Agentic AI and the new architecture of the contact center

Customers can smell autopilot from a mile away.

They open a chat, explain the issue, get marched through a script, finally reach a human and then have to start all over. That’s not self-service. It’s friction wrapped in a friendly UI.

As a Chief Customer Officer, I see the wider impact of that experience every day. When automation often prioritizes containment over resolution, customers lose trust and human agents inherit the fallout. The cost isn’t just longer handling times or lower CSAT. It’s repeat contacts, churn risk, and teams stuck managing frustration instead of solving problems.

At Dialpad, we’ve built around a simple principle: Automation should help customers reach an outcome, not block them from it. AI is powerful, but only when people stay in control. That means clear guardrails, real accountability, and a clean, contextual handoff to a human when it matters.

For too long, the contact center industry treated automation as the end goal: Deflect the ticket, reduce headcount, move faster. But customers don’t wake up hoping to be “contained.” They want a resolution. When they can’t get one, you don’t just irritate them. You erode trust, and trust is expensive to earn back.

That’s why agentic AI represents an architectural shift for the contact center. It moves us beyond scripted interactions toward systems designed to actually get work done.

From chatbots to agentic AI: Moving from answers to outcomes

Most “AI in customer service” has been about producing answers: Fetch a help‑center snippet, summarize a policy, draft something for an agent to paste.

Agentic AI is different: It’s optimized for outcomes. It takes a request, reasons through the steps, and executes the work across your systems, inside the guardrails you define. That’s why I keep calling agentic AI an architectural shift. The contact center stops being a place where conversations go to wait, and becomes a place where work gets done.

Deflection is easy. Resolution is hard. It’s simple to push someone toward an article or a menu. It’s harder (and far more valuable) to resolve end‑to‑end.

Our stance at Dialpad is straightforward: Don’t use AI to avoid your customers. Use it to solve their problems.

What an agentic contact center actually does

An agentic contact center isn’t a “better chatbot.” It’s a system built around repeatable customer journeys and the actions required to complete them.

In practice, that means reusable building blocks (skills and workflows) that handle common requests like scheduling changes, order lookups, account updates, or lead qualification. The point isn’t to sound human. The point is to deliver a human‑quality outcome.

And when a situation requires a person, for compliance reasons or because it’s sensitive, complex, or emotional, the handoff shouldn’t feel like failure. It should feel like the system did the right thing at the right time. The agent receives full context so the customer never repeats themselves.

The endgame: Pre‑emptive customer service

Even “great support” is reactive if it only starts after the customer hits a wall. The real opportunity is pre‑emptive service: Anticipate needs, catch issues earlier, and resolve them before they turn into tickets.

Agentic AI makes that possible because it isn’t limited to conversation. It can monitor signals, trigger workflows, coordinate across tools, and close loops. That’s how you go from “we answered fast” to “the customer never had to ask.”

Non‑negotiables: Trust, safety, and enterprise control

If an AI system can act, the bar goes up: Trust becomes the product.

That means clear policies, permissions, and oversight. It means monitoring and guardrails. It means redacting sensitive data, enforcing governance, and operating predictably under real‑world conditions.

And it means a working override–always. People should be in control of the customer experience, not the other way around.

Don’t bolt it on. Build for it.

A lot of AI “pilots” stall because the architecture is a science-fair of vendors and brittle integrations. My view is simple: You shouldn’t have to reinvent the wheel. The value is in putting AI into production, not in assembling it.

Agentic AI isn’t a feature you sprinkle on top of yesterday’s platform. It works best when it’s designed in: Conversation, workflow, governance, and human handoff operating as one system.

What changes for live agents

There’s a myth that automation replaces agents. The reality: The best automation elevates them.

As agentic AI handles routine workflows, humans spend more time where they’re uniquely valuable: Complex exceptions, nuanced judgment, and empathy‑heavy conversations. That’s better for customers and better for teams.

We’ve believed for years that real‑time AI makes people better at their jobs, not just faster. Agentic systems don’t eliminate human expertise. In fact, they depend on it. Humans set the standards, define the guardrails, and teach the system what “good” looks like.

A pragmatic adoption plan for CX leaders

If you want to move from hype to measurable progress:

• Start with one high‑volume journey. Pick a workflow that’s common and measurable, like status checks, scheduling changes, or basic account updates.

• Design the handoff like it’s the product. Make escalation seamless and contextual. The goal is to avoid starting over.

• Put guardrails in before you scale. Define policies, permissions, and monitoring up front. Treat this like production software, because it is.

• Measure outcomes, not just containment. Track time‑to‑resolution, repeat contacts, customer effort, CSAT, and agent workload, not only deflection rate.

• Scale through reusable building blocks. Once one journey works, replicate the pattern across more workflows instead of rebuilding from scratch.

Where agentic AI takes the contact center next

The contact center is the proving ground for agentic systems because it’s where customer conversations intersect with real business processes. When AI can act safely and humans can override cleanly, you unlock a step-function improvement in customer experience and operational efficiency.

What’s different about this wave is that enterprise leaders are leaning in early. They understand the stakes: Customer trust, data governance, brand risk, and the cost of getting it wrong. The winners won’t be the teams with the flashiest demo. They’ll be the teams that deliver reliable systems customers actually want to use.