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Progress AI Observability — Trace, evaluate, and improve AI agents in production

telerik.com

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What it does

The Progress Agent Engineering Platform captures trace-level data from AI agents and LLM applications, including prompts, responses, model calls, retrieval steps, tool use, latency, token usage, and cost signals. It enables teams to debug failures, monitor production costs, evaluate output quality, and optimize workflows across .NET, Python, and JavaScript.

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Two ratings

SiteList review score

Leverages a massive developer footprint to establish trust. Currently hampered by a 7MB homepage payload and design technical debt.

Reviewed on Aug 28, 2026

Why this score? →

Screenshots

Captured during our latest review on Aug 28, 2026

Home · www.telerik.com
Home · www.telerik.com
Products · www.telerik.com
Devcraft · www.telerik.com

Key features

  • AI Tracing

    Observability

    Shows the full execution path behind AI responses, including prompts, model calls, retrieval, tool use, latency, and outputs.

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  • Cost Analysis

    Monitoring

    Connects cost and token usage to real execution traces so teams can identify what is driving spend before the next invoice.

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  • LLM Evaluations

    Evaluation

    Uses evaluator models to score AI outputs against quality criteria such as relevance, helpfulness, groundedness, safety, or task completion.

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  • Workflow Debugging

    Debugging

    Investigates skipped tools, failed retrieval paths, agent loops, and bad context with trace-level evidence to resolve AI-specific failure modes.

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Who it’s for

  • Debugging AI Agent Failures

    Review full execution paths across prompts, model calls, retrieval, tool use, and workflow steps to identify failure modes.

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  • Monitoring Production AI Costs

    Understand what is driving token spend and invoice increases by connecting costs to real execution traces.

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  • Improving AI Output Quality

    Use production traces to identify weak responses, compare prompt or model changes, and build better evaluation coverage over time.

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Integrations

  • Semantic Kernel

    developer tools

    First-class native integration for .NET agent development and orchestration workflows.

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  • Azure

    cloud infrastructure

    Designed to fit naturally into Microsoft-ecosystem teams already using Azure services.

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Buyer questions

Questions buyers actually ask

Does this add latency to my agent workflows?

The Progress Agent Engineering Platform is designed to be lightweight and asynchronous, so instrumentation does not meaningfully impact agent execution or user-facing latency.

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What kinds of AI agents does this support?

It's built for production-grade AI agents, including single-agent workflows, multi-agent systems, tool-using agents, RAG pipelines, copilots and customer-facing assistants.

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Product FAQ information · View source

The expert review

Progress AI Observability earns a 75/100, leveraging a massive developer footprint to establish trust in the AI observability space. The site demonstrates deep technical understanding but is hampered by a 7MB homepage payload and design technical debt.

75/100: Strong enterprise foundation with significant performance debt.

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Scores across 13 dimensions

Brand & experience

Performance

Content & editorial

Trust & decision surfaces

Search & discoverability

Docs & measurement

Domain
telerik.com
Category
Monitoring & observability
Platforms
web, api
Founded
1991
HQ
Burlington, US
Last checked
Aug 28, 2026
Public dimensions
13

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