Trace and Observe
AI Agent Behavior

Your agent returned an answer. Now see how it got there.

See the full AI execution path across prompts, model calls, tools, and retrieval for AI agents and LLM applications so you can understand how every response is generated and know where to investigate next.

Start Free No credit card required. Free for small teams.

Schedule a Demo

85%

faster root cause analysis

faster time to resolution

<5 min

to first trace

Traditional Logs Don’t Show AI Behavior

AI agents can succeed while using the wrong context or tool.
Without tracing the full path, teams are left guessing.

Progress AI Observability gives you trace-level visibility into why an agent responded the way it did, which tools and context shaped the outcome, and where the workflow changed course.

See What Happened Behind Each AI Response

Trace an Agent Request from Prompt to Final Answer

Follow each step in an AI request, including prompts, model calls, retrieved context, tool usage, latency, spans, and final output.

No more stitching logs and screenshots together.

AI Agent Trace Explorer
LLM Request and Prompt Logging

Observe Model Calls, Tool Use, State, and Retrieval in Context

Inspect multi-step execution paths in context, including tool calls, retrieval steps, retries, state changes, and workflow decisions.

Tool, Retrieval, and Workflow Tracing
AI Agent Trace Explorer

Separate AI-Specific Traces from Noisy AI Telemetry

Filter noisy telemetry by app, environment, release, customer, model, workflow, status, tags, or metadata so teams can focus on the AI behavior behind each response.

Trace Filtering, Tags, and Replay
LLM Request and Prompt Logging

Monitor Agent Behavior Across Sessions and Workflows

Monitor production AI behavior over time across requests, sessions, agents, applications, and workflows. Spot patterns across multi-turn interactions, handoffs , and workflow steps. Know when a workflow needs deeper debugging, cost analysis , or quality evaluation.

Trace Filtering, Tags, and Replay
AI Metrics Dashboards and Alerts

“Being able to see the full trace - prompts, retrieval, tool calls - in one view changed how our team works.”

Early Access Program participant

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
});

Featured AI Agent Tracing Capabilities

Use production traces to understand how AI agents and LLM applications behave across prompts, model calls, retrieval, tools, latency, token usage, and outputs.

AI Agent Trace Explorer

Inspect the full end-to-end trace from prompt to final output, including model calls, tools, retrieval, spans and latency.

LLM Request and Prompt Logging

Review each model call, including prompts, responses, token count, latency, cost and metadata for LLM debugging and auditing.

Tool, Retrieval and Workflow Tracing

Trace the agentic steps around each model call, including tool use, retrieved context, retries, handoffs, state changes and custom spans.

Trace Filtering, Tags and Replay

Filter traces by application, environment, customer, release, model, workflow or status to isolate the requests for review and replay.

AI Metrics Dashboards and Alerts

Monitor latency, errors, token usage, cost, model usage and quality signals across production AI workflows.

AI Stack and Enterprise Support

Supports common languages, frameworks, model providers, and existing observability tools, with enterprise-ready security, privacy controls, and auditability.

Turn Real Behavior Into Better Releases

Use traces to debug, manage cost, evaluate and continuously improve your AI applications.

Trace and observe

See Execution Paths

Latency and tokens
Tools and retrieval
Outputs

Explore Trace and Observe

Debug

Diagnose Agent Failures

Skipped tools
Retrieval issues
Loops and errors

Explore Debug

Control costs

Track AI Spend

Token usage
Model selection
Workflow patterns

Explore Cost Control

Evaluate and Improve

Improve AI Output Quality

LLM-as-a-Judge
Quality scores
Prompt and model changes

Explore Evaluate & Improve

Connected Evidence

Reliable Releases

Start Your First Trace in Minutes.
Scale When You're Ready.

Progress AI Observability makes it easy to get started with flexible, affordable pricing that grows with your needs.

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

Frequently Asked Questions

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

What is agent observability?

Agent observability is the ability to understand how AI agents behave across prompts, models, tools, retrieval, memory, state, workflow logic, and runtime behavior. It helps teams see not only that a request completed, but how the agent reached its answer, which context shaped the response, and where behavior changed during execution.
What is AI agent tracing?

