Drive operational efficiency with GenAI observability

This announcement follows the preview availability of IBM Instana Intelligent Incident Investigation, which is built to use Agentic AI, to provide faster, more comprehensive root cause identification. As organizations scale large language models (LLMs) and agentic workflows into production, the operational challenges can multiply: debugging opaque AI pipelines, controlling unpredictable token costs and ensuring reliable customer experiences. Instana GenAI Observability directly addresses these issues with a unified, enterprise-ready solution.

Enterprise IT is already struggling under the weight of complexity with a recent report showing two of the top three most important observability-related tasks to complete include adapting observability tools to handle new architectural patterns and ensuring consistent observability coverage across multiple environments.

In addition to this AI has completely redefined what an application is. No longer are applications well scoped and user driven with predefined outcomes; they are self-directed, goal-driven, and include contextual reasoning. The stakes are high. The average cost of unplanned IT downtime at USD 14,056 per minute—equating to USD 218K–1.425M per hour depending on company size, and without visibility into AI-driven systems, organizations can risk runaway costs and reputational damage.

Highly automated enterprises show the way forward, when using observability tools that can potentially achieve a reduction in IT costs and a high ROI from AI-powered digital transformation.

IBM Instana GenAI Observability is built for platform engineers, SRE and IT Ops teams, and executives who must balance reliability and cost of AI features. It centralizes AI-specific telemetry alongside the rest of the IT stack:

• Debuggability of AI workflows: End-to-end traces of agents, tool calls, retrieval steps, retries, prompts, and outputs.
• Operational KPIs: Latency, error rates and throughput via tokens consumed per request, service, model, or tenant.
• Cost governance: Early adopters highlight Instana’s ability to prevent token “bill shock” by attributing spend to workloads and tenants.
• Unified context: AI telemetry isn’t siloed—it is deeply fused with application and infrastructure designed for faster root cause analysis.

The observability landscape is heating up, with many vendors layering AI visibility into their tools. We believe Instana stands apart with four core differentiators:

1. Real-time full-stack context: One product integrates applications, infrastructure, and AI signals.
2. Open-source first approach: Instrumentation through OpenLLMetry keeps data portable and vendor-neutral.
3. Broad ecosystem coverage: From IBM watsonx.ai and Amazon Bedrock to agentic frameworks like LangChain and CrewAI, to vector databases like Milvus DB, alongside  major large language model providers  
4. Next-gen troubleshooting views backed by IBM Research: Purpose-built visualizations reveal AI performance patterns, using IBM patented technology.

Customers want a single place to view AI performance in production—from prompts to tokens to latency—without losing end-to-end context. That’s exactly what Instana GenAI Observability delivers.

For teams using a leading observability solution, the payoff can be significant: reduced mean time to resolution (MTTR), fewer surprise costs. For executives, such a solution can support governance and operational intelligence to scale AI. And for industries facing surging IT complexity, it represents a way to address costs while accelerating innovation.

Looking forward, Instana’s real-time observability is an important part of IBM’s broader AI strategy. Together with offerings like watsonx.governance and watsonx Orchestrate, which provide customizable, domain-specific agents, IBM is building a consistent approach to monitoring, governance and automation across the AI lifecycle.

IBM Instana GenAI Observability is available now. If you’re attending IBM TechXchange conference in Orlando, FL learn more about these capabilities by attending the session #3122, “Drive operational efficiency with AI-powered observability”.

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