Parloa on Reinventing the Customer Experience With AI Agents

Parloa was recently recognized as an AI-Powered Enterprise Agent Solution of the Year for its AI Agent Management Platform, AMP. We sat down with Malte Kosub, CEO and Co-Founder of Parloa, to discuss why agentic AI represents a fundamental shift beyond traditional chatbots, what enterprises need to rethink before deploying AI agents at scale, and how autonomous systems could reshape the relationship between companies and their customers.

Parloa’s AI Agent Management Platform (AMP) was named AI-powered Enterprise Agent Solution of the Year. What does this recognition say about where the market is headed, and what makes AMP's approach to agent management different from the field it beat?

Winning AI-powered Enterprise Agent Solution of the Year confirmed the market has moved past proving AI Agents can hold a conversation and into proving they can run as production infrastructure.

AMP was built to go beyond voice AI layers and deliver a full agent lifecycle at global scale.

AMP unifies the entire lifecycle, design, testing, deployment, and optimization, inside one environment, while most competitors split those stages across separate tools. It also runs an evaluation-first architecture, testing every agent through thousands of synthetic conversations with LLM-as-a-judge scoring before it ever reaches a customer, on a foundation built compliant with GDPR, ISO 27001, and SOC 2. And its Agent Composition feature lets a company build an agent once and deploy it across more than 130 languages and 70 countries without rebuilding for every new market, something competitors still require.

That’s the difference maker for our customers: one platform, one build, full confidence.

Parloa's own research found that 99% of companies failed your first agentic-readiness evaluation. How does AMP close that gap?

Our 2026 research on the State of Agentic CX evaluated thousands or websites, chats and phone trees. The findings were bleak: 43% of company websites gave customers no clear path to support, fewer than 10% of chat conversations actually reached the customer's goal, and the vast majority of voice experiences still ran on decades-old automation or none at all.

The companies that failed our evaluation came down to two consistent markers. Companies are overestimating their own AI-readiness, discovering mid-deployment that they were lacking the APIs, MCP connections and general architecture needed for success. And more critically, many companies misunderstand how to apply agentic AI in the first place, treating it as a conversational version of the same IVR tree they’ve been using for years rather than an opportunity to redesign the customer journey.

AMP solves for both these by running a readiness assessment before any agent hits the field, and surfacing data gaps up front.

How should enterprise leaders think about agentic CX differently from the conversational AI or chatbot investments they may have already made?

Most leaders are still evaluating agentic CX with the same mental model they used for chatbots, and that is the first mistake. A chatbot answers a question and forgets you the moment the session ends. An agent remembers your last issue, your preferences, and your history, and it carries that context across the app, the web, and the phone. That is not a feature upgrade. It is a completely different relationship with the customer.

The old generation of automation ran on fixed branch logic: if this, then that, and it never veered off script. Agentic systems reason. They pick up on tone, pace, and frustration, and they take action instead of just routing a request. What I keep telling leaders is that most agentic pilots look great in isolation, one channel, one use case, and then fall apart the moment agents have to coordinate across backend systems or hand off between channels. That breakdown is an orchestration and infrastructure problem, not a model problem, and no one solves it by buying a bigger chatbot.

Why does that matter?

If you measure agentic CX the way you measured chatbots, by tickets deflected or average handle time, you will optimize for the wrong outcome. The smartest leaders are instead asking whether they made life easier for the customer, and whether that drove loyalty or revenue. If a brand's real goal is pure cost cutting, a high quality AI solution is probably not the right fit for them, and that choice will cost them customer loyalty in the long run. The organizations that treat this as a bigger chatbot budget line end up cutting labor costs without ever changing the actual customer relationship, and they leave the real value on the table.

As enterprises move from AI pilots to production, what will separate the organizations that successfully scale agentic CX from those that remain stuck in experimentation?

The companies stuck in pilot purgatory almost always made the same early decision: they picked a low complexity use case, something like an account balance lookup, because it was easy to prove out. The problem is that proving an easy use case does not tell you anything about how to scale, and it does not force the internal process change that agentic AI actually requires. It just confirms the technology works on something simple.

The organizations that break through, do the opposite. They start by mapping what the end to end customer journey should look like, and only then apply AI to that redesigned journey, instead of layering AI on top of a process that was never built for it. They also stop evaluating vendors on demos. If you hand three or four vendors the same narrow use case, they will all give you a similar answer, and that tells you nothing about which platform can actually deliver value at scale, across languages, load, and architecture, over the long term.

The last piece is governance, and it has to be built in from day one, not bolted on after volume increases. Can the agent explain its own decision? Can it escalate with context? Do your teams have real time visibility into what it is doing and why? If the answer is no, you are not ready to scale, no matter how good the pilot looked.

What’s next for Parloa and AMP as enterprise adoption of AI agents continues to grow?

The phase of ultra cheap, subsidized AI is ending. Compute costs are rising, models are getting more complex, and enterprises expect security, reliability, and customization at scale. That is exactly where we are putting our energy: continuing to build the evaluation and governance layer that lets enterprises trust an agent before it ever reaches a real customer, not just after something breaks.

We also believe the next era of customer experience is personal. Every customer should have an agent that recognizes them instantly, whether they are in the app, on the web, or on the phone, and that agent should already know their history instead of starting over with a stranger every time. That is one of the key drivers we are building toward.

And we are going to keep doing the research that got us here. Our State of Agentic CX work showed us that the market is still early, only about one percent of CX systems today can successfully manage agent to agent interaction. That gap is the opportunity, and closing it for our customers is what comes next for AMP.