Agentic AI Benchmarks: Proprietary- Open Source AI Agents & Performance
Agentic AI includes 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.
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.
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.
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.
Explore Agentic AI Benchmarks: Proprietary- Open Source AI Agents & Performance
40+ Agentic AI Use Cases with Real-life Examples
Autonomous generative AI agents execute complex tasks with little or no human supervision. Agentic AI differs from chatbots and co-pilots. Unlike traditional AI, particularly generative AI, which often requires human intervention in complex workflows, agentic AI aims to autonomously navigate and optimize processes thanks to its decision-making capabilities and goal-directed behavior.
Optimizing Agentic Coding: How I use Claude Code
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.
Top 8 Agentic CRM Platforms
Agentic CRM platforms are customer relationship management tools that can plan, execute, and adjust workflows autonomously without constant human supervision. Here are the main agentic CRM platforms, ranked by their actual capabilities, market presence, and real-world results.
LCMs: From LLM Tokenization to Concept-level Representation
Large concept models (LCMs), as introduced by Meta in their work on “Large Concept Models,” represent a fundamental shift away from token-based prediction toward concept-level representation.
Top 10 Agentic AI in Supply Chain Tools & Use Cases
Forecasts suggest that by 2030, half of cross-functional supply chain management solutions will integrate agentic AI capabilities. This widespread adoption will enable global enterprises to reduce exposure to supply chain disruptions and achieve more consistent performance.
How we Moved from LLM Scorers to Agentic Evals?
Evaluating LLM applications primarily focuses on testing an application end-to-end to ensure it performs consistently and reliably. We previously covered traditional text-based LLM evaluation methods like BLEU or ROUGE. Those classical reference-based NLP metrics are useful for tasks such as translation or summarization, where the goal is simply to match a reference output.
4 Agentic AI Design Patterns & Real-World Examples
Agentic AI design patterns enhance the autonomy of large language models (LLMs) like Llama, Claude, or GPT by leveraging tool-use, decision-making, and problem-solving. This brings a structured approach for creating and managing autonomous agents in several use cases.
Authorization for AI Agents: Permit.io, Descope & more
I have been exploring agent identity and the authentication/authorization platforms that could support it, while also examining how standards like OAuth 2.0 and frameworks such as Keycloak might apply. Below, I listed the best AI agent–specific platforms and features, categorized by their primary focus.
Agentic AI Architecture for Industrial Systems
Agentic AI allows natural language interaction with industrial systems, enabling users to query data and receive actionable insights. We will outline a reference architecture designed for industrial environments, describe how task-specific agents and tools can be orchestrated. We will also explore current state of natural language interfaces (NLIs) in industrial systems.
The 7 Layers of Agentic AI Stack
The rise of agentic AI has introduced a technology stack that extends well beyond simple calls to foundation-model APIs. Unlike traditional software stacks, where value often concentrates at the application tier, the agentic AI stack distributes value more unevenly. Some layers offer strong opportunities for differentiation and moat building, while others are rapidly becoming commoditized.