any framework, because its SDK wraps the model call you already make.
Where Agent Control Panel sits
- Beside the model call, not in it. Your agent calls its provider directly with your own keys, which stay in your app; the SDK only sends telemetry and receives commands.
- Fail-open. If ACP is slow or unreachable, your agents keep running and reporting resumes later. The flip side: a command cannot reach an agent while ACP is unreachable, so keep your own local way to disable an agent as well, such as an environment variable.
- Signed, scoped commands. Commands are HMAC-SHA256 signed with a timestamp and a single-use nonce. A new app starts in monitor scope (telemetry only); control allows pause, resume, stop, restart and model changes; full adds prompt updates, which still need approval.
- Honest timing. A pause prevents the next model call; it does not cancel a request already in flight.
- Two SDKs, one contract.
npm install agent-control-panelfor Node.js andpip install agent-control-panelfor Python 3.10+, both Apache-2.0.
When you do not need one
- You run a single prototype agent and the developer who wrote it is the only operator.
- You only need traces and evaluations while you build: an observability tool is the right start.
- You need every tool call checked before it executes: that is a policy engine or gateway in the call path, which can run alongside a control panel.
Ready to try it? Connect an existing app in a few minutes, or read how to add a kill switch first.
Frequently asked questions
What is an AI agent control panel?
The dashboard a team uses to see every AI agent it runs in production (runs, tokens, cost, errors and health) and to act on them: pause or stop an agent, switch its model or roll out an approved prompt, without redeploying.
Is an agent control panel the same as an agent control plane?
They are layers of the same thing. The control plane is the system underneath (server, SDK, signed commands, permissions, audit); the control panel is the interface people use to operate agents through it.
Is it the same as LLM observability?
No. Observability records and evaluates what agents did. A control panel also changes what happens next, for example pausing an agent or switching its model.
Do I need to change my agent framework or model provider?
No. The SDK wraps the model call you already make, with any framework, and your agent keeps calling its provider directly with your own keys.
Is Agent Control Panel related to the Agent Client Protocol?
No. Both are abbreviated ACP, but the Agent Client Protocol connects code editors to coding agents. Agent Control Panel is a product for monitoring and operating production AI agents.
Related guides
- Switch an AI agent’s model without redeploying: Move an agent to another model, or another provider, in seconds, and what to check first.
- How to monitor AI agents in production: The signals that matter (liveness, errors, cost, quality) and what to do when one moves.
- Agent Control Panel vs Langfuse and LangSmith: Observing agents versus operating them, and when to use both.
- Roll out a new AI agent prompt safely: Versioning, review, approval, testing and rollback for prompts in production.
- Daily spend caps and automatic pause for AI agents: Stop runaway token spend before the invoice: caps, loop detection and safe defaults.
- What is an agent control plane?: How it differs from observability and gateways, and when you need one.
- AI agent kill switch: How to stop or pause an agent in production, and what a trustworthy switch needs.
Next: connect an existing app, read the documentation, or request early access.