How to bring observability to LLM workflows

As AI agents become more complex, traditional observability tools fall short. Lumigo’s new LLM Observability offering brings deep visibility to your AI workflows. Here’s what’s coming:

  • Full input/output visibility
    Track system prompts, user prompts, and LLM responses for every call—no more black boxes.

  • True cost tracing
    Understand and optimize the cost of each LLM call with full payload correlation.

  • Data-rich exploration
    Filter and analyze by model, latency, tags, and custom dimensions to uncover insights.

  • Agent call graph navigation
    Dive into internal calls and decision graphs to pinpoint breakdowns and bottlenecks fast.

This is the beta phase—your feedback will shape the product.

Orr Weinstein
Orr Weinstein
Orr is VP of Product at Lumigo and has been building products for nearly two decades in various dev and product roles. Orr has formerly held leading Product roles at AWS, GCP, and Spot.io and was lead developer at the IDF and RAM Engineering. Orr holds a Master of Business Administration (M.B.A.) from The University of Chicago Booth School of Business and a Bachelor's Degree in Economics and Management from The Open University of Israel.
Danilio
Danilo Poccia
Danilo works with startups and companies of any size to support their innovation. In his role as Chief Evangelist (EMEA) at Amazon Web Services, he leverages his experience to help people bring their ideas to life, focusing on serverless architectures and event-driven programming, and on the technical and business impact of machine learning and edge computing. He is the author of AWS Lambda in Action from Manning.