AI Observability
for Production Agents

See where workflows break, what they cost, and whether outputs are good enough to ship. Built for teams working in .NET, Python, and JavaScript.

Start Free

No credit card required to start.
5-minute set up.

Schedule a Demo

Turn Production Evidence Into Reliable Releases

Connect every signal across the production loop.
AI agents, LLM apps, RAG systems, and copilots don’t follow simple request-response paths. Trace behavior, debug failures, control spend, and evaluate quality across real production workflows.

Trace and Observe

See execution paths across prompts, models, and tools

Latency and tokens
Execution paths
Outputs

Debug

Diagnose agent
failures fast

Skipped tools
Retrieval issues & bad context
Loops, retries & errors

Control Costs

Track and reduce
AI spend

Token usage & estimated cost
Models, providers, & agents
Workflow patterns

Evaluate Quality

Measure and improve
AI outputs

LLM-as-a-Judge evaluations
Quality scores
Prompt, model & workflow changes

Workflow Debugging

Cost Analysis

LLM Evaluations

Datasets & Experiments

Data Export

Prompt Management

Model Optimization

Performance Metrics

AI Playground

Why Production AI Is Hard to
Understand and Operate

No Clear Execution Path

AI agents do not follow a simple request-response path. A single answer can move through prompts, retrieval, tool calls, retries, model responses, and custom workflow logic. Without a full trace, teams are left guessing what happened.

Failures Do Not Always Look Like Errors

An agent can return a response while skipping a tool, using stale context, retrieving the wrong source, or failing the task. Teams need AI-specific debugging context, not just application logs.

AI Cost is Shaped by Runtime Behavior

LLM spend changes with prompt size, model choice, retries, tool loops, retrieval patterns, evaluation runs, and workflow volume. Teams need cost and token usage tied to the execution path, not just pricing tables.

Successful Responses Do Not Guarantee Quality

A response can be fast, complete, and valid-looking while still containing hallucinations, being ungrounded, unsafe, irrelevant, or unhelpful. Teams need repeatable evaluations connected to production traces.

See Progress AI Observability in Action

Explore product views that help teams trace AI behavior, debug agent failures, analyze cost and token usage, and evaluate output quality from real AI execution data.

Trace Explorer Workflow Debugging Cost Attribution Quality Scorecards

See all

Understand What your Agent Did

Capture the full path of an agent run across prompts, models, tools, retrieval steps and outputs. See how decisions unfold across multi-step and multi-agent workflows.
What’s measured: spans, model calls, tool calls, retrieval steps, latency, token usage, outputs

Investigate Why it Failed

Pinpoint where behavior broke down and what to fix next. Diagnose failures using trace-level context from real agent runs.
What’s measured: errors, failed spans, retries, workflow status, tool failures, latency spikes

Understand What it Costs

Track LLM spend across agents, workflows, models and providers. Identify what is driving cost so teams can optimize usage before it scales.
What’s measured: estimated cost, input tokens, output tokens, cost by model, cost by workflow, usage units

Understand How Well it Performs

Run LLM-as-a-judge evaluations on captured traces. Score quality, usefulness and policy alignment. Compare prompt, model or workflow changes side by side using real execution data.
What’s measured: evaluation scores, judge verdicts, quality trends, weak responses, prompt/model comparisons, before/after results

Get Observable Agents in Minutes

Progress AI Observability fits into your existing agent workflows with lightweight SDKs for .NET, Python, and JavaScript. Start capturing execution data quickly, then use it to understand, debug, and improve agent behavior.

Instrument your AI agents with lightweight integrations that capture prompts, model calls, todiv usage, retrieval steps and state.

Observe agent behavior end to end using session- and trace-level views designed specifically for multi-step and multi-agent workflows.

Improve reliability, performance, and cost by debugging failures, running evaluations and tuning orchestration and model choices using real production data.

