As customer experience (CX) teams push beyond basic chatbot deployments, a new model is emerging: coordinated multi-agent AI systems that can execute end-to-end workflows across teams, tools, and policies. In this interview, Rob Wilkinson speaks with Raj Koneru, Founder and CEO at Kore.ai, about why this shift is happening now and what leaders should demand before they trust AI with real customer-facing execution.
Koneru argues that the last 12 months have changed the game because AI models have improved dramatically in both generation and reasoning. That progress moves automation beyond question answering and into task completion, where agents can take action across systems and processes. In regulated and complex environments, he says, a single agent is limited by context and scope. A multi-agent approach better mirrors how organizations actually operate, with specialized functions like billing, fraud checks, fulfillment, and escalation requiring orchestration.
Koneru also outlines what tends to fail in single-bot deployments: inconsistent answers, broken handoffs between bots and humans, actions taken without enough context, and heavy cleanup work for frontline teams. Risk and compliance leaders, he adds, often struggle because prompt chains offer limited traceability and control.
For CX leaders, he recommends insisting on deterministic policy enforcement, clear permission boundaries, human escalation controls, and runtime observability before going live. He emphasizes that governance should not be bolted on after deployment. It needs to be embedded into the platform and runtime so teams can reproduce, audit, and optimize outcomes.
Finally, Koneru shares the production metrics that matter: non-negotiables like failure recovery, auditability, and compliance, plus business outcomes like resolution rates, customer effort reduction, and time to resolution. He also highlights the “soft” impact of better experiences on brand loyalty and long-term value.
