Krazimo CEO Akhil Verghese sits down with TMCnet to discuss one of the most pressing challenges facing enterprise technology leaders today: how to rigorously evaluate AI agents before trusting them with business-critical workflows. The conversation addresses the fundamental trust deficit that exists between the promise of agentic AI and the reality of deploying autonomous systems in production environments.
Verghese explains why traditional software evaluation methods fall short when applied to AI agents. Because large language models produce non-deterministic outputs, enterprises need new testing frameworks that go beyond standard QA. Krazimo’s approach — grounded in the same engineering rigor Verghese practiced during six years as a senior software engineer at Google — centers on deterministic workflow design, modular agent architecture, and robust evaluation pipelines that measure accuracy, consistency, and edge-case handling before any agent touches live data.
The interview covers Krazimo’s phased deployment methodology: starting with shadow launches where the AI operates in parallel with human workers, progressing to human-in-the-loop validation where the agent performs the task but a human approves the output, and only moving to full automation once performance matches or exceeds human baselines over a sustained period. This approach applies across use cases — from AI-powered CRM automation and customer service bots to intelligent document processing and multi-agent orchestration systems.
For enterprise buyers evaluating AI development agencies, AI consulting firms, or building internal AI capabilities, Verghese provides a clear framework: demand outcome-based contracts, insist on phased rollouts with measurable checkpoints, and treat any vendor who skips testing and governance as a red flag.
Read the full interview on TMCnet →