Progress AI Observability

What is Progress AI Observability?

Progress AI Observability is a production-grade monitoring platform built specifically for AI agents, LLM applications, RAG systems, and copilots. Instead of treating AI workloads like standard request-response APIs, it captures the full execution path—prompts, model calls, tool usage, retrieval steps, retries, and outputs—so teams can trace behavior, debug failures, control spend, and evaluate output quality from real production data. It supports .NET, Python, and JavaScript, and can be set up in about five minutes with just an SDK install and a few lines of code.

Who it's for

• AI engineering leads who need visibility into multi-step agent workflows and want to stop guessing why a production run misbehaved.
• Platform and DevOps teams responsible for controlling LLM costs across models, providers, and agents, and who need token usage tied to actual execution paths.
• Quality-focused product teams that must catch hallucinations, ungrounded answers, and policy violations before shipping AI features to end users.

Key features

Trace and observe every run

Capture execution paths across prompts, models, and tools, including latency, token usage, and outputs. You see how decisions unfold across multi-step and multi-agent workflows, not just a single request-response pair.

Debug agent failures with context

Diagnose failures using trace-level context: skipped tools, retrieval issues, bad context, loops, retries, and errors. The platform surfaces AI-specific failure modes that ordinary application logs miss.

Control AI spend

Track token usage and estimated cost by model, provider, agent, and workflow pattern. Identify what drives cost so you can optimize before usage scales out of control.

Evaluate output quality

Run LLM-as-a-judge evaluations on captured traces, scoring quality, usefulness, and policy alignment. Compare prompt, model, or workflow changes side by side using real execution data.

What stands out

Traditional monitoring sees errors; Progress AI Observability sees the reasoning behind them.

That distinction matters because AI agents fail in ways that don't look like errors. A response can be fast, complete, and valid-looking while still being ungrounded, unsafe, or irrelevant. This platform connects evaluation scores directly to production traces, so you're not judging quality in a vacuum—you're judging it against the exact execution path that produced the output. That combination of tracing, cost analysis, and quality evaluation in one tool is what separates it from generic APM solutions.

Worth checking out if…

You're running AI agents or LLM apps in production and you've hit the wall where logs don't explain behavior, costs are climbing without clear attribution, or you suspect your outputs aren't as reliable as they look. If you're working in .NET, Python, or JavaScript and want a single pane of glass for tracing, debugging, cost control, and quality evaluation, this is worth a look. The free tier with no credit card requirement makes it easy to test against your own workloads before committing.