Business challenges
As organizations move from experimenting with generative AI to deploying agentic AI in production, new operational challenges have emerged. AI systems are no longer isolated models, they are dynamic workflows of agents, LLMs and services making decisions in real-time. Traditional observability tools were not designed for systems that reason, evolve and act autonomously, leaving teams without the visibility and context needed to ensure performance, control cost, and maintain trust.
AI applications continuously evolve as agents, models and dependencies change, making it difficult to understand what is running and how components interact.
Teams lack consistent ways to measure output quality, relevance and accuracy across AI workflows, relying on manual review and spot-checking.
AI systems can drift over time, with changes in latency, outputs and token usage that are difficult to detect before impacting users or budgets.
Teams cannot easily understand how or why agents make decisions, making it difficult to troubleshoot workflows or ensure accountability and governance.
The Instana AI Agent and LLM Observability solution
IBM Instana provides full-stack observability for AI-powered applications, extending existing GenAI Observability capabilities with deeper visibility into agent behavior, decision-making and business impact.
Instana automatically discovers AI components, traces end-to-end workflows across agents and services and correlates performance, cost and quality signals in a unified solution. With built-in evaluations, adaptive baselining and task-level visibility into agent reasoning, teams can continuously monitor, understand and optimize AI systems in production.
The result is a shift from reactive troubleshooting to proactive, governed AI operations, enabling teams to manage performance, control cost, and build trust in AI at scale.
Features
Monitor latency, throughput, token usage and cost across models and services. Identify cost drivers, optimize usage and align AI performance with operational goals.
Trace requests across agents, LLM calls, tools and traditional services. Visualize complete workflows and understand how AI components interact within the full application context.
Track token usage and modeled cost at granular levels to optimize models, prompts and tenancy. Use these insights to prevent overruns and align expenditure with performance objectives.
Using built-in evaluations, assess AI outputs for accuracy, relevance and consistency. Automatically learn normal behavior to detect drift, anomalies and performance changes over time.
Understand how agents make decisions by visualizing multi-step workflows, tool usage and LLM interactions. Diagnose issues faster with full context into how outcomes are generated.
See how it works with an interactive demo
Benefits
Resources
Get complimentary access to the full Gartner Magic Quadrant report and explore how the Observability Platforms market is evolving, what to look for in a modern observability solution, and why IBM is a trusted choice.
See how AI Agents transform anomaly detection & resolution.
See how AI agents and LLMs predict and prevent IT issues in real-time.
Related use cases
Frequently asked questions
IBM Instana automatically traces every AI request across the entire AI workflow - from the user prompt to model inference and any downstream services, without requiring manual instrumentation. Instana maps all dependencies, correlates latency and error patterns, and highlights issues like slow inference, token bottlenecks, degraded GPU performance, or prompt failures.
Instana provides detailed visibility into token usage and cost across models, services, and workflows, helping teams identify cost drivers, optimize usage and prevent unexpected spend.
Instana combines real-time monitoring with continuous evaluations and adaptive baselining to detect performance issues, drift and anomalies early, ensuring AI systems remain reliable and aligned with intended outcomes.
Yes. Instana provides unified full-stack observability across AI components and traditional services, enabling end-to-end visibility and faster troubleshooting across hybrid applications.
IBM Instana captures AI-specific metrics such as prompt execution time, inference latency, token counts, model routing behavior, embedding generation time, and vector database retrieval latency. It also surfaces errors like context-window limits, malformed prompts, and timeout events, helping teams monitor both model behavior and surrounding services.
Instana visualizes every step in a RAG or multi-model pipeline, including embedding services, vector stores, API calls, LLM endpoints, and downstream microservices. Its analytics automatically identify the root cause of issues such as slow retrieval, fallback loops, model saturation, or API bottlenecks, making troubleshooting more efficiently.