Cisco has introduced a new phase in observability by embedding agentic AI into its Splunk Observability portfolio, designed to surface instantaneous, business-focused insights across networks, applications and AI systems. This shift aligns telemetry with business outcomes, automates incident triage, and provides specialized monitoring for LLMs and AI agents. The result is a unified approach that links application health, user experience and cost signals to operational decisions, enabling teams to detect, investigate and remediate issues with far greater speed and context than traditional monitoring stacks.
Cisco Agentic AI Observability Boosts Real-Time Business Insights
The announcement that Cisco has infused Splunk Observability with agentic AI marks a strategic pivot from passive logging and alerting to proactive, outcome-oriented observability. Agentic AI here means systems that do more than surface signals: they act on telemetry to automate collection, tune alerts and recommend or even apply remediations. This capability reframes observability as an operational partner, not just a dashboard.
From an engineering and cyber-resilience viewpoint, the implications are broad. Enterprises running hybrid stacks—spanning cloud-native microservices, legacy three-tier apps and embedded AI services—now require correlated telemetry that respects both technical and business priorities. Cisco’s integration of Splunk AppDynamics and Splunk Observability Cloud aims to provide that correlation, while maintaining compatibility with standards like OpenTelemetry to ease vendor migration and data continuity.
Key technical shifts introduced by agentic AI observability include:
- Automated telemetry orchestration—agents that discover where metrics, traces and logs are missing and orchestrate collection without manual instrumentation.
- Context-aware alerting—alerts prioritized against business impact (e.g., checkout failures vs. background job latency).
- Adaptive remediation guidance—AI-suggested fixes derived from historical incidents and known good baselines.
- AI-aware telemetry—specialized metrics to validate LLM behavior, cost per API call and model drift.
A practical example: a retail platform detects a surge in cart abandonment. Traditional observability would surface elevated error rates and increased latency. Agentic AI augments that by automatically correlating errors to a recent deployment, mapping impacted transactions to revenue-critical business processes, and suggesting a rollback or targeted traffic shaping until a code fix is applied. That business-context prioritization contrasts strongly with many traditional tools that present raw signals without impact ranking.
Comparative vendor context matters. While companies like New Relic, Datadog, Dynatrace, Elastic and IBM Instana have invested heavily in observability and ML-driven anomaly detection, Cisco’s approach emphasizes agentic automation and tighter alignment with business metrics via Splunk AppDynamics capabilities. Simultaneously, specialists such as Honeycomb and Amazon CloudWatch offer strengths in queryability and deep integration with specific cloud platforms—highlighting that multi-tool strategies persist.
List of immediate operational benefits expected:
- Faster mean time to detection (MTTD) through proactive telemetry collection.
- Lower mean time to resolution (MTTR) via AI-guided root cause workflows.
- Reduced alert noise by grouping and summarizing noisy signals into episodes.
- Cost visibility for AI workloads, aligning cloud spend with business outcomes.
For teams facing signal overload, the promise is clear: reduce repetitive toil and redirect engineers toward product innovation. The next section will detail how agentic AI operationalizes incident management and root cause analysis with concrete features and availability.
Cisco Splunk Observability: Agentic AI for Incident Response and Root Cause Analysis
The integration of agentic AI into Splunk Observability is designed to tackle the full incident lifecycle: detection, correlation, investigation and remediation. Several named capabilities illustrate this trend: AI Troubleshooting Agents, Event iQ for automated alert correlation, and ITSI Episode Summarization. Each feature reduces cognitive load on operators while increasing fidelity of insights.
AI Troubleshooting Agents operate within the Observability Cloud and AppDynamics platforms to automatically analyze incidents. They process traces, logs and metrics together to propose likely root causes and remediation steps. These agents can:
- Collect additional telemetry on demand, such as extended traces or debug logs.
- Rank probable root causes using historical incident patterns.
- Suggest prioritized next steps and confidence scores for each recommendation.
Event iQ addresses a perennial problem: alert noise. By grouping related alerts and reducing duplicates, Event iQ creates a coherent incident narrative. For SRE and ITOps teams, this means fewer paged engineers at 2 a.m. and more time for proactive reliability engineering.
ITSI Episode Summarization then converts grouped alerts into consumable summaries showing trends, impacts and hypothesized root causes. This capability speeds handoffs from on-call responders to engineers responsible for long-term fixes.
