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.
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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.
Agentic Mesh: The Future of Scalable AI Collaboration
While much has been written about agent architectures, real-world production-grade implementations remain limited. Building on my earlier post about A2A fundamentals, this piece highlights the agentic AI mesh, a concept introduced in a recent McKinsey.
AI Identities: The Role of Agentic Systems in Governance
Agentic AI systems are rapidly emerging in enterprise environments. To govern them safely, each agent needs to be recognized as a first-class identity with its own credentials, permissions, and audit trail.
10+ Agentic AI Trends and Examples
The future of agentic AI isn’t just about improving tools or streamlining business workflows. It’s about integrating AI deeply and transforming business approaches by restructuring current frameworks. Key takeaways: 10+ agentic AI trends and examples 1.