Get Started in Minutes

.NET Python Javascript

// .NET - Install & Instrument
// 1. Install
dotnet add package Progress.Observability.Instrumentation
// 2. Instrument
chatClient = chatClient.AddObservability(options =>
{
  options.AppName = Environment.GetEnvironmentVariable("OBSERVABILITY_APP_NAME")!;
  options.ApiKey  = Environment.GetEnvironmentVariable("OBSERVABILITY_API_KEY")!;
});

# Python - Install & Instrument
# 1. Install
pip install progress-observability
# 2. Instrument
from progress_observability import Observability; import os
 
Observability.instrument(
  app_name=os.getenv("OBSERVABILITY_APP_NAME"),
  api_key=os.getenv("OBSERVABILITY_API_KEY")
)

// TypeScript - Install & Instrument
// 1. Install
npm install progress-observability
 
// 2. Instrument
import { Observability } from 'progress-observability';
 
Observability.instrument({
  appName: process.env.OBSERVABILITY_APP_NAME,
  apiKey: process.env.OBSERVABILITY_API_KEY
});

“We cut our agent debugging time from 4 hours to 20 minutes. Being able to see the full trace - prompts, retrieval, tool calls - in one view changed how our team works.”

Early Access Program participant

85%faster root cause analysis

3xfaster time to resolution

<5 minto first trace

Who It’s For

From debugging to governance, built around real AI workflows.

For Developers

Debug Agent Failures in Minutes, Not Days

Find where behavior broke down across prompts, retrieval, tools, model calls, retries, and workflow logic
Trace hallucinations and weak responses to their source
Detect loops, timeouts, skipped tools, and cascading failures

For Engineering Leaders

Control reliability, performance, and cost

See agent behavior across workflows, environments, models, providers, and teams
Identify inefficient agent behavior and expensive patterns
Compare cost, performance, and output quality

For Enterprise Teams

Scale AI systems with control and visibility

Maintain trace history and audit trails
Manage access, retention, and data residency requirements
Support SSO, governance controls, data residency options, and volume-based plans

Pricing

Simple, predictable pricing. Start free, scale as you grow. No surprises, no hidden fees.

Free ForeverFor developers testing early agent prototypes

^$ 0

per month

Includes 10,000 units

Retention: 7 days

Start Free

Agent Trace Explorer
LLM request and prompt logging
Basic cost and token visibility
Basic LLM-as-a-Judge evaluations
.NET, Python and TypeScript SDKs
Integrations with popular AI frameworks and model providers

StarterFor small teams deploying their first live AI agents

^$ 29

per month

Includes 200,000 units

Retention: 30 days

$8 USD per additional 100K units

Schedule a Demo

Everything in Free, plus:
Full Cost Attribution (per-agent, per-model, total costs)
Real-Time & Historical LLM-as-a-Judge Evaluations
Evaluation Datasets & Experiments
Anomaly Detection & Alerting

ProFor teams running production AI agents at scale

^$ 299

per month

Includes 1,000,000 units

Retention: 60 days

$8 USD per additional 100K units

Schedule a Demo

Everything in Starter, plus:
SSO Included

EnterpriseFor organizations scaling governed AI applications

Starting at

^$ 3,000

per month

Custom trace volume

Retention: Infinite

Schedule a Demo

Everything in Pro, plus:
BYOS data residency options for teams with strict data control requirements
Enterprise governance with audit logs, access controls and SLA commitments
Custom volume pricing for high-throughput AI applications and AI labs

Works with Your Stack

The Progress AI Observability Platform integrates with the tools, frameworks and platforms teams already use to build and run AI agents.

Languages & SDKs: .NET (C#), Python, JavaScript/TypeScript
Agent Frameworks: Semantic Kernel, LangChain, LlamaIndex, AutoGen, Microsoft Agent Framework
LLM Providers: Azure OpenAI, OpenAI, Anthropic
AI Tooling: Microsoft.Extensions.AI, Microsoft AI Foundry, Progress RAG
Enterprise SSO: Okta, Azure AD, SAML
Open‑Source Models (OSS): Llama 2/3, Mistral, Mixtral, Falcon, Gemma, etc.

Development and Production: Use the same observability workflow to debug locally, validate changes, and investigate production behavior.

Frequently Asked Questions

The most common questions teams ask when evaluating AI observability for production agents.

Does this add latency to my agent workflows?

The Progress AI Observability 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.
What data does Progress AI Observability capture?