Feature availability, applicability and competitive mapping
A concise matrix highlights how these agentic observability features align with operational needs and how they map against other market offerings.
| Feature | Operational Benefit | Availability |
|---|---|---|
| AI Troubleshooting Agents | Automated root cause suggestions, telemetry augmentation | Available in Splunk Observability Cloud & AppDynamics (GA/Preview) |
| Event iQ | Alert correlation and noise reduction | Offered in Splunk ITSI (GA) |
| ITSI Episode Summarization | Grouped alert overviews with trends and impact | Alpha/Private Preview for some features |
Operational teams should consider that some capabilities are in alpha or private preview while others are generally available. Planning for phased adoption—starting with non-production environments—helps validate efficacy and governance controls. This staged approach mirrors typical enterprise rollouts, particularly when AI agents are permitted to take automated actions.
Practical scenarios demonstrate value quickly:
- Financial services: automatic correlation of a database migration to transaction failures, with an episode summary for the compliance team.
- Telecom: AI agent pinpoints a configuration drift in a load balancer causing regional outages and recommends a targeted config rollback.
- Healthcare platform: Event iQ suppresses a flurry of related alerts during a scheduled batch job, preventing unnecessary escalations.
These capabilities dovetail with observability best practices and complement existing toolchains from Datadog, Dynatrace and New Relic. Integration with open standards—such as OpenTelemetry—and existing AppDynamics agents allows organizations to adopt agentic observability incrementally while protecting prior investments.
As teams validate these features, governance questions emerge: when should AI agents be allowed to take automated corrective actions, and how should rollback safeguards be constructed? The following sections analyze observability for AI workloads and governance frameworks that operationalize trust.
Cisco Observability for AI: Monitoring LLMs, Agents and Infrastructure at Scale
Observability must adapt to the arrival of LLMs and agentic workflows inside enterprise applications. Monitoring models is materially different from monitoring stateless microservices: models have quality, cost and behavior dimensions that require specialized telemetry. Cisco’s Splunk advancements introduce AI Agent Monitoring and AI Infrastructure Monitoring to surface metrics such as inference latency, token costs, model drift and query quality.
Consider a hypothetical retailer, Aurora Retail, that deploys an LLM-driven recommendation agent across web and call-center channels. Without AI-aware observability, issues like relevance degradation, unexpected hallucinations or cost spikes can go undetected until business KPIs suffer. With agentic observability, Aurora can:
- Track recommendation accuracy by sampling model outputs against known outcomes.
- Alert on semantic drift when training data distribution diverges from production input.
- Monitor per-query cost and throttle expensive inference paths automatically.
Practical metrics to instrument for LLMs and agents include:
- Quality metrics: precision/recall proxies, human feedback ratios, response coherence scores.
- Operational metrics: inference latency percentiles, concurrency limits, retry rates.
- Cost metrics: tokens per request, model selection frequency, GPU-hour consumption by service.
AI Infrastructure Monitoring focuses on the health and consumption of GPUs, model-serving clusters and orchestrators. It alerts on bottlenecks (e.g., GPU saturation) and anticipates spikes that would materially change operational cost. These signals feed into capacity planning and can automatically trigger scale-up or degraded-mode fallback strategies.
A list of recommended steps for teams adopting AI observability:
- Inventory AI assets (models, endpoints, agents) and map to business processes.
- Define quality SLOs for model outputs in business-terms (conversion lift, satisfied queries).
- Instrument telemetry for quality, cost and infrastructure health using OpenTelemetry-compatible collectors.
- Deploy agentic monitors in preview environments to validate alert fidelity before production rollout.
- Establish review gates for automated actions and define human-in-the-loop escalation paths.
Integration with other observability and analytics tools—such as Elastic, Honeycomb and legacy APM solutions—can provide complementary views. For example, Elastic may be used for log-heavy forensic analysis while Honeycomb offers event-driven tracing for high-cardinality use cases where model inputs vary widely.
In practice, monitoring AI at scale is as much organizational as technical. Aurora Retail’s SRE team learns that correlating a 15% drop in recommendation relevance to a data pipeline change reduces revenue loss within a single business day. This demonstrates that observability for AI directly protects revenue and brand reputation, and must be embedded into release and incident management processes.
Key insight: AI observability converts opaque model behavior into actionable operational signals, enabling cost control and quality assurance tied to business outcomes.
Cisco Unified Observability: Correlating Business Impact, Network and User Experience
A major value proposition of Cisco’s approach is unifying application telemetry with network and end-user signals. By combining Splunk AppDynamics, Splunk Observability Cloud and Cisco ThousandEyes, teams can trace a customer-facing performance issue from the browser through the network to backend services and databases. This unified visibility is critical for organizations with global footprints and complex third-party dependencies.
Core capabilities that enable this correlation include:
- Business Insights—links application metrics to business processes like checkout or loan processing.
- Digital Experience Analytics—captures detailed user journey data for product and design teams.
- Session Replay for RUM—records browser and mobile sessions to reproduce and analyze user issues.
