Measuring AI Agent Success: An Enterprise Guide

Written by:

September 17, 2025

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5 min read

You've built and tested your AI Agent, and you’re ready to go live.

Now comes the next step in developing fully optimized AI Agents: understanding how it’s performing.

Unlike human agents, who might catch you up on their day’s performance in an informal check-in, AI Agents communicate performance through data alone.

That’s why having the right data, where you need it, and when you need it, is essential for monitoring AI Agents—and ultimately the difference between an AI Agent that meets targets and one that excels beyond them.

In this guide, we’ll help you monitor every element of your agent in Regal—from the predictability of its prompt logic, to the customer’s experience, to the KPIs it drives for your business.

We’ll touch on every layer of Regal’s reporting engine:

Real-Time Visibility: How is my AI Agent performing right now?
Performance Reporting: How are KPIs trending over time?
Tailored Insights: What specific patterns are driving my AI Agent outcomes?
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With these monitoring layers in place, you can deploy Regal AI agents with confidence and optimize them with ease.

1. Monitor deployments live with real-time visibility

When you're running AI agents that handle hundreds or thousands of conversations daily, you need immediate visibility into performance. Without it, you run the risk of the agent mishandling objections, providing inaccurate answers, and degrading the customer experience on a large volume of live calls.

Whether you've just deployed your agent for the first time or are tweaking a long-standing agent, Regal’s cross-platform live metrics (updated intraday, every 10 seconds) give you the visibility that you need to monitor conversation volume and quality in real time—to take immediate action to ensure new agents are performing to grade, and existing agents aren’t causing regression.

Ensure Performance is on Track with Live Cross-Agent Snapshot

Whether you’ve just launched an AI agent or are fine-tuning performance mid-day, Regal Live provides you with an overview of all things live stats across your agents, campaigns, queues, and journeys.

This is your source of truth for understanding how things are going right now: You can catch regressions or unexpected behaviors before they impact performance at scale, make live decisions on where priority changes need to be made, and instantly know which agents can safely be handed more volume.

You can use the live dashboard to compare completed tasks and number of conversations across all active agents, and confirm that agents are actively dialing, handling conversations, and driving the intended outcomes (i.e., transferring qualified leads to licensed agents).

With Regal Live, you can answer critical questions like:

“Is my qualification AI Agent on track to reach 1,000 connected leads by end of day?”

See real-time pacing against your targets and compare throughput to human agents handling similar work.
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“What percent of sales conversations are dropping off early, and why?”

Break down dispositions (e.g. "Not Interested", "Disconnected", "No Answer") to quickly spot trends in drop-offs and what’s driving them.
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“Is introducing AI Agents decreasing speed-to-dial and inbound call abandonment rate?”

Use aggregated live stats to compare operational metrics pre- and post-AI rollout, and understand how that impacts customer experience and engagement.

Drill Into Agent-Level Metrics to Instantly Identify Top Performers

The Agents page lets you dive deeper into agent-specific metrics to identify your top-performing AI agents, spot where underperforming agents need adjustments, and validate that your agents are driving helpful outcomes on a call-by-call level.

‍Some examples of what you can monitor on this page:

In lead qualification, monitor call conversations and conversations over 15 seconds to confirm agents are creating meaningful interactions and not just driving short hangups
In multi-step acquisition calls, track transfers and transfer rate to validate that qualified contacts are advancing smoothly and catch early if an agent is struggling with objections or outdated prompts.
In engagement monitoring, watch receptiveness to AI in real time to spot drops in willingness to engage, signaling when tone, messaging, or agent intros might need adjustment.
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Where real-time visibility shines:

First deployment pulse check: You've just set your AI Agent live for the first time and want to ensure it's operational - making calls, handling conversations, and transferring contacts as expected.
Validating AI Agent Tweaks: You've made a tweak to your agent's prompt and want to immediately see if it improves performance or causes any unexpected issues.
Performance benchmarking: You have human agents working alongside AI agents and want to compare performance in real-time.
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2. Tie AI Agent Impact Directly to Key KPIs Trends

Fully understanding your AI agent's performance requires deep analysis, historical tracking, and the ability to tie metrics to your business outcomes.

Regal provides the comprehensive data and analytics needed to know whether your agents are helping you successfully meet and scale your KPIs.

With it, you can answer questions like:

Are my agents improving things like conversion efficiency, volume, and sentiment over time?
How are different agents impacting overall transfer rate, talk time, and scheduled callbacks?
Which recent prompt or routing changes directly improved key customer engagement metrics?
Are AI Agents driving us toward target KPIs—like increased pipeline, reduced response time, or higher lead engagement?
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Reporting That Mirrors Your Unique Success Signals

For each AI agent you build, you'll automatically get access to a curated Reporting dashboard that tracks the metrics that precisely reflect how you measure and track towards key KPIs.

Regal’s implementation team works with you to tailor these dashboards to your specific use case and success metrics.

