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
Does a similar job
all alternatives →
AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317OpenTelemetry-native observability for agents and LLMs
Time travel debugger for web development2021 · replay.io · ▲681Automated Penetration Testing for AI-Built Apps
Oodle.ai – $10 per million agent tracesJul 2026 · oodle.ai · ▲31Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Tracium.aiMar 2026 · tracium.ai · ▲70Hi everyone, thanks for checking out tracium. At work I needed some clarity on how much each agent costs us and how they perform on production so I started looking at existing solutions for this such as LangSmith and LangFuse. Honestly, they felt like they are overcomplicated to integrate and that there should be a simpler solution so I decided to build Tracium. Tracium keeps track of every single AI API call that you make to an LLM provider, with this you can see traces, spans, I/O, tokens in/out, latency. You can compare during A/B testing, see usage from each customer and…
Questions this answers
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Product Hunt launch ↗
Launched alongside, August 2026
the whole month →- TL
Life & fun · Aug 2026 · louisabraham.github.io
Clipto MCP▲655Hey HN, We recently built Clipto. It’s a tool that lets you search over terabytes of video, audio, and images on your computer, without relying on the cloud. Motivation: we probably all had this similar experience, we know a moment exists in a video or audio, but finding it takes hours scrubbing the timeline. You can send all the media to process in the cloud, but it’s slow, expensive and raises privacy concerns. So we decided to build our own on-device media search engine. How it works (high level): 1. We ingest video, audio, image; normalize formats via ffmpeg; run content analysis to…
Work · Aug 2026 · clipto.com
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
Astute▲606Automate your B2B brand going viral, with new media creators
AI · Aug 2026 · company-app.joinastute.com