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Progress AI Observability

Trace, evaluate, and improve AI agents in production

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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.

PH · launched Aug 7, 2026 · 168 upvotes (top 87%) · 0 views

Niche: ai agent observability platforms · 222 launches, 7 in the last 7 days ▼ -50% · Explore this niche →

Category and tags

  • Horizontal
  • Observability & eval
  • Developer
  • AI-native
  • Commercial product
  • SaaS
  • Software Engineering
  • Artificial Intelligence

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Could you build this?

Partial Creating the basic dashboard and SDK wrapper around LLM calls is accessible, but building high-throughput OpenTelemetry ingestion, deterministic automated evaluation for agent reasoning, and real-time hallucination detection requires specialized backend and ML evaluation pipelines.

What it would actually take: Requires a scalable distributed time-series and trace backend (e.g., ClickHouse, OpenTelemetry collector) capable of handling millions of spans per minute with minimal latency overhead on user applications. The evaluation engine needs specialized RAG/agent groundness classifiers, token drift analytics, and fine-tuned lightweight judge models to grade agent execution steps reliably without adding prohibitive token latency/cost.

Competitors

Other products that read as similar to this one : 1270 launches clear the similarity bar, closest 8 shown.

Attention rank: #1127 of 1271 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 280 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.