Why observability must evolve into a system of control
As enterprises move AI agents from experimentation into business-critical workflows, the challenge is shifting from proving that the technology works to establishing whether it can be trusted to operate with autonomy.
For CIOs, this means conventional application and infrastructure monitoring is no longer sufficient. Agentic systems can access data, invoke APIs, make decisions, hand off tasks to other agents and trigger business actions. Understanding whether an agent is behaving as intended requires visibility across this entire chain.
Prashant Chaudhary, Area Vice President, India, Splunk, says the biggest barrier to scaling AI is confidence, which depends on having sufficient operational context.
“Enterprises need to see what their agents are doing, what they cost and whether their actions can be trusted,” he says. “Many organisations can monitor individual applications and infrastructure but cannot trace an agent’s complete journey — what data it accessed, which identity it used, which tools or APIs...
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