Agent Observability Redefines AI Trust at SplunkConf 2026
Agent Observability Expands the AI Trust Conversation
Agent observability is becoming essential as enterprises move AI agents from experiments into production workflows. In this Techstrong TV interview from SplunkConf 2026, Alan Shimel talks with Vikram Chatterji about the new requirements for monitoring, measuring and controlling agent behavior.
Chatterji explains that agentic software is different from traditional applications. It is probabilistic, adaptive and often made up of multiple agents, tools, memory layers and handoffs. That means observability can no longer begin only after production issues appear. It must support the full agent development life cycle.
From Telemetry to Agent Behavior
The conversation explores why OpenTelemetry matters for AI agents. Enterprises need a common language for tracing agent activity across internal systems, third-party agents and complex workflows. Without that standard layer, teams struggle to understand what happened and why it happened.
Agent observability builds on traditional monitoring, but it also adds a different product experience. Teams need to see tool calls, agent-to-agent handoffs, memory use, datasets, evaluations and outcomes. They also need insight into whether an agent is creating risk, wasting resources or drifting from its intended purpose.
Signals, Supervisors and Humans at the Helm
Chatterji describes an insights-driven approach that helps teams find unknown problems inside large volumes of agent data. Instead of asking people to scan endless rows of logs, a supervisory layer can surface suspicious behavior, inefficient tool use, privacy concerns and other signals that deserve attention.
That does not remove people from the process. It changes their role. Human teams stay at the helm, while AI-powered supervisors help them focus on the traces and decisions that matter most.
Tokenomics Brings Cost Control Into Focus
The interview also highlights SplunkConf 2026 announcements around Cisco Data Fabric, Splunk Agent Observability and tokenomics. Chatterji says enterprises are adopting AI agents quickly, even when budgets are under pressure. The challenge is not simply whether to use agents. It is whether the value justifies the cost.
For technology leaders, the takeaway is clear. Agent observability gives teams a way to connect trust, performance, cost and governance. As AI agents scale across the enterprise, that visibility becomes critical for building systems that are reliable, accountable and worth the investment.