- ThousandEyes integration—correlates real-user experience with network performance across owned and third-party domains.
This unified perspective solves a familiar puzzle: is a spike in error rates caused by backend microservices, a CDN provider outage, or a misconfigured client-side release? A consolidated stack reduces time spent switching tools and improves the accuracy of root cause analysis.
Comparisons to competing approaches:
| Capability | Cisco + Splunk | Alternative Strengths |
|---|---|---|
| Network-App Correlation | Deep ThousandEyes integration; end-to-end traces | Datadog and Dynatrace have strong full-stack tracing; ThousandEyes provides network depth |
| User Journey Analytics | Session Replay + Digital Experience Analytics | New Relic and AppDynamics excel in APM UX metrics |
| AI Observability | Specialized AI Agent/Infrastructure monitoring | Emerging capability among Elastic, Honeycomb and vendors focusing on custom instrumentation |
For product teams, the Digital Experience Analytics and session replay features enable fast hypotheses about UX regressions without pager-driven interruptions. For NetOps teams, ThousandEyes’ network telemetry clarifies whether slow API responses are caused by transit providers or by upstream service degradation.
A short list of operational scenarios that benefit:
- Global e-commerce: Correlate region-specific network latency with checkout abandonment and adapt edge routing.
- Financial services: Map transaction timeouts to a third-party payment gateway incident to expedite vendor escalation.
- SaaS platform: Use session replay to reproduce mobile crashes tied to a library upgrade, reducing MTTR.
Interoperability remains a priority. Cisco’s support for OpenTelemetry and the Splunk AppDynamics agent enables data portability and coexistence with other telemetry tools, including New Relic, Datadog, Dynatrace, Elastic and IBM Instana. This reduces lock-in risk and allows organizations to assemble best-of-breed observability architectures.
Final insight for this section: unified observability that ties user experience, networking and business KPIs creates a single source of truth for cross-functional teams, enabling faster, more accurate decisions and fewer escalations across vendor boundaries.
Cisco Operationalizing Agentic AI Observability: Governance, Security and Cost Controls
Operationalizing agentic AI-powered observability requires robust governance frameworks covering security, privacy, cost control and model accountability. Enterprises must balance automation benefits with controls that protect data and maintain auditability. Cisco’s announcement aligns with this need by offering alpha and GA stages for different features, signaling a phased adoption model where sensitive capabilities can be validated before full deployment.
Key governance components to implement:
- Access and action policies: define which agents can execute automated remediation and under what conditions.
- Audit trails: record agent decisions, telemetry snapshots and operator overrides for compliance.
- Security monitoring: ensure AI agents and model endpoints are covered by the security stack to detect misuse or data exfiltration.
- Cost governance: set budgets and enforce model selection rules to prevent runaway inference expenses.
Frameworks such as NIST’s AI security guidance provide a useful starting point. Practical resources and discussions—like those found in industry webinars and technical write-ups—help teams translate frameworks into operational controls. For technical teams seeking implementation examples and broader context, resources on topics ranging from AI security to multi-agent orchestration are useful reads: see curated material on observability architecture, risk management and agentic AI events (NIST AI security frameworks), (agentic AI webinar) and (multi-agent orchestration and reliability).
Recommended operational checklist:
- Run agentic observability features in a limited-scope pilot with clear rollback procedures.
- Define SLOs that combine technical and business metrics to prioritize automation decisions.
- Instrument model inputs and outputs for auditability and drift detection.
- Integrate cost signals into incident playbooks to avoid unintentionally expensive mitigation steps.
- Engage security teams early to map data flows and enforce encryption, masking and retention policies.
A few practical links that offer supplemental insights include research and case studies on AI-driven operations, cybersecurity for agentic systems, and applied observability architectures. Relevant material includes analysis of AI in hospitality, healthcare integrations, and crisis communications for cyberattacks—each showcasing how observability intersects with real-world operations (AI transform hospitality), (AI in healthcare integrations), (crisis communication for cyberattacks).
The role of security-specific observability deserves emphasis. Monitoring for anomalous model input patterns, spikes in token usage, or unexpected outbound requests can reveal compromised agents or supply-chain misuse; resources on AI agents for cyber defense and AI security best practices are increasingly relevant (AI agents cyber defense), (AI security and cybersecurity risk).
Finally, operationalizing agentic AI observability yields direct business benefits: lower incident costs, improved uptime for revenue-critical flows, and more predictable AI expenditures. Appropriate guardrails and staged adoption help organizations realize these gains without compromising security or compliance.
Operational insight: treat observability automation as an enforceable product with SLOs, budgets and auditability baked in—this ensures agentic AI scales reliability while preserving control.