"There are two complementary revolutions happening simultaneously: Observability for Agentic AI: Providing the visibility needed to facilitate AI adoption by making black-box AI systems transparent and debuggable. AI for Observability: Using intelligent insights and automated actions across complex workflows to transform traditional reactive monitoring into proactive, predictive operations. Observability for AI: Making Black Boxes Transparent Consider a modern AI-powered application where each agent interaction involves multiple of the following:"
"Traditional Application Performance Monitoring (APM) is not able to provide transparency into this complex web of interactions. You may be able to see HTTP request durations but miss the AI decision-making process entirely. To unlock AI's full value, you need deep visibility into these agent-level interactions. At the same time, their performance within the APM entity is also needed in order to quickly identify and resolve issues specific to your AI components."
Agentic orchestration and workflows increase AI productivity across incident response, research, and software development while adding substantial complexity. Monitoring, debugging, and governance become more difficult than with relatively linear LLM systems. Two complementary revolutions arise: observability for agentic AI to make black-box systems transparent and debuggable, and AI for observability to apply intelligent insights and automated actions for proactive, predictive operations. Modern agent interactions commonly include tool calling with external APIs, context passing between agents, parallel processing branches, error handling and retry logic, and performance optimization decisions. Traditional APM cannot reveal AI decision-making, so deep agent-level visibility integrated with performance metrics is required to identify and resolve AI-specific issues and reduce unknowns.
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