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Angular v22: Agentic Pipelines and Resilient Template Architectures The frontend landscape is no longer just about rendering pixels; it's about establishing robust pipelines that integrate seamlessly with automated, AI-driven workflows. At Google I/O 2026, the Angular team unveiled a massive structu
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
"AI agent" sounds like it needs a framework. It doesn't. Strip away the branding and an agent is one loop: Send the conversation to the model. If it asks to use a tool, run the tool. Feed the result back. Repeat until it answers. Here's the whole thing with Claude: for (let step = 0; step send(delta
Until now, the only way to run Hermes was through a terminal—or one of several unofficial GUIs the community had cobbled together.
If you have spent any time building production-grade LLM applications, you know the dirty secret of the industry: prompt engineering is a vibe-based unscientific mess. You write a prompt. It works for three test cases. You deploy it. It fails on the fourth. You tweak a sentence, which fixes the four
When the conversation turns to AI infrastructure, it almost always lands on GPUs and TPUs. The New Stack sat down The post Why CPUs still matter in the age of AI agents appeared first on The New Stack.
I had seven AI agents on cron. Two of them stopped running on day one. I noticed on day eighteen. That sentence is the whole article, and it is also the kind of sentence I would have argued against if someone else had said it on a podcast. "Surely you would notice. You have tracing. You have dashboa
The AI Risk Quadrant evaluates AI agents based on three factors: how vulnerable they are to compromise, the potential impact of a breach, and the strength of their security defenses. The post Security of 100 AI Agents Tested and Ranked – What You Need to Know appeared first on SecurityWeek.
AI agents are increasingly moving beyond text generation. Modern agent systems can execute code, manipulate files, browse the web, call APIs, manage infrastructure, and coordinate distributed tasks. Once agents begin interacting with real environments, execution safety shifts from a prompt problem t
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science.
Qualcomm envisions a future where autonomous AI agents will manage tasks independently across devices, shifting the digital experience away from smartphones. President and CEO Cristiano R Amon declared 2026 "the year of agents," highlighting AI's evolution from prompt-response to proactive action. T
The problem Every deploy — same manual test steps. Login, open the form, I wanted to skip the Playwright/Selenium boilerplate and just qpilot — an AI agent that reads your manual test case and You write this: Go to https://myapp.com/login Enter email and password Click Login Verify dashboard is visi
Documented AI incidents rose to 362 in 2025 from 233 in 2024, while hallucination rates across 26 leading models ranged from 22% to 94%. These numbers show that the quality of AI Agents is becoming a serious bottleneck. The real danger arises when we try to test AI Agents using traditional software
If you’ve ever been involved in hiring developers, you know the drill. You open a position, and within 48 hours, you have 300+ PDFs sitting in your inbox. Most of them are spam, heavily exaggerated, or completely irrelevant. But to find the hidden gems, your engineering team has to waste hours scann
A new survey of 6,500 service pros shows investments in agentic AI are essential for business success.
Last week, I ran a small experiment. I wanted to see how well GPT-4 could interact with a real-world API without much hand-holding. Nothing complicated. No multi-agent workflows. No orchestration frameworks. Just a simple task that thousands of applications perform every day. Send an email through S
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,
A developer agent should not start by writing code. That may sound strange, but if you are building an agent for real engineering work, the first version should be cautious. It should read an issue, inspect the codebase, create a plan, suggest tests, and generate a pull request summary. Only later s
High-autonomy agents with broad permissions and unfettered access are a recipe for disaster, and enterprises need to act now before they become the next horror story.
