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.
Artificial intelligence has moved far beyond simple chatbots and automated replies. Today, AI agents are becoming digital workers that can make decisions, perform tasks, learn from interactions, and help businesses operate around the clock. From managing customer support to optimizing…
How AI Agents Power Online Business Growth in 2026 Read More »
Your product manager wants “an AI thing that answers customer questions automatically.” Your developer quotes a price based on a chatbot. Your AI consultant sends a proposal for an agent architecture that costs five times more. All three of you…
AI Agents vs Chatbots: What Is the Real Difference? (2026 Guide) Read More »
A developer ships a customer-facing agent on a Friday afternoon. It has access to the company’s email system, the CRM, and the refund API. By Monday, the agent has sent 400 emails to customers it was never supposed to contact,…
AI Agent Security: What to Lock Down Before You Deploy (2026 Guide) Read More »
You are building a research pipeline that needs to search the web, process a dozen PDFs, extract structured data from tables, and produce a cited written report. You set it up as a single Claude Opus 4.7 agent. The quality…
How to Build Multi-Agent Systems with Claude Opus 4.7 & Gemini Flash 3.5 (2026) Read More »
Your team has built on ChatGPT for two years. The new project is a customer facing agent with complex workflows, several tool integrations, and strict output constraints. Someone suggests evaluating Claude before you commit. Just before the architecture meeting, somebody…
Claude vs ChatGPT 5: Which AI Wins for Agent Building? (2026) Read More »
A team from Carnegie Mellon University flipped conventional wisdom on its head — proving that agents with different behavior policies don’t just tolerate each other’s differences, they actively benefit from them. And a novel importance-averaging scheme eliminates the last remaining…
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond Read More »
You have a clear picture of the agent. It should monitor a data source, reason over what it finds, call the right tools, and return a structured answer — without you babysitting every step. You open Claude, describe the setup…
10 Best Claude Prompts for Building AI Agents (2026 Guide) Read More »
A team from TU Dortmund and Universiteit Leiden transformed three 2D partially observable environments into theoretically infinite challenges — and discovered that GRU, a decade-old recurrent cell, consistently and decisively beats Transformer-XL at sustaining memory over long, escalating horizons.
Memory Gym: Endless Tasks to Benchmark Memory Capabilities of Agents Read More »
The hiring gap between a solo founder and a five-person team used to cost somewhere between $180,000 and $250,000 a year in salary. In 2026, a significant portion of that gap — customer support coverage, sales follow-up, appointment scheduling, content…
Best AI Agents for Small Business in 2026: Tools That Act as Virtual Employees Read More »
Donghua University’s five-agent AI system cross-checks muscle signals against facial expressions and verbal reports to configure personalized rehabilitation braces — catching cases where patients exaggerate, suppress, or simply can’t articulate their discomfort accurately. It achieves 0.88 parameter accuracy versus 0.13…