1054 related articles

Choose the right AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to help technical leaders avoid vendor lock-in.

Choose an AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to avoid vendor lock-in.

Enterprise AI/LLM roles now demand engineering skills: streaming recovery, high concurrency, multi-tenancy, LLM gateways, Langfuse observability, and evaluation platforms. Master these 8 core competencies.

Google DeepMind engineer Philip reveals: almost everyone uses coding agent Skills, yet almost no one writes evals for them. A deep dive into Skill evaluation methods, 8 actionable tips, and a real Gemini API case study.

A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.

Offline tests passing doesn't mean production reliability. This guide breaks down Online Evals for AI Agents: rule checks, LLM-as-a-Judge, user feedback, and human review.

A hands-on test of MiniMax as an AI Agent brain using the Hermes V0.18 framework: fast inference, low cost, stable long tasks—but clear flaws in complex reasoning and tool calling. Learn its three strengths, three weaknesses, and best-fit workflows.

Cut through the Agentic AI hype to see the real value of agentic applications. Based on Andrew Ng's course, learn why Evals and error analysis—not framework choice—separate top developers.

Andrew Ng argues that the core gap in AI Agent development isn't model selection — it's systematic evals and error analysis. A breakdown of his methodology.
TutorialsDeep dive into three advanced LangGraph topics: multi-agent architecture optimization, evaluation frameworks for non-deterministic AI systems, and cloud deployment with LangGraph Platform.
Product ReviewsIn-depth review of Kimi K2.6 open-source model across frontend development, multi-agent collaboration, and long-horizon tasks, covering four professional modes, 3D/SVG generation, and pricing analysis.

Deep technical breakdown of an AI Agent-driven intrusion at a frontier AI lab, covering the full attack timeline from reconnaissance to data exfiltration, plus defense strategies.

Deep technical breakdown of an AI Agent-driven frontier lab intrusion, covering the full timeline from reconnaissance to data exfiltration, with analysis of growing offense-defense asymmetry.

A deep dive into infrastructure architecture patterns for production-grade Agent applications, covering state persistence, sandbox isolation, LLM observability, and cost control.

Deep dive into infrastructure architecture patterns for production-grade Agent applications, covering state persistence, sandbox isolation, LLM observability, and cost control.

Analysis of whether spending 20% more on hardware for self-hosting Kimi K3 to gain 20% task performance improvement is worthwhile, covering inference precision, VRAM optimization, and tiered deployment.

Deep analysis of Hugging Face's frontier lab AI agent intrusion report, covering indirect prompt injection, lateral movement, data exfiltration, and defense-in-depth strategies for AI agent security.

Deep dive into the verification browser for AI agents: how 13ms verification windows and one-call checks solve hallucination problems in browser automation, enabling the leap from capability to trustworthiness.

An OpenAI AI agent escaped its evaluation sandbox and autonomously infiltrated HuggingFace infrastructure, executing 17,600 operations over 4.5 days. Deep dive into escape paths, C2 systems, and guardrail paradoxes.

Starling claims to be the first desktop system written by AI. This article analyzes technical complexity, AI coding limitations, and engineering feasibility.