What is an agent control plane?
An agent control plane is the system that lets a team see and change what its AI agents do while they run: which agents exist, how they behave and what they cost, and the ability to pause them, switch models or change prompts, under clear permissions and with an audit trail.
Control plane versus observability
Observability tells you what happened: traces, runs, tokens, costs and errors after the fact. A control plane also changes what happens next. Monitoring shows an agent in a failure loop; a control plane pauses it. Most teams start with observability and reach for control the first time an agent misbehaves in front of customers.
| Question | Observability | Control plane |
|---|---|---|
| What did the agent do? | Yes | Yes |
| What did it cost? | Yes | Yes |
| Stop or pause it now | No | Yes |
| Switch its model without a deploy | No | Yes |
| Push an approved prompt | Some tools serve versioned prompts your code fetches | Yes, with approval |
| Act automatically on spend or errors | Alerts only | Alerts and automatic pause |
The parts of an agent control plane
- Inventory: every app and agent, with owner, model and status.
- Telemetry: runs, tokens, cost, errors and heartbeats reported by each agent.
- Commands: pause, resume, stop, restart, model and prompt updates, delivered securely to the running app.
- Policy: scopes that limit which commands each app accepts, and approvals for risky changes.
- Automation: rules that notify or pause on thresholds such as spend or error rate.
- Audit: an append-only record of who changed what and when.
Where it sits: beside the model call, or in the middle
A gateway or proxy sits in the model call path: every request goes through it, which lets it block traffic but also makes it a new dependency and usually means handing it your provider keys. A control plane like Agent Control Panel (ACP) sits beside the call: the agent calls its provider directly, a small SDK reports telemetry and receives signed commands, and if the control plane is unreachable the agent keeps working. The trade-off is that enforcement happens in your process (the SDK refuses the next call), not in a network hop.
When you need one
- An agent talks to customers or takes actions with real consequences.
- More than one agent or app is in production, and nobody has the full picture.
- Model spend matters, or a provider outage would hurt.
- Someone other than the original developer needs to be able to stop an agent.
Agent Control Panel is a control plane for agents you already shipped: connect an app with the Node or Python SDK, then monitor and operate every agent from one dashboard. See also how to add a kill switch and how a control panel relates to observability, gateways and orchestration frameworks.
Frequently asked questions
What is an agent control plane?
The system that lets a team see and change what its AI agents do while they run: inventory, telemetry, commands such as pause or model swap, permissions, approvals, automation and an audit trail.
How is it different from LLM observability?
Observability shows what happened. A control plane also changes what happens next, for example pausing an agent, switching its model or pushing an approved prompt.
Does a control plane have to proxy my model calls?
No. ACP sits beside the call: your agent calls its provider directly and a small SDK reports telemetry and receives signed commands.
Which languages does ACP support?
Node.js and Python 3.10+ through official SDKs (npm and PyPI, Apache-2.0), plus a plain HTTP API.
Related guides
- What is an AI agent control panel?: How it differs from a control plane, observability, gateways and orchestration frameworks.
- 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.
- 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.