Table of Contents
Key things to know
- Most enterprises waste months and significant budget automating the wrong customer interactions first.
- Discovery Agent analyzes your real interactions and other artifacts to identify and prioritize the highest-impact automation opportunities before you build a single workflow.
- It identifies required APIs and knowledge sources, and generates a recommended execution plan, so your team starts with confidence, not assumptions.
- It can power optimization opportunities surfaced in CXP’s ROI dashboard to identify ROI opportunities per intent.
- Discovery Agent is a continuous capability, not a one-time assessment. It keeps surfacing new opportunities as your operation evolves.
- Discovery Agent is one of five new agents in the ASAPP CXP—alongside Developer, Simulation, Optimization, and Insights—each serving a different layer of your CX operation and tied together by Orchestration at the core.
Automation strategy should start with evidence, not instincts
There's a pattern we see consistently across enterprise contact centers. A team decides to invest in customer-facing AI automation. They have real urgency: costs are up, volume is relentless, leadership wants results. So they pick a few use cases that seem promising, and they build.
Six months later, they're troubleshooting containment rates that didn't move the way they expected. The use cases they chose turned out to be harder to automate than expected. Or the volume wasn't quite there. Or the right APIs didn't exist. Or all three.
The automation itself wasn't the problem. The decision about what to automate was.
This is the part of the AI deployment process that doesn't get enough attention. Everyone talks about how to build AI agents. Almost no one talks about how to figure out which intents to automate: which intent to automate first, and what should be the automation roadmap, before committing resources.
The first decision is the most expensive one
When enterprises move quickly to automate without a clear picture of where automation will actually deliver value, the consequences can compound quickly, too.
- Investing engineering time in use cases that turn out to be edge cases.
- Missing high-volume, high-impact intents that were sitting in your interaction repository all along.
- Launching, underperforming against internal expectations, and spending the next quarter defending the program to stakeholders who were already skeptical.
The cost isn't just the wasted development time and effort. It's the internal credibility you lose and the opportunity cost of what you could have been accomplishing.
And as AI adoption accelerates and the number of potential automation opportunities grows, getting this first decision wrong only gets more expensive. Every quarter you spend automating the wrong things is a quarter you're not building toward the operation you actually want.
What's actually been missing
The standard approach to use case selection usually goes one of a few ways: focusing on volume only, focusing on low containment only, gut instinct from ops leadership, a vendor telling you what worked for other customers, or some combination.
None of these give you what you actually need: a data-driven, intent-level view of your specific contact center, ranked by expected automation impact, with the implementation requirements mapped out before you commit to building anything.
Existing tools weren't designed for this. Your CCaaS platform tells you your call volume. Your analytics stack tells you the handle time. Your CRM tells you case categories. But none of them synthesize across those signals and systems to answer the question that actually matters: where should we automate first, and what will it take to get there?
That gap is what we built Discovery Agent to close.
Part of a coordinated set of agents
Last November, we launched CXP to orchestrate the best path to resolution across every customer interaction. This spring, we're introducing its next evolution: five purpose-built agents working as a team. Discovery Agent, Developer Agent, Simulation Agent, Optimization Agent, and Insights Agent. Each agent serves a different layer of your CX operation, shares the same context as all the others, and is coordinated by Orchestration at the core.
Discovery Agent is where that coordination begins, and never really stops. You can't build the right automation without first knowing where it will actually deliver value. And you can't scale intelligently without a continuous signal telling you where to go next.
Introducing Discovery Agent
Discovery Agent runs on the ASAPP Customer Experience Platform. It analyzes your real interactions data—thousands of actual customer conversations—to identify and prioritize the highest-value automation opportunities, based on your enterprise and based on the voice of your customers
Not hypothetical use cases. Not what worked at another enterprise in a different industry. Yours.
The output isn't a report you have to interpret. It's a ranked, actionable list of automation opportunities with the projected impact identified before your team writes a single line of code. And it doesn't stop after the initial analysis. Discovery Agent continues working after deployment, surfacing new opportunities as new interactions occur.
Why we built this now
What kept coming up, across customers and across deployments, was that the hardest part of getting to value wasn't building the automation. It was figuring out which intents to automate.
Teams were spending weeks, sometimes months, in manual discovery cycles. Analysts combing through interaction samples. Ops leaders debating which intents to prioritize. Product and engineering waiting for a decision that kept getting delayed because no one had the data to make it with confidence.
And even when teams did reach a decision, it was often based on incomplete information. Volume data without resolution data. Intent categories without complexity assessments. A sense that a use case should be automatable, without a clear picture of what it would actually take to automate it.
Enterprise CX teams needed a capability that could automate that entire discovery process, and make it continuous, not a one-time exercise.