These dashboards allow you to track high-level KPIs like:

Answer rates (outbound and inbound)
Transfer rates
Scheduled callbacks
Conversion rates
Call duration and talk time
Short hangups
Contact and agent Sentiment

This Reporting does not just give you a static number for your top KPIs, it’s also designed to identify trends, while letting you drill-down to surface distinct takeaways quickly:

Timeseries Graphs: Track how key metrics evolve as you make improvements, helping you identify what changes are actually working.
Call-level drill-downs: Isolate specific call recordings for manual review when you spot concerning trends or want to double down on success patterns.

Where customizable reporting shines:

A/B testing AI Agent versions: You’re testing different AI Agent versions to identify which prompt changes, voice settings, or other variations drive the highest customer satisfaction, resolution rate, or conversion rate.
Tracking trends over time to validate improvements: You want to see how KPIs evolve as you iterate on your agent to ensure you’re trending in the right direction.
Human vs. AI Agent outcome comparison: You’re comparing your AI agent performance against human agent benchmarks to confirm that your AI Agent is matching or exceeding targets.

3. Uncover Deeper Patterns and Insights with Custom AI Analysis

Regal's Reporting capabilities allow you to go beyond KPIs to uncover specific topical and performance patterns, as well as the conversational signals driving performance.

While standard performance metrics tell you what happened, metrics that are tailored to your agent’s specific use case, industry, and goals help point to why it happened—unlocking the ability to make surgical improvements.

In Regal, you can track custom, unique data points across calls to identify topical and sentiment related patterns, and surface the in-call data points that drive those patterns.

Extract Only the Data that Drives Your Decisions

With Custom AI Analysis, you define the unique data points that get extracted from AI Agent conversations—specific objections, compliance language, product or competitor mentions. Regal automatically analyzes post-call transcripts using an LLM to capture this data.

This leaves you with both in-aggregate and call-level insights that are fully tailored to your use case, your language, and your unique call outcomes. So you know exactly what’s working, and where to prioritize scale and needed updates.

For example, with an inbound lead qualification agent, you may care not only whether a call was transferred, but also:

Did the contact mention a competitor?
Did the AI Agent mention key disclaimers?
What specific objections did the contact raise?
What specific products did they mention?

These Custom AI Analysis data points are accessible in Reporting, so you can monitor them in-aggregate, and identify patterns down to precise data points instantly.

Define your Custom AI Analysis variables to track patterns in conversation topics, identify common dropoff points in your flow, watch for concerns (AI Agent compliance adherence, contact reluctance to AI interaction, objections raised), and more.

Automatically Spot Topical Patterns to Identify Successes and Gaps

The above monitoring layers are powerful—if you know what to look for. But what if you don’t? What if your AI Agent is failing for reasons you haven’t thought to track? What if critical patterns are buried under thousands of call transcripts?

That’s where Regal Improve comes in.

Regal Improve uses LLMs and unsupervised clustering to automatically extract the most relevant moments from calls and group them into topics and subtopics.

With no setup required, it identifies patterns across your AI agent conversations, helping you spot what's working and where gaps exist, and helping you prioritize and know where to drive improvement in your agent builds.

Regal Improve automatically categorizes conversations into topics like pricing objections, product questions, or technical issues, with further detail in sub-topic breakdowns.

For example, when monitoring competitor mentions, you could:

Compare how mentions of different competitors generally affect call outcomes (i.e. higher transfer rates when Competitor A is mentioned vs. Competitor B, which could signal how well the AI handles objections for different competitors).
Compare how certain mentions per competitor affect call outcomes (i.e. higher abandon rate when the caller mentions Competitor A plus pricing, versus Competitor A plus product availability). This signals how well the AI is handling different questions within the context of a single competitor.
Compare specific product or pricing mentions across all competitors (i.e. higher abandon rates when the caller mentions any competitor plus product availability, versus any competitor plus pricing).
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Within each topic and sub-topic, you can review associated key metrics (talk time, sentiment) to understand which topics are resulting in positive versus negative call outcomes.

Where tailored insights shine:

Optimizing business-specific outcomes: You want to make sure your AI Agent is set up to handle the nuances of conversations specific to your business - whether that's compliance requirements, customer intent signals, or competitor mentions.
Knowledge base gap identification: Your AI Agent is using a knowledge base (or perhaps isn’t but needs one), and you're looking to see which topics it could be better equipped to handle.
Strategically targeting calls for manual review: Instead of randomly sampling recordings, automatically prioritize the specific conversations most likely to provide insights for improving your agent.
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Confident AI Agent Monitoring Starts Here

With real-time data, historical reporting, and tailored insights, Regal provides comprehensive monitoring that turns every conversation into an opportunity for improvement.

This means that not only can you feel confident that you’re on top of every AI Agent deployment and tweak, but you’ll also have the right data to continuously optimize your agents.

Ready to build AI Agents that can reach their full potential? Get started with a Regal demo now.

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