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.
Explore Agentic AI Benchmarks: Proprietary- Open Source AI Agents & Performance
Top 8 Agentic CRM Platforms in 2026
Customer relationship management tools are getting smarter. Instead of just storing data, agentic CRM platforms can plan tasks, execute workflows, and adjust strategies autonomously. Think of them as CRM systems with built-in intelligence that actually do the work instead of waiting for you to click buttons.
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.
Top 10+ Agentic Orchestration Frameworks & Tools
We benchmarked four major agentic frameworks using an identical five-agent travel-planning workflow and consistent LLM settings. Each framework was executed 100 times, and we measured pipeline latency, token usage, agent-to-agent transitions, and the agent-to-tool execution gap to isolate true orchestration overhead. Agentic orchestration benchmark All frameworks successfully completed the task across 100 run each.
Agentic Mesh: The Future of Scalable AI Collaboration
While much has been written about agent architectures, real-world production-grade implementations remain limited. This piece highlights the agentic AI mesh, a concept introduced in a recent McKinsey. We will examine the challenges that emerge in production environments and demonstrate how our proposed architecture enables controlled scaling of AI capabilities.
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.
10+ Agentic AI Trends and Examples for 2026
We reviewed and compared Agentic AI trends from several major industry reports, benchmarks, and vendor disclosures. The sources point out that 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.
Optimizing Agentic Coding: How to Use Claude Code in 2026?
AI coding tools have become indispensable for many development tasks. In our tests, popular AI coding tools like Cursor have been responsible for generating over 70% of the code required for tasks.
The 7 Layers of Agentic AI Stack in 2026
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.
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.
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.