How it works in practice
Discovery Agent starts by analyzing your historical customer interaction transcripts. Using natural language techniques, it identifies patterns across thousands of conversations: what customers are contacting you about, how those interactions are being resolved today, and where the current gaps are. It can also ingest your current process documentation (meeting notes, documents, and diagrams), and even meeting transcripts.
From there, it does something static reporting can't do—it calculates the expected impact of automation for each intent, before development begins. That means:
- Conversation volume per intent: so you know where the scale opportunity is
- Current resolution and containment rates: so you know where human effort is being absorbed
- Automation feasibility: including whether the intent requires new APIs, existing APIs, new knowledge bases, or some combination
- Projected return on automation: so you can build the internal business case, not just the technical plan
The Snapshot Report presents this as a prioritized list of recommended actions ranked by projected impact, with the implementation requirements already identified. Your team can see immediately which intents will deliver the most value, and what it will actually take to get there.
After deployment, Discovery Agent continues monitoring. As new interactions occur, it surfaces additional automation opportunities automatically, creating a continuous improvement cycle rather than a periodic audit or static report.
What this unlocks for your operation
The most immediate impact is time. Instead of weeks of manual discovery, your team gets a clear, data-backed starting point, fast.
That means the conversation with engineering and IT moves from "What should we build?" to "Here's what we've prioritized and what’s involved."
That shift matters beyond just the first deployment. CX leaders often need to make the case for resource commitment to IT leadership, to finance, sometimes to the C-suite. Discovery Agent delivers the data-driven analysis to make that case with real numbers behind it — projected impact, required integrations, for specific intents — not a vendor's best guess.
For teams responsible for automation strategy, the continuous surfacing of new opportunities means you're never starting from scratch when you're ready to expand. The automation roadmap builds itself based on what's actually happening in your contact center.
And for organizations where the automation team and the agent ops team are operating separately, which is most of them, Discovery Agent creates a shared source of truth. Everyone is looking at the same data, the same prioritization, and the same expected outcomes.
Two scenarios where this changes the outcome
Scenario 1: Picking the right first use case
A telecom operator is planning its first generative AI deployment. Leadership is aligned, IT has signed off, and the team is ready to move. The question is where to start.
Previously, the team would have made this decision based on gut feeling and a few data pulls—probably landing on something like billing inquiries or account changes because they're high-volume and seem manageable.
With Discovery Agent, they can see the full picture in hours. Phone activation, for example, turns out to be 18% of conversation volume, but current containment is low because the resolution path requires specific API connections that aren’t available. Discovery Agent maps those API requirements, calculates the projected containment lift, and flags it as a high-priority target. The team starts there, with a clear implementation plan already in hand.
Scenario 2: Expanding after initial launch
A financial services enterprise has been running GenerativeAgent on three intents for six months. Results are strong and leadership wants to expand, but the team isn't sure which intents to add next.
Discovery Agent has been running continuously since launch. It surfaces two intents that have emerged as high-value targets based on recent interaction data: one with growing volume and low containment and one that existing automation handles partially but frequently escalates. The team has a prioritized expansion plan with expected ROI projections, ready to present to the stakeholders who need to approve the next phase of investment.
A note on how we designed this
We didn't build Discovery Agent to give you a recommendation and then step back. It's designed to stay active, continuously evaluating what's happening in your contact center and what automation could do about it.
That's intentional. The contact center isn't static. Customer behaviors shift. New product and service launches generate new interaction patterns. Seasonal volume changes what's high-priority. A capability that only looks at historical data once, at the start of a deployment, is already out of date by the time you use it.
We also designed it to be honest about implementation requirements. It's not enough to know that an intent has high automation potential if you don't also know what it will actually take to automate it. Discovery Agent surfaces both, so the prioritization reflects not just the opportunity, but the realistic path to getting there.
What this means for how you run CX
The question that's been hardest to answer in enterprise CX—where does automation actually create value for us? — now has a data-driven answer that doesn't require months of internal analysis to produce.
That changes the nature of the conversation between CX leadership and the rest of the organization. You're not advocating for automation in the abstract. You're presenting a specific, prioritized plan with projected outcomes, implementation requirements, and a clear path from first deployment to expanded coverage.
It also changes what "continuous improvement" means in practice. Instead of relying on periodic reviews to find the next automation opportunity, your team has a capability that surfaces them automatically—so the question is never where to go next, only when.
Enterprise AI does not need more pilots built on hunches. It needs a repeatable way to find, prove, and scale value. ASAPP’s Discovery Agent gives CX leaders something they have not had before: confidence before build, and additional ROI signals post go-live.
The enterprises that will get the most out of AI automation in customer service aren't the ones that move the fastest. They're the ones that start in the right place.
Want to see how Discovery Agent would assess your contact center? Talk to an AI CX specialist now.