Progress AI Observability captures trace-level data from AI agents and LLM applications, including prompts, responses, model calls, retrieval steps, tool use, metadata, latency, token usage, cost signals and execution context. Teams control what is logged and stored, with options to limit, redact or exclude sensitive data.
Can I use this with my existing observability or monitoring tools?

Yes, the Progress AI Observability Platform complements existing monitoring and logging tools by adding agent-specific visibility rather than replacing your current stack.
What is AI agent tracing and how is it different from traditional application observability?

AI agent tracing shows the execution path behind an AI response, including prompts, model calls, retrieval, tool use, latency, token usage and outputs. Traditional observability focuses on services, infrastructure and application logs. AI observability adds the trace-level context teams need to understand non-deterministic agent behavior and LLM workflows.

Is this meant for development, production or ongoing AI improvement?

All three. Teams can use Progress AI Observability to debug locally, validate changes before release, investigate production issues, evaluate output quality and use production traces to improve prompts, models, retrieval, tools and workflows over time.
Who is this built for?

It’s designed for professional teams building and running AI-powered applications.
Is this built for .NET teams or just adapted from Python tooling?

The Progress AI Observability Platform includes native .NET support designed for production environments, with the same level of visibility and control available to Python and JavaScript teams.
How is this different from LangSmith or other AI observability tools?

Most AI observability tools are built Python-first with .NET as an afterthought or unsupported entirely. The Progress AI Observability Platform offers native .NET support, first-class Semantic Kernel integration, enterprise-grade compliance features (audit trails, PII redaction, data residency), and fits naturally into Microsoft-ecosystem teams already using Azure.

Capability Specific FAQs

How do you debug AI agent failures?

Teams debug AI agent failures by reviewing the full execution path across prompts, model calls, retrieval, tool use, workflow steps, latency, errors, token usage and outputs. Progress AI Observability helps teams investigate skipped tools, failed retrieval paths, agent loops, bad context and other AI-specific failure modes with trace-level evidence.
Why do production AI costs increase unexpectedly?

Production AI costs can increase because of long prompts, retries, agent loops, tool calls, retrieval patterns, context growth, evaluation runs and workflow volume. Progress AI Observability connects cost and token usage to real execution traces so teams can understand what is driving spend before the next invoice.
What is LLM-as-a-judge evaluation?

LLM-as-a-judge evaluation uses an evaluator model to score AI outputs against quality criteria such as relevance, helpfulness, groundedness, safety or task completion. Progress AI Observability connects evaluation scores back to production traces so teams can see which prompts, retrieval steps, tools or model calls shaped the result.

Can production traces be used to improve AI quality?

Yes. Production traces show how AI agents and LLM applications behave in real workflows. Teams can use those traces to identify weak responses, investigate failures, compare prompt or model changes and build better evaluation coverage over time.
Is this the same as machine learning observability?

No. Traditional ML observability focuses on model performance, drift, features and prediction quality for machine learning models. Progress AI Observability focuses on LLM applications and agent workflows, including prompts, model calls, retrieval, tools, traces, sessions, token usage, cost and output quality.

Ready to See What Your AI Agents Are Actually Doing?

Get end-to-end visibility into your AI agents in minutes. Free to start, built to scale.

Start Free No credit card required · Free for small teams · Enterprise plans available

Talk to Sales

AI Observability for Production Agents

Turn Production Evidence Into Reliable Releases

Trace and Observe

Debug

Control Costs

Evaluate Quality

Why Production AI Is Hard to Understand and Operate

No Clear Execution Path

Failures Do Not Always Look Like Errors

AI Cost is Shaped by Runtime Behavior

Successful Responses Do Not Guarantee Quality

See Progress AI Observability in Action

Get Observable Agents in Minutes

Who It’s For

Pricing

Free ForeverFor developers testing early agent prototypes

StarterFor small teams deploying their first live AI agents

ProFor teams running production AI agents at scale

EnterpriseFor organizations scaling governed AI applications

Works with Your Stack

Frequently Asked Questions

Capability Specific FAQs

Ready to See What Your AI Agents Are Actually Doing?

AI Observability
for Production Agents

Why Production AI Is Hard to
Understand and Operate