As organizations move beyond AI experimentation and begin allowing agents to take actions on behalf of employees and customers, visibility into those systems is becoming a business necessity. Companies need to understand what AI agents are doing, if they are behaving as intended and if they are operating outside established guardrails.
That need is driving a new focus on observability for AI, Hathi says.
“If you want agentic AI to be practically useful in an enterprise or in an organization, you need to make sure that it scales, that it’s able to deliver results and that you can trust it,” Hathi tells BizTech.
Observability Becomes the Foundation for Trust
Enterprises face three major challenges as agentic AI adoption accelerates, Hathi says: operating at machine scale, operating at machine speed and building trust in AI systems.
The first challenge is managing the explosion of telemetry and machine data generated by AI agents, applications and infrastructure. Organizations need ways to collect and analyze that information while controlling costs and making the data useful for AI-driven operations.
READ MORE: Here are three ways AI is revolutionizing customer experience.
The second challenge is responding to events at machine speed. As AI-generated code, automated workflows and AI-powered threats become more common, security and operations teams need tools that can keep pace with rapidly changing environments.
The third challenge is trust.
“The same unified visibility and telemetry allows us to start looking and seeing what the AI is actually doing,” Hathi says. “Do we understand what it’s supposed to do versus what it’s actually doing?”
For Hathi, observability is the foundation that makes autonomous AI possible. Without visibility into agent behavior, organizations cannot confidently determine whether AI systems are delivering value, operating safely or staying within established business rules.
AI Autonomy Requires Guardrails
The answer is not to limit AI agents, Hathi says, but to create the guardrails needed to manage them.
Using the customer refund example, organizations must establish clear expectations for what an agent is supposed to do and continuously evaluate whether it is behaving accordingly. Those evaluations then become the basis for governance and control.
“To have autonomy, you need to be able to both understand and control what the agent is doing,” Hathi says.
