The AI review score
Leverages a massive developer footprint to establish trust. Currently hampered by a 7MB homepage payload and design technical debt.
13 dimensions
Reviewed on Aug 28, 2026
Progress AI Observability — Trace, evaluate, and improve AI agents in production
telerik.com
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Claim itWhat 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.
Two ratings
Verified user rating
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Screenshots
Captured during our latest review on Aug 28, 2026
Key features
AI Tracing
Observability
Shows the full execution path behind AI responses, including prompts, model calls, retrieval, tool use, latency, and outputs.
Cost Analysis
Monitoring
Connects cost and token usage to real execution traces so teams can identify what is driving spend before the next invoice.
LLM Evaluations
Evaluation
Uses evaluator models to score AI outputs against quality criteria such as relevance, helpfulness, groundedness, safety, or task completion.
Workflow Debugging
Debugging
Investigates skipped tools, failed retrieval paths, agent loops, and bad context with trace-level evidence to resolve AI-specific failure modes.
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.
Monitoring Production AI Costs
Understand what is driving token spend and invoice increases by connecting costs to real execution traces.
Improving AI Output Quality
Use production traces to identify weak responses, compare prompt or model changes, and build better evaluation coverage over time.
Integrations
Semantic Kernel
developer tools
First-class native integration for .NET agent development and orchestration workflows.
Azure
cloud infrastructure
Designed to fit naturally into Microsoft-ecosystem teams already using Azure services.
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.
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.
Product FAQ information · View source
The expert review
Progress AI Observability earns a 74.8/100, 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.
74.8/100: Strong enterprise foundation with significant performance debt.
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Scores across 13 dimensions
Brand & experience
Performance
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