Agent systems covers the move from single model prompts to AI that plans, uses tools, and acts across multiple steps. Articles here examine agent architectures, memory, tool use, and the evaluation problems that make autonomous behavior hard to trust. We focus on how these systems are actually built and where current designs still fall short.
10 Best Claude Prompts for Building AI Agents (2026 Guide) aitrendblend.com Prompt Engineering AI Agents AI Tools Deep Learning About aitrendblend.com · Prompt Engineering · May 2026 · 12 min read 10 Best Claude Prompts for Building AI Agents (2026 Guide) Claude Prompts AI Agents Prompt Engineering Claude 4 Tool Use 2026 Guide 10 Best […]
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Memory Gym: Endless Tasks to Benchmark Memory Capabilities of Agents | AI Trend Blend AITrendBlend Machine Learning Computer Vision Agent Systems About Deep Reinforcement Learning · Journal of Machine Learning Research 26 (2025) 1–40 · 22 min read When Memory Actually Matters: How Memory Gym’s Endless Tasks Expose What Benchmarks Have Been Missing All Along
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Best AI Agents for Small Business in 2026: Tools That Act as Virtual Employees aitrendblend Prompts Claude ChatGPT About AI Tools · Small Business · Automation 2026 Best AI Agents for Small Business in 2026: A Guide to Tools That Act as Virtual Employees By the aitrendblend.com Editorial Team · May 2026 · ~24 min
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Agentic AI for Personalized Knee Braces: How sEMG, Facial Expressions, and LLMs Combine to Configure Rehab Devices | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Healthcare AI · Advanced Engineering Informatics 74 (2026) 104695 · 22 min read Your Knee Brace Said It Hurts. The AI Didn’t Believe It — Until the Muscle
ARuleCon: How NUS Researchers Built an AI Agent That Translates Security Rules Between Any SIEM Platform | AI Security Research AISecurity Research Agentic AI SIEM & SOC About AIOps / SIEM Security · arXiv:2604.06762v1 [cs.CR] · NUS & Fudan University · WWW ’26 · 17 min read ARuleCon: How NUS Researchers Built an AI Agent
MetaClaw: The LLM Agent That Meta-Learns and Evolves in the Wild | AI Trend Blend AITrendBlend Machine Learning Computer Vision About LLM Agents · Continual Learning · UNC-Chapel Hill · CMU · UC Santa Cruz · UC Berkeley (2026) · 25 min read MetaClaw: The LLM Agent That Meta-Learns and Evolves in the Wild —
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Goal-Oriented Graphs: How NTU Researchers Finally Taught LLMs to Plan Like Humans in Minecraft | AI Trend Blend AITrendBlend Home ML Research NLP & LLMs Contact LLM Agents & Reasoning Goal-Oriented Graphs: How NTU Researchers Finally Taught LLMs to Plan Like Humans GraphRAG shreds procedural knowledge into thousands of disconnected fragments. A new framework from
When Drones Learn to Distrust: A New Framework That Teaches Multi-Agent Systems to Spot Unreliable Sensors AITrendBlend Machine Learning About Multi-Agent Systems · Information Fusion 133 (2026) 104261 · 18 min read When Drones Learn to Distrust: The Sensor Fusion Framework That Teaches Multi-Agent Systems to Spot Bad Data in Real Time Researchers at the
AgentDropoutV2: Test-Time Rectify-or-Reject Pruning for Multi-Agent Systems | AI Security Research AISecurity Research Machine Learning About Multi-Agent Systems · arXiv:2602.23258v1 [cs.AI] · 16 min read AgentDropoutV2: Teaching Multi-Agent Systems to Self-Correct Through Test-Time Rectify-or-Reject Pruning A novel test-time framework that intercepts and iteratively rectifies erroneous agent outputs using retrieval-augmented adversarial indicators, achieving 6.3% accuracy improvement
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K2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control | AI Security Research AISecurity Research Machine Learning About Agent Systems · ICLR 2026 · 18 min read K2-Agent: The Cognitive Architecture That Taught AI to Think Like Humans About Mobile Tasks A hierarchical framework separates “knowing what” from “knowing how” — enabling co-evolution of
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