Introduction to agent signals: The key to billing AI agents

As agents take over the discussions, one challenge remains consistent for the businesses building them: how do you effectively monetize agent solutions?

At the core of this shift? Signals.

Signals are a powerful concept that's transforming how AI agent companies measure, demonstrate, and bill for value.

What are signals? The atomic unit of AI work

Signals are (usually) discrete, measurable events that occur when an AI agent performs a meaningful action or reaches a significant milestone.

In the Paid platform, signals are events that come in when an agent has performed a unit of work.

Think of signals as digital stamps (or breadcrumbs!) that mark each valuable action your AI agent takes.

A signal gets generated whenever an agent:

• Completes a specific task
• Reaches a checkpoint in a workflow
• Produces an output
• Interacts with a user or system
• Achieves a meaningful outcome

Each signal captures rich metadata:

• Timestamp
• Agent identifier
• Action type
• Context (customer ID, conversation details)
• Performance data (tokens consumed, processing time, input lengths, costs and margins)

Why we've built our platform around signals

Signals aren't just a technical feature, they’re a business innovation at Paid.

There are 5 key reasons why we’re building them into our core:

1. Bill on what creates value

With signals, you can price and more importantly bill based on:

• Individual workflow steps (charge more for high-value steps)
• Complete outcomes (only bill when value is delivered)
• Volume-based activities (scale with usage)
• Hybrid models (combine subscription + outcomes)

The workflow image above shows how each step in a content creation process becomes a billable unit, with distinct pricing tied to the specific value delivered.

2. Transparency and auditability

In a world of AI black boxes, signals create trust. Your customers see exactly what they're paying for.

You can show the actual work performed rather than vague summaries and entitlements.

3. Value-aligned pricing

Signals let you connect pricing directly to business outcomes that matter.

Each meaningful action becomes a distinct signal. In the medical receptionist example above - from appointment scheduling to preventing no-shows.

This creates multiple potential monetization points that your customers would care about.

4. Intelligent Business Decisions

Signal data provide insights:

• Which agent actions drive the most customer value?
• Where are the performance bottlenecks?
• Where are we not monetizing something we could?
• How do costs compare to revenue for each action across customers and agents?

5. Evolving Business Models

As your AI capabilities grow, your pricing can evolve without rebuilding your entire infrastructure.

Pricing an outcome at $1 that delivers $2 of human-equivalent value creates an obvious win for customers while maintaining healthy margins for you.

Prepare for the future: Figure out your value signals today

Signals vary across different types of AI agent applications.

I thought I’d give some practical examples of signals to monetize:

AI SDRs / Sales AI agents

• Prospecting signals: New lead identified, contact information verified
• Outreach signals: Email drafted, email sent, follow-up scheduled
• Engagement signals: Response received, meeting requested
• Outcome signals: Meeting booked, deal advanced

Document processing agent

• Input signals: Document received, format identified
• Processing signals: Text extracted, information categorized
• Output signals: Summary generated, data entered into system
• Quality signals: Accuracy score, confidence level

Customer support agents

• Conversation signals: Chat initiated, intent identified
• Resolution signals: Answer provided, issue escalated to human
• Satisfaction signals: Positive feedback received, problem resolved

Start your signal strategy today

Practically, here are things you can do now:

1. Map your agent's core value stream - Identify every interaction that creates tangible value
2. Prioritize high-impact signals - Focus on outcomes that customers would willingly pay for
3. Design your signal architecture - Create consistent naming and metadata conventions
4. Start tracking - Ensure signal emission at each critical step
5. Experiment with pricing models - Test activity-based vs. outcome-based approaches - even in a spreadsheet. Are you leaving money on the table?

The question isn't whether to implement signals-based agent monetization, but how quickly you can make them the backbone of your AI business strategy.