Building AI Agents Is Easy—Transforming Enterprise Workflows Is the Hard Part

Why It Matters

Integrating AI agents into core workflows will redefine enterprise software, delivering faster ROI while demanding new governance and hybrid development expertise.

Key Takeaways

• Building AI agents is easy; integrating them into workflows is hard.
• ROI appears fastest in customer‑experience automation via contact‑center reduction.
• Hybrid approach: combine deterministic business rules with generative LLM responses.
• Enterprises need new governance, observability, and specialist skill sets.
• AI‑generated code will enable continuously evolving, business‑driven applications.

Summary

In this Techstrong.AI interview, Core.ai CEO Raj Conru argues that while constructing AI agents has become relatively straightforward, the real challenge lies in embedding them within existing enterprise workflows. He frames the conversation around three primary ROI‑driven use‑cases—customer‑experience automation, employee‑experience automation, and large‑scale process automation—highlighting that contact‑center improvements deliver the quickest financial returns. Conru stresses that successful deployments require a hybrid architecture: deterministic business‑rule engines guarantee compliance, while large language models provide natural, conversational responses. He warns against fully agentic designs for regulated sectors, noting that generative models can hallucinate and jeopardize critical processes. Governance, traceability, and ROI measurement emerge as recurring concerns across all categories. Illustrating the future, Conru describes Core.ai’s “agent blueprint language,” which lets AI design, code, test, and continuously refine applications. The platform can simulate every possible user interaction, achieve near‑complete test coverage, and auto‑scale deployments based on traffic patterns—turning software into a self‑evolving service rather than a static product. The implication for businesses is profound: legacy packaged software will give way to AI‑crafted, process‑centric applications that adapt on the fly. Companies must invest in new skill sets, governance frameworks, and hybrid tooling to capture value and avoid costly missteps as the industry shifts from code‑first to agent‑first development.

Original Description

In this Techstrong.ai Leadership Insight Series interview, Mike Vizard speaks with Kore.ai CEO Raj Koneru about why building AI agents is only the beginning of enterprise transformation. As organizations move beyond experimentation, the real challenge is applying agentic AI to customer experience, employee experience and process automation in ways that are scalable, governed and tied to real business outcomes.

Koneru explains why enterprises need to balance deterministic rules with reasoning-based AI as they modernize applications and workflows. The conversation also explores why a model-agnostic, cloud-agnostic, data-agnostic and experience-agnostic strategy can give organizations the flexibility they need to govern agentic AI at scale while adapting to a rapidly changing technology landscape.

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