From the Cisco edition of October 29, 2026
Splunk State of Observability 2026: 56% cannot measure AI response quality and 55% cannot control AI costs, in a 2,050-respondent survey
Cisco / Splunk (survey fielded by Oxford Economics) · IT operations and observability (cross-industry, includes government and education respondents) · 10 countries including the United States
- Publisher
- Cisco Investor Relations (PR Newswire release)
- Original publication
- Cisco press release, Oct. 20, 2026
- Source retrieved
- 2026-10-30
What happened
Cisco released its sixth Splunk State of Observability report. Oxford Economics surveyed 2,050 ITOps and engineering professionals in 10 countries and 16 industries, including government (federal, state and local) and education, from February through March 2026. Reported results: 87% say monitoring AI is a priority but 54% lack clear business outcomes tied to it; 56% cannot measure AI workload metrics such as LLM response quality; 85% call hallucination visibility important but only 58% say they are effective at it; 55% say they cannot control AI costs; 60% say unexpected billing spikes cause more strain than high baseline costs; 57% say telemetry costs exceed the infrastructure being monitored; 83% are concerned about unintended consequences of AI agents.
Why it matters
Agencies and campuses piloting AI agents face the same measurement and cost-predictability gaps; the finding that most organizations cannot measure agent quality is a useful caution for SLED pilots. Sector-level results for government and education are not broken out.
Evidence and measured results
Opened the press release and read it in full, including methodology. The full report was not opened.
Limitations and uncertainty
Vendor-sponsored survey, self-reported; private and public sectors combined; government and education results not separated; full report not reviewed; leader-versus-non-leader comparisons are correlational. Transferability caveat: mostly private-sector, multinational sample.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-30; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Public bodies piloting AI agents cannot predict AI costs or measure answer quality.
- Stakeholders
- CIO
- IT operations lead
- Budget office
- CISO
- Discovery questions
- How is AI usage billed and who reviews it?
- Can you measure response quality for current agents?
- Has an unexpected AI or telemetry bill occurred?
- Value hypothesis
- Defining quality and cost metrics early may prevent budget surprises and unsafe agent behaviour going unseen.
- Potential engagement
- Metric-definition and instrumentation workshop for one AI pilot.
- Unsupported claims
- Leader ROI or revenue figures as expected outcomes
- Government-specific results
- Any savings from adopting Splunk
Pre-sales engineering
Role takeaway
- Fit
- Applies to any SLED organization running AI agents or LLM workloads.
- Architecture and integration
- Observability pipeline extended to LLM response quality, hallucination indicators and cost per agent, shared with security tooling.
- Prerequisites
- Inventory of AI workloads
- Billing data access
- Monitoring platform able to ingest AI telemetry
- Constraints
- Self-reported survey
- Sectors combined
- Telemetry costs can grow with coverage
- Security
- Agent telemetry may contain prompts with personal data; set retention and redaction.
- Proof of value
- Detect a simulated billing spike before invoice
- Track a quality metric for one agent for a month
- Measure telemetry cost added
Delivery
Role takeaway
- Work
- Build an AI operations baseline for pilots.
- Dependencies
- Finance thresholds
- Agent owners
- Security team participation
- Ownership
- IT operations or platform team with finance
- Skills and adoption
- Operations staff learn AI-specific metrics; finance learns usage-based billing patterns.
- Governance checkpoints
- Owner and metric assigned before production
- Monthly cost review
- Joint ops-security review of agent incidents
- Acceptance criteria
- Each production agent has owner, quality metric and cost alert
- Cost alerts fire in test
- Telemetry spend tracked
- Risks
- Telemetry cost growth
- Metrics that do not reflect real quality
- Vendor-framed benchmarks
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Agent monitoring needs new metrics (response quality, hallucination, cost per agent/model) beyond traditional application monitoring, and shared data between observability and security teams.
Governance
Who approves, reviews and stays accountable for outcomes?
Ties AI spend to business outcomes; public bodies need budget controls for usage-based AI pricing.
Security and privacy
What data, permissions and controls need testing?
81% say AI monitoring increased collaboration between observability and security teams, reflecting agents’ broad access.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Not addressed.
Procurement
What should contracts, pricing and exit terms secure?
Usage-based AI and telemetry pricing makes spikes, not baselines, the main budget risk; contracts should include caps or alerts.
Operating model
Which teams own the service once it runs?
"Leaders" in the survey report better cost attribution, but the leader definition and causality are vendor-framed.
Publication history
- 2026-10-29Cisco · Issue 0111 resources
Stable resource ID: splunk-state-of-observability-2026-agent-monitoring-ai-cost-gaps-20261020