AI Dynamics

Global AI News Aggregator

About

AGENTS

  • New Course: Agent Skills with Anthropic and Claude
    New Course: Agent Skills with Anthropic and Claude

    Important new course: Agent Skills with Anthropic, built with @AnthropicAI and taught by @eschoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: – Create custom skills for code generation and review, data analysis, and research – Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills – Combine skills with MCP and subagents to create agentic systems with specialized knowledge – Deploy the same skills across Claude.ai, Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows. deeplearning.ai/short-course…

    → View original post on X — @andrewyng, 2026-01-28 17:31 UTC

  • Benchmarking Agentic AI in Insurance Underwriting Applications
    Benchmarking Agentic AI in Insurance Underwriting Applications

    #AAAI26 was a great space for thoughtful conversations around Agentic AI, especially its applications in real-world domains. @amanda_dsouza presented our poster, Benchmarking Agents in Insurance Underwriting Environments at the Agentic AI Benchmarks & Applications workshop. The

    → View original post on X — @snorkelai

  • CooperBench: AI Agents Perform 50% Worse in Teams
    CooperBench: AI Agents Perform 50% Worse in Teams

    Introducing the curse of coordination. Agents perform 50% worse in teams than working alone. People building human-AI collaboration today don't realize why current LLMs fail to be good teammates. We built CooperBench to study this. For humans, we recognize that teamwork isn't just the sum of individual capability. Communication and coordination often outweigh raw skill. But for AI? We're only hill-climbing benchmarks that evaluate solo technical abilities. CooperBench A benchmark to evaluate agent cooperation in realistic software teamwork tasks. The setup is intuitive: two agents, two tasks, two VMs, one chat channel (agents can send over arbitrary text, even the entire patch they wrote). We evaluate whether the merged solution from both agents passes the requirements of both tasks. The curse of coordination The most striking result: agents perform 50% worse in teams (black line) than working alone (blue line). Why is this happening? Is it because they can't use the communication tool? No. They spent 20% of their time sending messages. The problem? Those messages were repetitive, vague, ignored questions, or straight-up hallucinated. But bad communication is only part of the story. We found two deeper failures: Commitment: Agents don't do what they promised. Expectations: Agents don't expect others to keep promises either. Without these, cooperation collapses. However, there is a silver lining We also find emergent coordination behaviors, e.g. role division, resource division, and negotiation, which gives us hope that we can use reinforcement learning to improve coordination. What's next? It is true that highly-engineered multi-agent orchestration could largely sidestep the coordination problem. However, we care more about the AI's capability: if we truly want AI to be our teammates, we need them to be natively capable of effective communicating and coordinating. Two agents on software tasks is just the beginning. The real goal: agents that can cooperate with us well enough to actually empower us. CooperBench is our first step. If you're working on this too, let's talk.

    → View original post on X — @jeande_d, 2026-01-28 16:00 UTC

  • Best Healthcare AI Tools: AlphaFold and AI CoScientist Guide

    List all the best tools for healthcare, alphafold, ai coscientist ? what's your best tools list ? then create skills, and ask for a goal

    → View original post on X — @jessyseonoob

  • Linda: Custom AI Agent for Banking Compliance Solutions
    Linda: Custom AI Agent for Banking Compliance Solutions

    This is Linda. 25,000 regulation docs. Before lunch. No smile.
    She’s a custom AI agent for banking compliance. Consistent. Accurate. Reliable. Boring (the good kind).
    Swipe to meet her. Build boring AI solutions with AI21 Labs: https://
    ai21.com/boring-agents?
    utm_source=org-twitter

    → View original post on X — @ai21labs

  • Training Student LLMs to Fix Sloppy Code Reimplementations
    Training Student LLMs to Fix Sloppy Code Reimplementations

    FREE IDEA! Take a human-authored codebase from <2022, have teacher LLM create a "sloppy" reimplementation of a single feature, then train student LLM to undo the slop. Should bias away from broad exceptions, blank returns, attribute/type checking, or low-q defensive coding.

    → View original post on X — @alexjc

  • AdaReasoner: Dynamic Tool Orchestration for Visual Reasoning

    AdaReasoner Dynamic Tool Orchestration for Iterative Visual Reasoning

    → View original post on X — @_akhaliq

  • AI Stack Layers: From Rules to Agentic Intelligence
    AI Stack Layers: From Rules to Agentic Intelligence

    AI isn’t one thing — it’s a stack. Rules → ML → Neural Nets → Deep Learning → GenAI → Agentic AI Agents don’t replace the layers below.
    They orchestrate them. If GenAI answers,
    Agentic AI executes.

    → View original post on X — @ingliguori

  • Abacus AI Deep Agent: Autonomous AI with Infinite Memory

    Abacus AI Deep Agent actually does the work. Give it a task → it builds the app.
    Needs a database → it connects and uses it.
    Needs recurring logic → it schedules itself.
    Needs context tomorrow → it remembers. Autonomous agents with infinite memory, backed by a persistent AI

    → View original post on X — @abacusai

  • Superagent: Structured Thinking for Enterprise AI Decision-Making

    What interests me most about Superagent is its focus on structured thinking. Breaking down complex questions and synthesizing them into a single report mirrors how real enterprise decisions are approached. Follow @Superagent — now part of @Airtable
    . #SuperagentPartner #ad

    → View original post on X — @antgrasso