Evaluate and Improve AI Agent Outcomes and LLM Output Quality

Your AI answered. But did it miss the mark?

Measure task completion, agent workflow behavior, and output quality with evaluations connected to trace context so you can identify weak responses and improve AI performance.

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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 Quality Issues Don’t Look Like Failures

A response can look successful even if the agent skips a required step, uses the wrong tool, misses an escalation, or produces a weak final answer. Without evaluation signals, teams miss regressions in both output quality and workflow outcomes.

Connect evaluation scores to trace data so your teams can ship more confidently and optimize quickly.

Catch Quality Drift Before It Becomes
a User-Reported Bug

Evaluate RAG Answer Quality

Your AI application gives an answer, but your team needs to know whether it was grounded in the right context and useful to the user. Use evaluation tasks and evaluator templates to score AI outputs for quality dimensions such as relevance, helpfulness and safety.

Identify weak or risky answers and decide which outputs should be reviewed, improved, or investigated further.

LLM-as-a-Judge Evaluations
Custom Evaluators and Evaluation Templates
Risk and Reliability Metrics

Monitor Output Quality Over Time

Your AI output quality is changing due to evolving prompts, models, user traffic, retrieved content and application workflows. Run historical evaluations on collected traces or real-time evaluations as new traces arrive.

Track quality trends, catch regressions earlier and avoid relying only on manual review.

Continuous Production Evaluations
AI Quality Scorecards
Risk and Reliability Metrics

Compare Prompt, Model, and Workflow Changes

A new prompt, model or workflow version looks promising, but your team needs evidence before shipping it broadly. Use scores and evaluation results, and experiments to compare outputs across traces, prompt strategies, agent versions or model choices.

Make release decisions with quality, latency and cost signals in view.

Prompt, Model, and Tool Comparison
Evaluation Datasets and Experiments
AI Quality Scorecards

Turn Failed Outputs and Agent Behaviors into Future Test Coverage

A production issue reveals a weak answer, missed tool call, failed handoff, incomplete task, or other behavior that should not happen again. Use observed traces and poor evaluation scores to create evaluation datasets, regression checks, and pre-deployment validation.

Learn from production behavior instead of treating each AI failure as a one-off incident.

Evaluation Datasets and Experiments
AI Regression Test Sets
Pre-Deploy Evaluation and Regression Testing

"We use custom spans to track Tenant-level interactions, which helps us quickly understand issues, take corrective action, and continuously improve system performance."

Jeremy Schaab

Vice President Software Development, FYIsoft

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 Evaluation Capabilities

Works with Azure, OpenAI, Anthropic and other major LLM providers.

LLM-as-a-Judge and Custom Evaluators

Use evaluator models, built-in templates, and custom criteria to score outputs for relevance, helpfulness, groundedness, safety, task completion, and domain-specific quality.

Continuous Production Evaluations

Run evaluations on historical traces and new production activity to monitor quality trends, detect regressions, and understand how outputs change over time.

Evaluation Datasets and Experiments

Create reusable datasets from production traces, weak outputs, and known failures, then compare prompts, models, tools, retrieval settings, and workflow changes before release.

Trace-Connected Quality Analysis

Connect scores back to the traces, spans, prompts, retrieved context, tool calls, latency, token usage, and outputs that shaped the result.

Quality Scorecards and Risk Signals

Review verdicts, ratings, explanations, quality trends, hallucination risk, safety violations, escalation accuracy, and other reliability signals.

Agent Outcome Metrics

Measure task completion, correct tool use, successful handoffs, recovery from errors, multi-turn success, and robustness across real workflows.

Follow the Evidence Across the AI Production Workflow

Evaluations show how outputs perform, so teams can improve what ships.

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 are AI evals in simple terms?

