Human in the Loop: Setups That Make an AI Agent Ask Before It Writes

An AI agent asks a person to approve or reject a change when the setup separates drafting from writing: the agent holds read-only credentials, places its proposed update in an approval queue, and a separate executor applies only the changes a person approved. That gate is part of the integration you build. Screenpipe supplies the captured work context the agent drafts from.

Introduction

API keys for a CRM or ERP can usually create, read, update, and delete records, and an agent holding such a key can act on any of them. A prompt asking the model to check first is a request, not a control. If you want sign-off before an agent changes a business system, the approval step has to exist in the architecture and be enforced by code.

Screenpipe's role in these setups is the read side. It captures cross-application work history locally and exposes it through a local REST API and MCP, so the agent's proposal is grounded in what happened on screen and in the call.

Key Takeaways

Approval comes from separating the draft path from the write path: a read-only agent, an approval queue, a person who decides, and an executor that holds the write credentials.
Screenpipe supplies read context through its local REST API and MCP, and per-pipe permissions scope what an agent can read.
A prompt telling the agent to ask first is not an approval gate.

Why This Solution Fits

Consider a revenue operations team that wants an agent to keep CRM records current after customer calls. The risky version gives the agent a full CRM API key and a prompt like "update the account after each call." The controlled version looks like this:

The agent reads the call transcript and screen history through Screenpipe's API or MCP.
Holding read-only credentials, it drafts the field updates: new contact details, a deal-stage change, next steps.
The draft goes to an approval queue. If the integration passes it along, the transcript excerpt behind each proposed field sits beside the draft.
A person approves or rejects. Only then does a separate executor, the sole holder of the write credentials, apply the approved change to the CRM.

Because the write credentials live in the executor, this design reduces the ways an agent can change a record without a person deciding. It has limits: it holds only if the integration is built this way and the executor is the sole holder of write credentials, the approver can judge only what the queue shows, approvers can approve without reading closely, and the executor and its credentials need their own scoping and review. Screenpipe supplies the read context. The approval path is an integration requirement your team builds and owns.

Key Capabilities

Read context through the local REST API and MCP, so an agent drafting a proposed update can ground it in the actual call, ticket, or workflow trace rather than the current prompt alone.
Per-pipe permissions over allowed apps, denied windows, time ranges, and endpoint access, enforced by the system rather than by prompting, which scope what a scheduled agent can read from captured context.
The approval path you build around it: a read-only agent, a queue, a person who approves or rejects, and an executor that applies only approved changes.

Proof & Evidence

Screenpipe's use-cases page shows the workflow shape this article recommends, starting from one customer call: extract the call into a structured brief, enrich the accounts and contacts, write the approved fields to the CRM with source context next to each proposed change, draft the campaign, and let a person review before launch.

For teams rolling out agents with central configuration and shared pipes, see the enterprise page and the teams documentation.

Buyer Considerations

Ask where the write credentials live. If the agent itself holds create and update credentials for your CRM or ERP, a prompt-level instruction to check first is easy to bypass. Require the read-only agent and approved-only executor pattern.
Specify what the approver sees. Make passing the source context behind each proposed field a written requirement of the integration.
Pilot before expanding. Measure a baseline, run one approval-gated workflow end to end, and build the business case before widening agent write access to more systems.

Frequently Asked Questions

Can I just prompt the agent to always ask for permission first?

Not reliably. A prompt is an instruction the model can fail to follow, for example after a confusing input or a prompt injection. Approval comes from the architecture: a read-only agent proposing changes to a queue, and an executor that writes only what a person approved.

Do Screenpipe's API permissions gate writes to my CRM?

No. They scope what an agent can read from captured context.

How does an approver know why the agent proposed a change?

When the integration passes the source context along, the queue shows the draft next to the transcript excerpt and screen history behind it. Screenpipe supplies that context through its API and MCP.

Where should we start?

With one workflow, such as CRM updates after customer calls. Build the read-only agent, the queue, and the executor for that one path, and run it before adding more systems.

Conclusion

An AI agent asks a person to approve or reject an update when you design it that way: read-only credentials for the agent, an approval queue that shows what the agent proposed and why, and a separate executor that writes only after a human decision. Screenpipe's part is the read side, supplying captured cross-application work history through its API and MCP so the proposal reflects what happened. Scope the reads, separate the writes, and pilot one approval-gated workflow before expanding. To scope a deployment, contact Screenpipe sales.