AI Agents

The recent wave of innovation in AI systems has ushered in two distinct design paradigms—AI Agents and Agentic AI. While these may sound like mere terminological variations, the conceptual taxonomy separating them is foundational. As explored in Sapkota et al.’s comprehensive review, failing to recognize these distinctions risks not only poor architectural decisions but also suboptimal performance, misaligned safety protocols, and bloated systems. This article breaks down why this taxonomy matters, the implications of its misapplication, and how we apply these lessons to design Cognaptus’ own multi-agent framework: XAgent. ...

Half-Life Crisis: Why AI Agents Fade with Time (and What It Means for Automation) “The longer the task, the harder they fall.” In the world of automation, we often focus on how capable AI agents are — but rarely on how long they can sustain that capability. A new paper by Toby Ord, drawing from the empirical work of Kwa et al. (2025), introduces a profound insight: AI agents have a “half-life” — a predictable drop-off in success as task duration increases. Like radioactive decay, it follows an exponential curve. ...

What if your business processes could think like your most experienced employee—recalling similar past cases, adapting on the fly, and explaining every decision? Welcome to the world of CBR-augmented LLMs: where Large Language Models meet Case-Based Reasoning to bring Business Process Automation (BPA) to a new cognitive level. From Black Box to Playbook Traditional LLM agents often act like black boxes: smart, fast, but hard to explain. Meanwhile, legacy automation tools follow strict, rule-based scripts that struggle when exceptions pop up. ...

Memory in the Machine: How SHIMI Makes Decentralized AI Smarter As the race to build more capable and autonomous AI agents accelerates, one question is rising to the surface: how should these agents store, retrieve, and reason with knowledge across a decentralized ecosystem? In today’s increasingly distributed world, AI ecosystems are often decentralized due to concerns around data privacy, infrastructure independence, and the need to scale across diverse environments without central bottlenecks. ...