Learn what autoinstrumentation is, how it works, and how it enables observability for LLM applications, AI agents, and modern AI infrastructure.

Portkey's resource offers insights, tutorials, and resources for web developers and designers. Readers can learn about frontend development, user experience design, and web development tools. With articles, tutorials, and design showcases, Portkey provides  guidance and expertise for creating modern and responsive web applications.

portkey

Autoinstrumentation automatically captures telemetry from LLM applications and AI agent workflows without requiring developers to add custom monitoring code. It operates at the SDK, agent framework, or gateway layer, collecting prompts, responses, token usage, latency, and tool calls. For multi-step AI agents, it links individual model calls into a single trace showing the full workflow. This is especially valuable in production where manual logging is inconsistent and debugging LLM failures requires structured, contextual data. Portkey's AI Gateway implements autoinstrumentation at the infrastructure layer, centralizing observability across providers and models without code changes.

What is autoinstrumentation?

How autoinstrumentation works in LLM systems