SynopsisThe earliest wave of agentic AI deployments has produced a strange pattern: the technology demos beautifully and stalls in production. Models can classify tickets, summarize threads and recommend next actions in seconds, but the business outcome — a closed support case, a renewed contract, a saved customer — keeps slipping out of reach. The gap isn't really about model quality. It's about everything the agent depends on once it tries to do something useful.
Ahmed Bashir of DevRev sits down with Mike Vizard to dig into where that breakdown actually happens. His argument is that ROI from agentic AI lives or dies on six unglamorous foundations: trusted data, durable memory, scoped permissions, precision, safety and the ability to take actions inside the systems people already use. Miss any one of them and the agent reverts to being a fancy chatbot — interesting in a demo, irrelevant inside a real workflow.
Bashir gets into the practical mechanics of getting those foundations right. Trusted data means an agent reasoning over a clean, governed source of record rather than guessing from stale snapshots. Memory means continuity across interactions instead of starting from scratch every conversation. Permissions and precision matter because an agent operating with too much access — or too little context — produces the same bad outcome from opposite directions.
The other thread is organizational. Leaders chasing top-line “AI strategy” goals end up with vague pilots; the ones tying agentic work to specific line-of-business outcomes are the ones actually moving the needle. Meeting employees inside the tools they already use, building trust in the answers before expanding scope, and treating safety as a first-class design constraint are what turn agentic AI from a perpetual experiment into something that compounds value across the business.
