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Beyond Monitoring: Observability for Generative AI, Agentic AI, and LLM Workloads
Why Complexity Demands a Modern Platform
As generative and agentic AI systems move into production, they introduce significant complexity and unpredictability, making traditional monitoring tools obsolete. The non-deterministic nature of AI, where execution paths constantly shift, creates new risk vectors like spiraling costs, operational blind spots, and the downstream propagation of flaws, demanding a modern approach to visibility. Observability is now a mandatory strategic imperative, requiring early instrumentation and causal explanations to understand why an agent chose a specific path, not just what the outcome was. To manage this, practitioners must adopt modern observability platforms that provide continuous insight, context-rich root cause analysis, and automation across models, prompts, and orchestration layers. Dynatrace is identified as a complete answer with its purpose-built features to provide visibility, guardrails, and automation across AI-infused systems.
Key Highlights
Observability Mandate: Observability is now a mandatory requirement for managing generative AI and agentic systems due to their complexity and non-deterministic behavior.
New AI Risks: Key risks of AI in production include hidden costs from unpredictable token consumption, operational blind spots in decision-making, and "poisoning the well" where one agent's poor output corrupts others.
Monitoring vs. Observability: Unlike traditional monitoring which only confirms a system is "up," observability provides the causal explanations and context linking telemetry across infrastructure, models, prompts, and orchestration layers.
Platform Approach is Essential: Manual dashboards and scripts cannot keep pace with the velocity and compounding complexity of agentic AI; a modern, platform-based observability approach is the only way to succeed.
Practical Recommendations: Practitioners must instrument systems early, prioritize root cause analysis, critically evaluate agentic workflows, and leverage open standards like OpenTelemetry for effective AI governance.
Stephanie Walter | Practice Leader, AI Stack
Stephanie Walter is a results-driven technology executive and analyst in residence with over 20 years leading innovation in Cloud, SaaS, Middleware, Data, and AI. She has guided product life cycles from concept to go-to-market in both senior roles at IBM and fractional executive capacities, blending engineering expertise with business strategy and market insights. From software engineering and architecture to executive product management, Stephanie has driven large-scale transformations, developed technical talent, and solved complex challenges across startup, growth-stage, and enterprise environments.