Progress AI Observability
Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Progress AI Observability launched on August 7, 2026 with 168 votes, #180 of 23,274 launches that month and more than 99% of that year's launches.
In plain words
Progress AI Observability is a debugging and monitoring platform for AI agents running in production. It traces agent executions to identify failures, hallucinations, and ungrounded responses that standard monitoring tools miss. The platform helps teams reduce token waste, improve output quality, and identify workflow breakdowns across AI agents, LLM applications, RAG systems, and copilots. It supports .NET, Python, and JavaScript environments, enabling faster deployment of production-ready AI systems.
written from the facts on this page · September 2026
From the sources
Debug and monitor AI agent failures in minutes. Trace every run, catch hallucinations and ungrounded answers that traditional monitoring misses, and see exactly what went wrong. Reduce token waste, improve agent quality, and ship faster with support forNET, Python, and JavaScript.
Debug and monitor AI agent failures in minutes. Cut token waste and ship with .NET, Python, and JavaScript support.
See where workflows break, control output quality and cost, and improve with evidence. Built for.NET, Python, and JavaScript teams shipping reliable AI agents. The answer looks right. The trace shows what actually happened: a made-up refund policy, exposed card data, and cost the team did not see coming. Your agent will ask for your API key. Get your API Key here . See where workflows fail, what they cost, and which agent steps need attention. Test agent behavior before and after release so teams can catch regressions and optimize with confidence. Control how agents behave, what they access, and when teams need to review or intervene. Manage the context agents retrieve, reuse, share, and…from telerik.com
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