Who Watches the AI Agents? — Cisco’s Case for Agentic Observability

Who Watches the AI Agents? Cisco’s Case for Agentic Observability

The enterprise AI conversation has quietly shifted from “can we build an agent” to “can we trust the agents we’ve already built.” This Cisco Live EMEA 2026 session, run by the Outshift by Cisco team, digs straight into that gap — and it’s been rattling around my head since I watched it.

From a single chatbot to an Internet of Agents

The premise is that enterprises aren’t deploying one tidy AI assistant anymore. They’re standing up multi-agent systems — what the session calls MAS — where distributed, interconnected agents hand work to each other. Cisco even has a phrase for it: the “Internet of Agents.” A single chatbot was something you could reason about. A mesh of agents calling other agents, tools, and models is a different animal, and the moment something goes wrong, “which agent, doing what, and why” becomes a genuinely hard question.

Why traditional observability falls short

If you’ve run APM tooling before, you know the usual signals: requests, latency, error rates. Agentic applications break that model. The session argues you also have to track quality — did the agent actually produce a good answer? — along with cost, since tokens add up fast across a multi-agent workflow, and behavior that isn’t deterministic from one run to the next. On top of raw telemetry, the team frames the real goals as explainability, evaluation, predictability, and control: four words that don’t show up on a classic monitoring dashboard.

A new charter for “agentic APM”

The most concrete idea here is a proposed charter for agentic APM — application performance monitoring rebuilt for agents. That means agentic quality and cost tracking, impact assessment when an agent’s behavior shifts, and anomaly detection tuned to agentic patterns rather than HTTP error spikes. The session spends real time on evaluation, too: approaches like LLM-as-a-Judge, where one model grades another’s output, alongside active testing to keep agent performance honest across different deployment scenarios. That evaluation piece is what stuck with me — monitoring tells you something changed, evaluation tells you whether it actually got worse.

Doing it in the open

What makes this more than a product pitch is that Cisco is pushing it as an open standard rather than a closed feature. The work is happening in an open-source collective called Agncty, as an industry collaboration that includes Cisco and Splunk, and it’s being brought to the OpenTelemetry GenAI community for standardization. The session closes with a live demo of end-to-end agentic observability built on Agncty’s open-source components. For anyone who has been burned by monitoring lock-in, an interoperable standard that works across agent frameworks is the right instinct — your observability layer shouldn’t depend on which vendor’s agents you happened to deploy.

Where it fits in Cisco’s AI direction

This lines up neatly with Cisco’s broader AgenticOps story and its Splunk pairing. Cisco clearly wants to own the operational layer of enterprise AI — not just the network and infrastructure underneath the agents, but the tooling that tells you whether those agents are behaving. Observability is an unglamorous place to plant a flag, but it is a sticky one.

My take: this is the part of the agentic AI wave that doesn’t get enough airtime. Everyone is racing to ship agents, and far fewer people are asking how they’ll debug, cost-control, and trust them at scale. Betting on an open standard instead of a proprietary dashboard is a smart move — though standards only matter if the rest of the industry actually shows up, so it is worth watching whether OpenTelemetry adoption follows.

Source: Agentic Observability and Evaluation | Cisco Live EMEA 2026 on YouTube.