AI evals (short for evaluations) are tests that measure whether AI outputs are accurate, useful, grounded, safe or aligned with the task. Progress AI Observability supports LLM-as-a-Judge Evaluations, Custom Evaluators and Evaluation Templates and AI Quality Scorecards, so teams can evaluate real outputs from production traces.
How do you evaluate LLM outputs?

Teams evaluate LLM outputs by defining quality criteria, selecting representative examples and scoring outputs consistently. Progress AI Observability supports LLM-as-a-Judge evaluation tasks that can score collected traces or new traces as they arrive.
What is an LLM-as-a-judge evaluation?

An LLM-as-a-Judge evaluation uses a configured evaluator model to assess an AI output against defined scoring criteria. In Progress AI Observability, you can use out-of-the box evaluator templates to define judge instructions, scoring criteria and LLM integration used for the evaluation or you can customize and build your own templates.
What is RAG evaluation?

RAG evaluation measures whether an AI answer is useful, relevant and grounded in the right retrieved context. Teams can use evaluation tasks and trace data to investigate whether weak answers come from retrieval, prompt design, model behavior or workflow logic.
Can Progress evaluate production traces?

Yes. Progress AI Observability supports historical evaluations for traces that have already been collected and real-time evaluations for new traces as they arrive.
What’s the difference between AI evals and benchmarks?

Benchmarks usually test models against standard public datasets or general tasks. AI evals test how your AI agent or LLM application performs in your actual workflow, with your prompts, retrieved context, tools, users, evaluation datasets and quality criteria. Progress AI Observability connects eval results back to traces so teams can understand what shaped the output.

How do evaluation results help teams improve AI systems?

Evaluation results help teams find poor-scoring traces, track quality trends, compare prompt or model strategies and decide where to improve prompts, retrieval, workflow logic or model choices.
How often should I rerun evals?

Teams should rerun evals when prompts, models, retrieval settings, tools, workflows or traffic patterns change. Progress AI Observability supports historical and real-time evaluations so teams can monitor output quality over time, catch regressions and compare changes before issues reach users.
How can teams compare prompt, model or workflow changes?

Teams can compare prompt, model, tool, retrieval or workflow changes by running experiments against consistent evaluation datasets and reviewing outputs against the same criteria. Progress AI Observability helps teams compare quality, latency, token usage, estimated cost and trace context together so they can make better release decisions before shipping broadly.
What are risk and reliability metrics for AI outputs?

Risk and reliability metrics help teams understand whether AI outputs are safe and dependable enough for production. These may include signals such as hallucination rate, safety violations, escalation accuracy, regression rate and other quality indicators.
What are agent-specific evaluation metrics?

Agent-specific evaluation metrics measure whether an AI agent completed the intended task, used the right tools and context, handled handoffs correctly, recovered from errors, escalated when needed and produced (or was able to produce) a useful final outcome. These help teams evaluate the full workflow, not just one response. These can include multi-turn success, recovery from errors, attempt-based success and robustness under variation.
Can evaluation help reduce cost without hurting quality?

Yes, if teams evaluate changes instead of optimizing blindly. Progress AI Observability helps teams review evaluation results alongside token usage, latency, estimated cost and trace context so they can reduce waste while continuing to monitor AI output quality.

Evaluate, Measure, and Improve Your AI System Quality!

Start Free

Built for .NET, Python, and JavaScript.

Request a Demo

Evaluate and Improve AI Agent Outcomes and LLM Output Quality

AI Quality Issues Don’t Look Like Failures

Catch Quality Drift Before It Becomes a User-Reported Bug

Get Observable Agents in Minutes

Featured AI Evaluation Capabilities

LLM-as-a-Judge and Custom Evaluators

Continuous Production Evaluations

Evaluation Datasets and Experiments

Trace-Connected Quality Analysis

Quality Scorecards and Risk Signals

Agent Outcome Metrics

Follow the Evidence Across the AI Production Workflow

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

Evaluate, Measure, and Improve Your AI System Quality!

Catch Quality Drift Before It Becomes
a User-Reported Bug

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