Misryoum outlines three practical steps to deploy AI agents responsibly: govern data access, evaluate outputs, and start small.
AI agents are inching closer to “human-level” capabilities, but the real work for businesses starts long before an agent is ever deployed: you have to control what it can access, prove what it produces is correct, and build in a way you can actually manage.
Misryoum reports that enterprises are grappling with a familiar set of CFO-style questions as agentic AI moves from demos to operations.. Can you keep it under control. can you verify that its output is genuinely useful. and how will costs behave as usage grows?. The path forward is less about replacing entire workflows and more about putting practical safeguards around the agent’s permissions. decision-making. and rollout.
One of the biggest levers is governance, which starts with data access rules.. In this model. an AI agent can do far more than answer questions: it may connect to corporate systems. pull from databases. and trigger actions across external tools.. That makes “do no harm” a design requirement, especially when information is sensitive.. Misryoum notes that governance typically focuses on making access selective and enforceable. so an agent can only retrieve what a user is allowed to see. not “whatever the model might guess” from prompts.
This matters because agent failures often don’t look like obvious bugs. Instead, they show up as privacy leaks, incorrect personalization, or responses that mix the wrong context with the right intent.
The second best practice is evaluation for correctness, not just quality at the surface level.. Misryoum highlights that evaluation needs to happen throughout the agent’s workflow. including intermediate steps. and it should involve the right expertise for the domain.. When organizations can’t validate outputs properly, they risk treating confident language as accuracy, which undermines trust and stalls production.
This matters because evaluation is what turns agent experiments into reliable systems. Without it, teams end up debugging “after the fact,” when it’s too late to correct the process that produced the answer.
Finally, Misryoum emphasizes starting small to maximize efficiency and payoff.. Instead of attempting to replace an entire ERP-like workflow with a single leap. teams should build smaller. more atomic capabilities that can be tested and governed individually.. Over time, those components can be combined into a broader “confederation of capabilities” that still remains verifiable and controllable.
This approach also helps with cost discipline. since governance and evaluation set the boundaries for what gets built and how safely it can scale.. Misryoum’s takeaway is clear: clean data organization and a cautious rollout pace can speed development. while reducing the risk that an agent becomes expensive to run and hard to trust.