Next-Level Observability.
Optimize AI at Scale.
Splunk Observability helps you build trust in AI with deep context and correlation across your entire stack, so you can understand how a performance issue happening anywhere is impacting your business — and optimize AI at scale. Full control of your data means you own your data; no proprietary vendor agents get in the way. Upfront, predictable pricing means you only pay for what you use, with no bill shock at the end of the month.
Four Concepts. One Cisco Architecture.
Full-stack observability, observability modernization on the Cisco Data Fabric, network observability, and agent observability aren’t four separate products — they’re four capabilities of one platform. Let’s zoom in from the whole Cisco stack, to where this brief focuses, to the detail underneath.
Cisco’s Co-Designed Full Stack
Cisco delivers a full-stack platform with Observability and Security running across every layer — from AI applications down to silicon and optics.
Where This Brief Goes Deep
Cisco Observability sits at the top of the stack — where AI applications & agents, Model, and Data intersect. That’s where the four concepts of this brief live.
Zoomed In: How Cisco Fulfills Each Layer
The three pink Cisco layers map to specific capabilities in this brief. Reveal them bottom-up to walk the story.
There are four concepts on this page we’ll explore — and show you how easy it is to get started with Splunk Observability.
Full-stack observability that surfaces business impact. Deeper business context to solve issues faster, with greater precision, across apps, infrastructure, and user journeys.
02Built on top of the Cisco Data Fabric — one platform to unify cross-domain insights and activate agentic ops from app, to agent, to AI-PoD silicon.
03Pinpoint owned and unowned network impact on app performance and end-user experience — spot bottlenecks in real time across every network path.
04Accurate, low-cost evaluations and guardrails to build trust in AI — from agent to GPU, with Luna SLMs and real-time AI tokenomics.
See the Business Impact of Problems Across Apps, Infrastructure, and Business Processes.
Deeper business context to solve issues faster with greater precision. Modern incidents span security, network, infrastructure, application, and user behavior simultaneously — looking at one domain at a time produces a confident-but-wrong answer. Splunk Observability correlates all of it in one platform, so teams see the actual root cause and the business impact together.
Symptom: Users report slow checkout. 4 monitoring tools are firing. None of them agree on what’s wrong.
- Monitor critical business processes & user journeys
- Troubleshooting & root cause analysis
- Optimize observability costs
- Monitor the entire technology stack
Full-Stack Observability · Click-Through the Splunk Observability Cloud
Sam Ali starts her day with one login. One inventory and topology. One view for alerts. One AI Assistant. And AI Canvas embedded inside Cloud Control — the multiplayer workspace where human operators and AI agents investigate and remediate cross-domain incidents together, grounded in platform data and policy.
Connected Data Makes an AI Co-Worker Possible.
Deeper insights aren’t just for humans. The same cross-domain data foundation that gives your teams full-stack visibility — app, infrastructure, network, digital experience, and business signals in one Cisco Data Fabric — is what gives AI SRE the context to act as a real-time co-worker inside Observability Cloud. It monitors, investigates, and pre-prepares evidence before your team walks into the war room, so your engineers spend their time deciding, not scrambling — and your observability practice is ready for the agentic era.
- Monitor critical business processes & user journeys
- Troubleshooting & root cause analysis
- Optimize observability costs
- Monitor the entire AI stack
Observability Cloud with AI SRE · The Real-Time Co-Worker
Watch AI SRE handle detection, correlation, and evidence-gathering on a live incident inside Observability Cloud. The same cross-domain data foundation that unlocks full-stack insight for your teams is what makes an AI co-worker possible — see it monitor, investigate, and pre-prepare while your engineers focus on the decision.
End-to-End Visibility — From Network Paths to Application Performance to End-User Experience.
Co-sell Observability Cloud + ThousandEyes to spot bottlenecks in real time across network paths, application performance, and end-user experience. Cisco is the only vendor that bridges both layers — the insight layer with Cloud Control, and the management layer with ThousandEyes ↔ Observability Cloud.
- Pinpoint network impact on app performance
- Network device performance monitoring (NPM)
Network-to-Application Integration · Live Walkthrough
Follow a single incident from Cloud Control down into Observability Cloud. See the same signal flow through the Insights layer and the Management layer, with correlated ThousandEyes network telemetry surfacing beside application traces — one story, one team, one system.
Build Trust in AI by Evaluating, Observing, and Controlling Agent Behavior and Token Costs.
Accurate, low-cost evaluations and guardrails to build trust in AI. LLMs and autonomous agents introduce failure modes traditional monitoring cannot see — hallucinations, drift, runaway token spend, wrong-but-confident actions that return a 200 OK. Splunk Observability closes that gap with automated AI evaluation, programmable guardrails, Luna SLMs (proprietary small language models that evaluate at 95% lower cost than LLM-as-a-judge), prompt-level visibility, real-time AI tokenomics, and AI infrastructure monitoring for the underlying GPUs and vector databases.
- Evaluate agent behavior to ensure accuracy
- Monitor the entire AI stack — from agent to GPUs
- Optimize token costs (tokenomics)
- Prevent inaccurate and harmful outputs (guardrails)
Galileo · Agent Observability Platform
AI evaluation for agents and LLMs from dev to production, programmable runtime guardrails that intercept harmful/biased/off-topic responses in milliseconds, Luna SLMs that deliver superior evaluation performance at 95% lower cost than LLM-as-a-judge, prompt-level visibility to spot hallucinations and confidence drops, real-time AI tokenomics (cost-per-interaction and token usage), and AI infrastructure monitoring for the GPUs and vector databases underneath.
Four Demos. One Conversation.
Cross-launch into any of the four demo environments to see the concepts in action.
Concept 01 · Full-Stack Observability
Cloud Control
AgenticOps in action: one login, one topology, one alert view, and AI Canvas (embedded in Cloud Control) where operators and agents resolve cross-domain incidents together.
Launch Cloud Control DemoConcept 02 · Observability Modernization
Observability Cloud with AI SRE
An AI co-worker inside Observability Cloud, powered by the Cisco Data Fabric’s cross-domain data. AI SRE monitors, investigates, and pre-prepares evidence so your team decides faster — with deeper cross-domain insight than any peer platform.
Launch Observability Cloud DemoConcept 03 · Network Observability
Network-to-Application Integration
Walk a single incident from Cloud Control into Observability Cloud. Correlated ThousandEyes telemetry surfaces beside APM traces — the end of “network innocence” escalation loops.
Launch Network Observability DemoConcept 04 · Agent Observability
Galileo
Agent traces, evaluation metrics, and runtime Protect guardrails that stop hallucinations, off-policy actions, and silent failures before they reach customers.
Launch Galileo Demo