AI agent tracing captures the steps an AI agent takes across a request, session, or workflow, including prompts, model calls, retrieval, tool use, workflow steps, latency, errors, token usage, and final outputs. Progress AI Observability provides an AI Agent Trace Explorer so teams can inspect the full execution path from prompt to final response.
How is AI tracing different from application tracing?

Application tracing shows how software services and infrastructure behave. AI tracing adds visibility into prompts, model interactions, retrieved context, tool use, token usage, outputs, and workflow behavior. Progress AI Observability complements application tracing with LLM Request and Prompt Logging and Tool, Retrieval, and Workflow Tracing.
What should an AI trace include?

A useful AI trace should include the user request, prompt, model calls, retrieved context, tool availability, tool use, tool outputs, workflow spans, latency, errors, token usage, cost signals, and final response. This gives developers the trace-level context needed to understand agent behavior without stitching together logs, traces, and screenshots.
Can Progress AI Observability work with my existing monitoring tools?

Yes. Progress AI Observability complements traditional monitoring and observability tools by adding AI-specific visibility into agent behavior, model calls, retrieval, tool workflows, prompts, outputs, latency, and token usage. Teams can use Trace Filtering, Tags, and Replay to connect AI traces to applications, environments, releases, customers, or workflows.

Is this only for production?

No. Teams can use Progress AI Observability during development, testing, staging, and production. Developers can debug locally, validate changes before release, and investigate production behavior using a consistent view of AI execution data across prompts, model calls, tools, retrieval, spans, and outputs.
What is the difference between agent observability and LLM observability?

LLM observability focuses on model interactions, such as prompts, responses, model calls, latency, token usage, and outputs. Agent observability extends that view to the full workflow around the model, including tool use, retrieval, orchestration logic, state, retries, sessions, and multi-step decisions. For production agents, teams usually need both.
Do I need OpenTelemetry for agent tracing?

No, you do not need to manage OpenTelemetry directly to get started. Progress AI Observability provides SDKs and instrumentation for capturing AI-specific traces from agents and LLM applications. For teams already using OpenTelemetry-based observability practices, Progress supports an instrumentation model that fits into familiar tracing concepts such as spans, metadata, and workflow context.
Why are sessions and threads important for AI agent observability?

Sessions and threads help teams understand agent behavior across multi-turn interactions, not just single requests. They show how prompts, retrieved context, tool use, model responses, and user inputs evolve over time, making it easier to trace behavior, investigate failures, and evaluate whether the agent stayed on task across the full conversation or workflow.
How can teams find the traces that matter in production?

Production AI systems can generate a large volume of traces. Progress AI Observability helps teams use filtering, tags, metadata, status, application, environment, release, customer, model, or workflow context to isolate the requests that matter most, such as high-latency traces, failed tool calls, expensive requests, or behavior tied to a specific release.

Observe and Trace Every Action
of Your AI Agents!

Start tracing prompts, model calls, retrieval, tools, latency and outputs in minutes. Use Progress to understand agent behavior, debug faster and build production AI systems with more confidence.

Start Free

Production and enterprise options are available for teams that need higher usage, more seats, longer retention and advanced evaluation capabilities.

Request a Demo

Trace and Observe AI Agent Behavior

Traditional Logs Don’t Show AI Behavior

See What Happened Behind Each AI Response

Get Observable Agents in Minutes

Featured AI Agent Tracing Capabilities

AI Agent Trace Explorer

LLM Request and Prompt Logging

Tool, Retrieval and Workflow Tracing

Trace Filtering, Tags and Replay

AI Metrics Dashboards and Alerts

AI Stack and Enterprise Support

Turn Real Behavior Into Better Releases

See Execution Paths

Diagnose Agent Failures

Track AI Spend

Improve AI Output Quality

Reliable Releases

Start Your First Trace in Minutes. Scale When You're Ready.

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

Frequently Asked Questions

Observe and Trace Every Action of Your AI Agents!

Trace and Observe
AI Agent Behavior

Start Your First Trace in Minutes.
Scale When You're Ready.

Observe and Trace Every Action
of Your AI Agents!