Customer monitoring agent

The customer monitoring agent analyzes selected customers and produces a health score with supporting context. It helps you spot accounts that need attention because of payment issues, usage changes, support signals, contract risk, weak communication, or expansion potential. The agent benefits from Hyperline’s revenue management context, including contracts, subscriptions, invoices, payments, and usage. This gives the health score a revenue-aware foundation before it is enriched with connected tools such as Gmail, support platforms, meeting tools, and CRM systems. You can use one monitoring agent for your full customer base, or focus it on a specific customer segment such as enterprise customers, customers near renewal, or customers with late payment behavior.

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Where to find it

Go to Customers > Monitoring to create and manage monitoring agents. After an agent has analyzed a customer, the customer page can show:

• A health score card on the customer overview
• The last monitoring date
• A link to the latest analysis
• The monitoring agent assigned to that customer

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What the agent analyzes

The agent combines billing and customer signals across several dimensions: Connected tools can add more context. For example, Gmail can add recent customer email threads, support tools can add ticket history, meeting tools can add summaries and transcripts, and CRM tools can add account records, deals, contacts, and notes. These dimensions are analysis angles, not a rigid checklist. Depending on the customer context, the agent can emphasize the most relevant angle, such as payment risk, billing issues, expansion readiness, renewal risk, weak communication, or unresolved support issues.

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How context is used in analysis

During each run, Hyperline pulls the relevant context for each monitored customer from Hyperline and connected tools. The agent uses that context to explain the health score, not just to list recent activity. This helps the analysis connect signals that are often reviewed separately:

• A late invoice can be interpreted with payment history, recent emails, open support threads, and renewal context.
• A usage increase can be checked against CRM deal context and support sentiment to surface possible expansion readiness.
• A usage drop can be compared with recent meetings, email silence, or unresolved support issues to identify churn risk.
• A high-value customer can be prioritized differently when contract size, payment behavior, and relationship activity point in different directions.

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Use cases

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Choose a setup mode

When you create a monitoring agent, choose how much control you want over the analysis. Use Autopilot when you want the agent to start monitoring with minimal setup. Use Advanced when you want to adapt the agent to a specific team workflow or customer segment.

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Create a monitoring agent

1. Go to Customers > Monitoring.
2. Click Create agent.
3. Open the agent and go to the Configuration tab.
4. Choose Autopilot or Advanced.
5. Choose which customers the agent monitors:
   • All customers
   • A specific customer segment
6. Click Save changes.

When you change the monitored customer scope, Hyperline asks you to confirm the assignment changes. This helps prevent accidental changes when a segment includes a large number of customers.

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Fine tune the agent

In Advanced mode, use the configuration tabs to customize how the agent works:

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Configure schedule and scoring

In Advanced mode, use Schedule to decide when the agent runs:

• Automatic: Hyperline monitors customers when relevant events happen, such as new emails, late invoices, or usage changes.
• Fixed schedule: the agent runs at a regular interval that you define.

Use Fine tuning to adjust how strongly each dimension contributes to the health score. For example, you can give more importance to Payment health for collections-focused monitoring, to Usage and engagement for churn-risk monitoring, or to Contract and valuation for expansion and renewal reviews.

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Add context

Use the Context tab to give the agent additional instructions:

• General context: company or customer-base context the agent should know.
• Scoring instructions: guidance for interpreting signals, such as industry-specific thresholds or risk rules.

Keep context concise and operational. The best instructions explain how your team interprets customer risk, not generic business goals.

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Enable connectors

Use the Connectors tab to choose which connected tools the agent can use. The tab can include CRM, support, meeting, and custom MCP data sources. Each connected data source has a toggle. Unavailable connectors appear disabled with Connect to enable until the underlying integration is connected. Gmail email context is handled separately. When Gmail is connected for users in your workspace, the agent can use recent customer email threads for matching customer domains. Gmail can appear in Data sources on an analysis result even though it is not toggled from the agent Connectors tab.

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Configure notifications

Use the Notifications tab to decide when and where the agent can ping your team. You can configure:

• Customer owner: ping the user responsible for the monitored customer.
• Customer followers: ping users following the customer.
• Specific people: always ping selected users.
• Noise control: only notify for findings at or above a selected severity.
• Slack threads: post into a Slack channel when Slack is connected.

For more about customer ownership and followers, see Owner and followers.

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Run the agent manually

You can trigger a manual run from the agent page, or from a customer health score card when a customer has not been monitored yet. When triggering a run, you can:

1. Select one or more customers.
2. Adjust scoring weights for that run.
3. Add one-time context for the analysis.
4. Start the run and open the run detail page.

Manual runs are useful before a customer meeting, upsell review, renewal review, collections follow-up, or board reporting cycle.

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Read analysis results

Each completed analysis can include:

• Overall health score
• Score tier
• Short description
• Reasoning
• Dimension scores
• Positive signals
• Risk factors
• Summary
• Data sources and tools used

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Related

• Customer intelligence overview
• Owner and followers
• Custom usage tracking
• Late payment behavior