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The bygone year has been an interesting one, especially so for the age of AI that is fast coming. We saw AI agents rise for the first time and take over repetitive tasks that traditionally required a human workforce. However, in 2025, most AI agents still lived inside demos, copilots, and experimental workflows. With the […]
15 AI Agents Trends to Watch in 2026 Read More »
Recursive Language Models aim to break the usual trade off between context length, accuracy and cost in large language models. Instead of forcing a model to read a giant prompt in one pass, RLMs treat the prompt as an external environment and let the model decide how to inspect it with code, then recursively call
This winter, Cash App is preparing to roll out something quietly revolutionary: Moneybot, a financial chatbot designed not just to answer questions, but to take action. It’s a subtle shift in description, but a monumental shift in capability. For decades, banking chatbots have been reactive tools. They checked balances, fetched statements, and maybe helped reset
The Dark Side of Agentic AI: What Moneybot Signals About the Future of Digital Banking Read More »
In this tutorial, we implement an agentic AI pattern using LangGraph that treats reasoning and action as a transactional workflow rather than a single-shot decision. We model a two-phase commit system in which an agent stages reversible changes, validates strict invariants, pauses for human approval via graph interrupts, and commits or rolls back only then.
Alibaba Tongyi Lab have released MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld. The system targets three specific gaps that early GUI agents often ignore,
In this tutorial, we build an advanced, end-to-end multi-agent research workflow using the CAMEL framework. We design a coordinated society of agents, Planner, Researcher, Writer, Critic, and Finalizer, that collaboratively transform a high-level topic into a polished, evidence-grounded research brief. We securely integrate the OpenAI API, orchestrate agent interactions programmatically, and add lightweight persistent memory
Artificial Intelligence is no longer confined to massive servers or centralized clouds. As we move deeper into 2025, AI has become distributed, autonomous, and embedded in every layer of digital infrastructure. But with this shift comes a new strategic question for every engineering and business leader: Where should your AI agent actually live — on
Edge Agents vs. Cloud Agents: Why the Wrong Choice Can Kill AI Performance Read More »
More machine learning systems now rely on AI agents, which makes careful safety evaluation essential. With more and more vulnerabilities coming to the fray, it’s nigh impossible for a single unified protocol to stay up to date with them all. This piece introduces MLflow as a practical framework for testing and monitoring agentic systems through
Track and Monitor AI Agents Using MLflow: Complete Guide for Agentic Systems Read More »
NVIDIA AI research team released NitroGen, an open vision action foundation model for generalist gaming agents that learns to play commercial games directly from pixels and gamepad actions using internet video at scale. NitroGen is trained on 40,000 hours of gameplay across more than 1,000 games and comes with an open dataset, a universal simulator,
We have entered the time of multi-agent artificial intelligence. However, there is a very important issue: in what way can remote AI agents produce rich and interactive experiences without exposing the system to security risks? Google A2UI (Agent-to-UI) protocol addresses this question in a very smart way, allowing agents to create user interfaces that are
Google A2UI Explained: How AI Agents Build Secure, Native User Interfaces Read More »
