AI Observability

Introduction

AI Observability is the discipline of providing end-to-end visibility by collecting, analyzing, and correlating telemetry through AI systems models, Agents, LLM’s, decisions, workflows, and infrastructure to ensure they remain reliable, transparent, performant, cost-efficient and trustworthy in production.

Full stack Observability for AI systems are critical when working with managed AI platforms like Azure AI Foundry, Amazon Bedrock, OpenAI etc.

Challenges

AI systems fail differently than traditional code.

• The “Silent Failure” Problem: A traditional app crashes and throws a 500 Error. An AI model will confidentially return a wrong answer (a hallucination) without any error code. You won’t know it’s failing until a user complains.
• The Black Box: Deep learning models and LLMs are opaque. You cannot simply “read the code” to see where the logic broke; you have to infer it from millions of parameters.
• Data Volume & Velocity: Real-time AI (like recommendation engines) processes massive streams of data. Manually checking logs is impossible; you need automated statistical analysis.
• Regulatory Pressure: With the EU AI Act and GDPR, you are now legally required to explain why your AI made a decision. “The algorithm did it” is no longer a valid legal defense.

AI System Layers

• User/Experience Layer: The interface through which end users or systems interact with AI capabilities. Ex: Chatbot, assistance, API’s consumed by other systems
• Agentic Layer: Decision-making and autonomous execution layer in an AI system that allows AI models to act, plan, use tools, and interact with external systems.
• Application/Orchestration Layer: The control layer that manages AI workflows, prompt execution, tool usage, and decision logic. Ex: RAG pipelines, business logic etc.
• Model Layer: The core intelligence layer where trained machine learning or language models generate predictions or responses. Ex: LLM’s, SLM’s, ML Models etc.
• Data Layer: The foundational layer that stores, processes, and supplies data for training and inference. Ex: vector database, streaming pipelines etc
• Infrastructure Layer: The compute and runtime environment that hosts, scales, and operates AI workloads. Ex: Kubernetes, GPU’s etc

Benefits of AI Observability

• User experience/Application layer: Monitor Response latency, Error rate, Token usage, feedback signals etc
• Application/Orchestration Layer: Monitor Prompt versions, Hallucination patterns, Context injection issues.
• Agentic Layer: Monitor Tool invocation failures, reasoning steps & memory states.
• Model Layer: Monitor model drift, Token usage, cost, failure pattern etc.
• Data Layer: Monitor Data drift, pipeline failures, Retrieval accuracy etc
• Infrastructure: Monitor pod health, memory leaks, GPU usage, storage, network, cost impact etc.