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Agentic AI

Agentic AI represents the next phase in automation, featuring agents that execute complex tasks with minimal human supervision. We evaluated the most popular AI agents, open-source AI agent frameworks, customer service AI agents, and the performance of popular LLMs as AI agents.

AI Agents Benchmark Results

We tested leading AI agents across a benchmark that has actual workflow automation needs, including navigating complex interfaces, making precise edits, and completing multi-step processes.

AI Agents Benchmark

GitHub Stars of Open-Source AI Agent Frameworks

We collected GitHub stars of popular open-source AI agent frameworks over the years.

Read Open-Source Agentic Frameworks

Customer Service AI Agents

We evaluated four industry leaders on their API keys or playgrounds using a hold-out dataset of 100 questions randomly selected from Bitext Gen AI Chatbot Customer Support Dataset. We created an imaginary company, TechStyle, an e-commerce site with standard policies, and established a small customer database. This info was shared with each AI vendor before we posed our questions.

Agentic Customer Service Benchmark

AI Agent Performance Benchmark

Our benchmark includes five tasks of increasing difficulty and complexity designed for a human to test success rates with business-specific tasks. The goal of the benchmark is to evaluate document processing by AI agents. We used eighteen different large language models as AI agents.

AI Agent Performance Benchmark

Open-source web agents: WebVoyager accuracy benchmark

WebVoyager benchmark evaluates web agents on 15 real-world websites, including Google, GitHub, and Wikipedia. It includes tasks like searching, clicking, navigating, and submitting forms across 643 task instances. Accuracy is measured by successful completion, compared to standard outputs.

Web Voyager Benchmark

Explore Agentic AI

Optimizing Agentic Coding: How I use Claude Code in 2025

Agentic AIAug 15

AI coding tools have become indispensable for many tasks. In our tests, popular AI coding tools like Cursor have been responsible for generating over 70% of the code required for tasks. With AI agents still being relatively new, I observed some useful patterns in my workflow that I want to share.

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