Agentic AI

TL;DR Agentic AI is moving from toy demos to systems that must coordinate, persist memory, and interoperate across teams and services. A new survey maps the landscape—frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, Agno, Google ADK, MetaGPT), communication protocols (MCP, ACP, A2A, ANP, Agora), and the fault lines that still block production scale. This article distills what’s ready now, what breaks in production, and how to architect for the protocols coming next. ...

If you think AI models are getting too good at math, you’re not wrong. Benchmarks like GSM8K and MATH have been largely conquered. But when it comes to physics—where reasoning isn’t just about arithmetic, but about assumptions, abstractions, and real-world alignment—the picture is murkier. A new paper, PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems, makes a bold stride in this direction. It introduces a massive benchmark of 19,609 physics problems called PHYSICSEVAL and rigorously tests how frontier LLMs fare across topics from thermodynamics to quantum mechanics. Yet the real breakthrough isn’t just the dataset—it’s the method: multi-agent inference-time critique. ...

In the volatile world of crypto, the only constant is change. This makes portfolio optimization a challenge — especially for traditional strategies that assume stability over time. A new study by Castelli, Giudici, and Piergallini offers a compelling solution: build your investment pipeline out of agents. Using a modular Multi-Agent System (MAS) framework implemented in Crew AI, the authors compare two crypto portfolio strategies over the 2020–2025 period: one static and one adaptive. The system orchestrates specialized agents to ingest, clean, analyze, optimize, and report on daily crypto prices — all in a transparent and auditable way. ...

In the quest for smarter automation, businesses have long leaned on rigid workflow engines and task-centric diagrams. But in an increasingly dynamic, AI-powered world, these static pipelines are starting to show their cracks. A new paper, “An Agentic AI for a New Paradigm in Business Process Development,” proposes a compelling shift: reframe business processes not as sequences of tasks, but as networks of autonomous, goal-driven agents. From Flowcharts to Ecosystems Traditional business process management (BPM) operates like a production line: each step is predefined, and systems pass the baton from one task to the next. This works well for predictable operations but falters in environments requiring adaptability, exception handling, or dynamic goal reconfiguration. ...

Climate science, once defined by hand-tuned code and static diagnostics, is entering a new phase of automation and adaptability. At the forefront is EarthLink, a self-evolving multi-agent AI platform built specifically to support Earth system science. But this isn’t another LLM wrapper for answering climate questions. EarthLink is something deeper: a scientific collaborator that plans experiments, writes code, debugs itself, interprets results, and learns with each use. From Toolkits to Thinking Partners Traditional tools like ESMValTool or ILAMB have standardized climate model evaluation, but they remain brittle and rigid. They require domain-specific programming expertise and offer little flexibility beyond predefined tasks. In contrast, EarthLink introduces a new paradigm: ...

In today’s threat landscape, cybersecurity is no longer a battle of scripts and firewalls. It’s a war of minds. And with the rise of intelligent agents powered by Large Language Models (LLMs), we are now entering a new era where cyber defense becomes not just technical but deeply strategic. The paper “Game Theory Meets LLM and Agentic AI” by Quanyan Zhu provides one of the most profound frameworks yet for understanding this shift. ...

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. ...

Logos, Metron, and Kratos: Forging the Future of Conversational Agents Conversational agents are evolving beyond their traditional roles as scripted dialogue handlers. They are poised to become dynamic participants in human workflows, capable not only of responding but of reasoning, monitoring, and exercising control. This transformation demands a profound rethinking of the design principles behind AI agents. In this Cognaptus Insights article, we explore a new conceptual architecture for next-generation Conversational Agents inspired by ancient Greek notions of rationality, measurement, and governance. Building on recent academic advances, we propose that agents must master three fundamental dimensions: Logos (Reasoning), Metron (Monitoring), and Kratos (Control). These pillars, grounded in both cognitive science and agent-based modeling traditions, provide a robust foundation for agents capable of integrating deeply with human activities. ...