Debug AI Agent
Failures

The workflow failed. Find the step that sent it off course.

Identify where workflows break down and trace the prompts, tools, retrieval steps, and workflow context behind each failure to fix issues faster.

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Built for teams working in .NET, Python, and JavaScript. No credit card required. 5-minute setup. Free for small teams

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85%

faster root cause analysis

faster time to resolution

<5 min

to first trace

AI Failures Are Hard to Diagnose

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.

Find Out Why an AI Agent Failed

Get the debugging context you need to understand where an AI workflow broke down, which inputs or decisions shaped the outcome, and what to fix next.

Debug Skipped, Failed, or Misused Tool Calls

An agent had access to a tool, but skipped it, called it incorrectly, used it at the wrong time, or failed to complete the tool-driven task. Inspect tool availability, inputs, outputs, errors, approvals, and related spans in the context of the full workflow.

Determine whether the issue came from orchestration, tool behavior, model output, missing context, approval state, or custom application logic.

Tool and Workflow Trace Debugging
AI Agent Root-Cause Analysis

Troubleshoot MCP, Agent, and Multi-Agent Workflow Failures

A tool-driven or multi-agent workflow fails midway, stalls, loops, returns an incomplete outcome, or passes responsibility incorrectly between agents, tools, connectors and services. Review the execution path across model calls, tools, handoffs, MCP workflows, runtime state, latency, errors, and final output.

Find the broken step without manually stitching together logs, traces, screenshots, and user reports.

Tool and Workflow Trace Debugging
AI Agent Root-Cause Analysis

Investigate RAG and Retrieval Failures

A response is incomplete, hallucinated, poorly grounded, or based on the wrong source. Inspect the query, retrieved content, source metadata, prompt, model response, trace context, and final answer.

Identify whether the issue came from retrieval, source quality, prompt design, model behavior, or workflow logic.

RAG Retrieval Diagnostics
Evaluation-to-Trace Cause Analysis

Analyze AI Failure Modes with Production Evidence

When the same issue appears across production traces, your team needs to understand the pattern behind it. Connect weak or failed behavior to trace context, latency, token usage, cost signals, outputs, and evaluation scores.

Turn one-off debugging into repeatable reliability improvement.

AI Failure Classification
Eval-to-Trace Cause Analysis

“We cut our agent debugging time from 4 hours to 20 minutes.”

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 Debugging Capabilities

Use trace-level evidence to move from “something failed” to the prompt, retrieval step, tool call, workflow span, or model response that shaped the outcome.

Tool and Workflow Trace Debugging

Inspect tool calls, workflow steps, connector activity, approval flows, custom spans, latency, errors, and final outputs so teams can debug beyond the model response.

AI Agent Root-Cause Analysis

Review prompts, model calls, retrieval, tool use, spans, errors, latency, token usage, and outputs in one trace-level view to understand where agent behavior changed and what to inspect next.

RAG Retrieval Diagnostics

Investigate incomplete, hallucinated, or poorly grounded responses by reviewing the query, retrieved content, source metadata, prompt, model response, trace context, and final answer.

Failure Pattern Analysis

Analyze repeated calls, loops, retries, slow steps, failed tools, weak outputs, and recurring production traces to identify patterns behind AI agent failures.

Evaluation Scores as Debugging Signals

Use poor scores, failed evaluations, and weak outputs as starting points for deeper investigation into prompts, retrieval, tools, workflow logic, and model behavior.

Production Replay and Trace Review

Use captured traces and failed production cases to review what happened, reproduce the execution context where possible, and guide the next fix or validation step.

Follow the Evidence Across the AI Production Workflow

Debugging shows where behavior broke down, so teams can validate the fix.

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 AI debugging?

AI debugging is the process of investigating why an AI system behaved unexpectedly. For agents and LLM applications, this means inspecting prompts, model calls, tool use, retrieval, workflow steps, latency, errors, and outputs instead of relying only on traditional logs or stack traces.
How is LLM debugging different from traditional debugging?

Traditional debugging usually focuses on deterministic code paths, exceptions, and reproducible inputs. LLM and agent debugging often involves non-deterministic behavior across prompts, models, retrieved context, tools, and workflow state. Teams need trace-level visibility into the full AI execution path to understand what happened.
How do you debug an AI agent that skipped a tool?

To debug a skipped tool, teams need to inspect the prompt, available tools, model response, tool-selection logic, approval state, orchestration flow, and final output. Progress AI Observability helps connect those steps in a single trace so teams can identify why the tool was skipped or misused.

Can Progress help with MCP workflow debugging?

Progress can help teams inspect tool-driven and connector-driven workflows by capturing trace context across model calls, tool invocations, custom spans, latency, errors, and outputs. This helps teams understand where an MCP or agent workflow stalled, failed, or returned an incomplete result.
How does root-cause analysis work for AI agent failures?

Root-cause analysis for AI agent failures starts with the full execution path. Teams review prompts, retrieval, tools, model calls, spans, errors, token usage, latency, and output quality signals to determine whether the issue came from context, orchestration, model behavior, tool configuration, workflow logic, or application code.
How do traces and evaluations work together during debugging?

Traces show what happened during an AI request. Evaluations help identify whether the output was useful, relevant, safe, or grounded. Together, they help teams start from a weak output or poor score, inspect the execution path behind it, and validate whether a fix improved quality.