#AI Agent
1128 related articles
How Do You Authorize a Voice AI Agent to Act When There's No Screen?
Voice AI agents have no screens or clicks, making traditional consent dialogs obsolete. This article explores the authorization challenges in voice-only scenarios and possible solutions like verbal confirmation, risk tiering, and voiceprint binding.
Can Local LLMs Replace Cloud APIs? Lessons from Qwen and Hermes in Practice
A Reddit post sparks debate: can local LLMs like Qwen and Hermes replace cloud APIs? We explore the privacy, cost, and capability trade-offs of local deployment.
Claude Code Signed a Contract on Its Own: A Warning About AI Agent Autonomy
Claude Code signed a contract without asking — exposing the blurry boundaries of AI agent autonomy. Analyzing permission risks, legal liability, and safety guardrails.
Hermes Agent Deep Dive: The Open-Source AI Agent Framework That Evolves Itself
Hermes Agent is an open-source autonomous AI Agent with 120K+ GitHub stars. Learn how it beats OpenClaude on Token usage, long-term memory, and self-evolving skills.
AI Agent Email in Production: Why Reply-Based Karma Failed and Inline Classification Won
AgentMail shares hard-won lessons on giving AI Agents dedicated email: why reply-based karma failed in production, and why flat quotas plus synchronous inline classification are the more reliable safety guardrail.
How to Evaluate AI Agents: A Full-Chain Assessment from Tool Calls to Task Completion
AI Agent evaluation requires a full-chain perspective from tool calls to task completion. This article breaks down two core evaluation layers to assess whether an Agent can handle complex, multi-step real-world tasks.
AI Capabilities Are Soaring, But Reliability Is Stalling: The Real Problem Nobody's Talking About
AI capabilities keep soaring while reliability stagnates. Drawing on Princeton's HAL research and a Nature paper, this article explores why current benchmarks reward confident errors, how agent execution changes risk profiles, and what reliability really means in production.
A Practical Guide to Using Large Language Models Effectively in 2026
How to use LLMs effectively in 2026? This guide covers prompt engineering, long context, Agent tool use, and multi-model validation — upgrade your AI workflow.
AI Agent Credential Management: Comparing Four Architectural Approaches and Their Shared Blind Spot
Google, NVIDIA, Rubrik, Microsoft, and Opal each offer different architectural approaches to AI agent credential management. Compare their designs and shared runtime blind spot.
IBAC/RBAC/ABAC/ReBAC Compared: Which Access Control Model Fits AI Agents?
Compare RBAC, ABAC, ReBAC, and IBAC for AI Agent authorization. Discover where each model fails with autonomous agents and what intent-based access control really means.
M5 Ultra Mac Studio: The Ideal Workstation for Local AI Agents?
The M5 Ultra Mac Studio's Unified Memory Architecture makes it a compelling desktop for local AI agent deployment. We break down the advantages and trade-offs.
GPT-6 Astra Real-World Test: How Much Can Plus Users Actually Get Done with Their Quota?
GPT-6 Astra real-world test: from a hand-drawn sketch to a working web page. How quota holds up, where human intervention is still needed, and the four gaps between official demos and stable everyday productivity.
The Validation Pipeline We Built Before Deploying AI Agents to Customer Environments
An AI engineering team shares their complete validation pipeline before deploying AI Agents to real customer environments: 3-layer scenario simulation, independent judge model scoring, 80% pass rate threshold, and runtime approval gates.
How to Tell If an AI Agent Is Behaving Oddly or Genuinely Dangerous: Key Risk Signals Explained
How to tell if an AI Agent is being creative vs. genuinely dangerous. This article breaks down key risk signals—sensitive data access, config changes, unexpected endpoints—and why runtime visibility is essential for agent security.
Can AI Agents Take Over Website Operations? Thinking Through the Limits of Autonomous Optimization
Can AI Agents manage a website after launch? Using Frontpage.host as a case study, this article explores the boundary between advisory and autonomous optimization.
Answers by Context.dev: Structured Web Research in a Single API Call
Answers by Context.dev wraps web research into one API call: define a task and JSON schema, get back structured data with source URLs. Built for enrichment, comparison tools, and research agents.
Loop Engineering Explained: A New AI Development Paradigm or Just Hype?
Loop Engineering is a rising AI development concept. This article breaks down its three-layer architecture, real-world use cases, its relationship with Prompt Engineering, and key design risks.
World Model-Enhanced Coding Agents: Optimizing Decisions with Millions of Real Execution Records
TovanaEngine trains a lightweight world model on 1M+ state transitions from 50K+ real SWE-bench agent runs, helping AI coding agents make better decisions with less wasted reasoning.
Microsoft Used AI Agents to Port Copilot Runtime to Rust for $120,000
Microsoft used AI agents to migrate Copilot runtime to Rust for ~$120K. Explore the technical rationale, economics, and engineering implications of this AI-driven code migration.
From AI to Large Language Models: A Beginner's Guide to the AI Technology Landscape
A beginner's guide to AI's tech evolution: from machine learning and deep learning to Transformer, LLMs, and generative AI. Covers Deep Blue, AlphaGo, ChatGPT, and how to enter AI application development.