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measure-agent-task-success

Measure end-to-end success rates and task paths of AI agents using tracing data.

29|8|Updated Jul 5, 2026
Search Tags:#AI agent#task completion#AI monitoring#performance evaluation#agent benchmarking
One-click install
npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill measure-agent-task-success
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: measure-agent-task-success
Source: https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/measure-agent-task-success
Command: npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill measure-agent-task-success

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill assesses whether an AI agent successfully completes its task end-to-end, not just individual steps, by scoring outcomes and task paths.

Core Features & Use Cases

  • End-to-End Task Measurement: Evaluate whether the agent achieves the correct end state for each task.
  • Task Success Rate Analysis: Define "success" explicitly per task type, checking if conditions are met.
  • Diagnostic Metrics: Review the steps taken to complete the task, including tool-call success rate and recovery from errors.
  • Evaluation Frameworks: Use task datasets with defined success conditions, run agents in a sandbox, and analyze trace data for failures.
  • CI and Real-time Monitoring: Integrate success rate checks into CI and monitor real tasks for drift alerts.

Quick Start

Measure the success rate of your AI agent on the task 'book a flight', ensuring it reaches the correct end state.

Frequently Asked Questions about measure-agent-task-success

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I measure AI agent task completion accuracy end-to-end?▼

Measuring AI agent task completion accuracy requires scoring whether the agent achieves the correct end state for each task, analyzing task paths, and using tracing data to evaluate real production tasks rather than just individual steps.

What is the best way to evaluate AI agent performance using task success rates?▼

Evaluating AI agent performance involves defining success conditions explicitly per task type, running agents in a sandbox with task datasets, and analyzing trace data to calculate the overall task success rate and identify failures.

Can I integrate AI agent evaluation into CI pipelines for real-time monitoring?▼

Yes, you can integrate success rate checks into CI pipelines to automate AI agent evaluation and monitor real tasks continuously, generating drift alerts when performance degrades over time.

What diagnostic metrics should I track when benchmarking AI agent workflows?▼

Benchmarking AI agent workflows requires tracking diagnostic metrics like tool-call success rates and the agent's ability to recover from errors, reviewing the specific steps taken to complete each task.

Do I need to define explicit success conditions to benchmark an AI agent?▼

Yes, benchmarking an AI agent requires task datasets with explicitly defined success conditions to objectively determine if the agent reaches the correct end state and achieves accurate task completion.

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