Topic
59 articles found
We've all been there. You're working with an AI coding assistant, having a great conversation where it learns your project's architecture, your preferred patterns, the quirks of that legacy module. Then you close the session, open a new one, and it's like you've never met. Blank slate. Every. Single
Here's a pattern I keep seeing: a team builds an AI agent, the demo works, they ship it, and within a few weeks the outputs are unreliable. Someone opens a ticket about hallucinations. Someone else suggests switching to a better model. The model isn't the issue. The data feeding the model is. Multi-
I've been working on a Mac-native agent framework for about a year. One of the hardest problems: making the agent actually remember context across sessions in a way that's useful, not just "here's your last 10 messages." What I ended up with is a knowledge graph — entities (people, projects, tools,
Today, Microsoft Build brought the announcement of Project Solara, a new chip-to-cloud platform for AI agents. With the reveal, Microsoft CEO Satya Nadella shared an interesting quote on the company’s vision for an agent-first computing future. more…
The AI agent security community has been converging on a problem. A researcher recently ran an experiment — feeding a memory-retrieval framework 10 scenarios involving certificate operations: signing, issuing, revoking, delegating. The system retrieved the right memory 8 out of 10 times. It matched
I spent weeks building openMOON AI — an agent that executes multi-step tasks on your Mac, not just answers questions. "Find the invoice email and reply I'll pay Friday" → searches Mail → reads email → drafts reply → asks approval → sends GitHub: https://github.com/niceappspl/openmoon-ai Tauri 2 + Ru
For the past two years, the technology industry has raced to make AI agents more capable — teaching them to write code, navigate software interfaces, manage files, and orchestrate multi-step workflows with increasing autonomy. What the industry has not done, at least not with any consistency, is ans
Agents Don't Replace APIs. They Expose How Weak Most APIs Already Are There is a growing narrative that AI agents, often coupled with things like Model Context Protocol, will replace APIs. It is easy to see why that idea has taken hold. Agents can discover tools, reason about which one to call, and
Agent Skill is already one of the most widely accepted practices in AI Agent engineering: encapsulating repeatable tasks into capability packages that are discoverable, callable, and injectable into context. This is certainly valuable. Diagnosing bugs can be a skill. Reviewing code can be a skill. G
AI agents will need to pay for compute, data, and API calls. The current paradigm of human-managed payment systems breaks down when thousands of autonomous agents need to transact independently. They need wallets — not custodied accounts managed by humans, but autonomous financial infrastructure the
Over the last two years, an entire industry has emerged around AI development within the PHP ecosystem. Where integrating an LLM once looked like a few lines of code calling the OpenAI API, developers today are building full-fledged agent systems: with memory, tools, workflows, observability, and ev
Every testing tool for AI agents tests individual agents. But production failures don't happen inside agents — they happen between them. I learned this the hard way. I built a 14-agent document processing system using CrewAI. Each agent worked perfectly in isolation. In production, the system failed
The current wave of AI development is undergoing a massive paradigm shift. We are rapidly moving past simple "prompt wrapper" applications and entering the era of fully autonomous, agentic systems. Yet, if you’ve tried to build an AI agent for a production environment, you’ve likely run into a frust
This is a submission for the Hermes Agent Challenge: Write About Hermes Agent. Most AI assistants wait around like interns who lost the Slack invite. You open a tab. You type a prompt. You explain the same project again. You paste the same links again. Then you spend half the afternoon checking whet
Reading Claude's Mind: Anthropic's Natural Language Autoencoders Open a New Window Into Agent Alignment What if you could read an AI agent's thoughts — not just what it says, but what it thinks but doesn't tell you? That is precisely the question Anthropic set out to answer with Natural Language Aut
Published: May 30, 2026 Tags: ai, agents, opensource, github, bounty, tutorial, python, architecture Every week, someone tweets "I built an AI agent that makes money while I sleep." And every week, the replies are the same: prove it. So I did. I built ZKA (Zero Knowledge Agent) — an autonomous AI ag
Here are three ways to create your own AI agents that you can control entirely through your web browser
Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know? Workday's answer is to make its existing system of record the
Traditional IAM was designed for deterministic users and workflows. Agentic AI introduces autonomous, non-deterministic actors that require a fundamentally different authorization model, one that combines identity, delegation, runtime policy evaluation, and ephemeral authorization. AI agents require