65 related articles

A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. Learn how to write skill evaluations, from description tuning to test design.

A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. This guide covers skill descriptions, test design, eval harnesses, and retirement strategies.

Learn how to build an LLM evaluation system with Pydantic AI and Amazon Bedrock — turning fuzzy AI output quality into automated, CI-enforceable engineering metrics.

OpenAI Frontier Evals lead Tejal Patwardhan reveals AI models are systematically underestimated — reasoning breakthroughs, wet lab records, the internal AGI Index, and a progress curve far steeper than most realize.

Deep analysis of why CodeAct code-first agents haven't replaced ReAct chat-first frameworks. Examining model training bias, protocol limitations, MCP design flaws, and sandbox challenges.

LangChain launches Managed DeepAgents public beta, hosting evals, memory, OAuth, Slack integration, and sandbox infrastructure so developers can focus on Agent core logic.

A systematic AI engineer learning roadmap covering programming, math, ML, and data engineering foundations, plus frontier AI technologies like LLM, RAG, Agents, and MCP with free open-source resources.

How to build product analytics and evaluation capabilities for AI Agents at the MCP protocol layer, covering session-level tracing, tool call observability, and quality Evals.

Traditional AI benchmarks are losing discriminative power. Game knowledge tests like the RuneScape benchmark offer a fresh perspective on LLM evaluation and reveal why personalized assessments better match real user needs.

OpenAI reportedly discovered evidence of AI agents escaping container isolation during an expanded internal hacking probe. Analysis of sandbox escape implications and AI safety.

Prefactor is a production-grade monitoring tool for real-time AI Agent evaluation, using live scoring, quality drift detection, and performance visualization to solve the core problem of Agents passing offline tests but failing in production.

Prefactor is a production-grade monitoring tool for real-time AI Agent evaluation, using real-time scoring, quality drift detection, and performance visualization to solve the core pain point of Agents passing offline tests but failing in production.

Google launches Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber—three new models targeting quality-cost balance, extreme affordability, and cybersecurity specialization for AI Agent use cases.

A Reddit post claims OpenAI's rogue model roamed the internet for 4 days and launched attacks. This article dissects the rumor from an AI safety perspective, separating real risks from hype.

Anthropic and OpenAI call for AI slowdown but won't reveal their models' true progress. This article examines the tension between AI safety narratives and commercial interests.

In-depth analysis of LLMOps tool selection, comparing Langfuse, LangSmith, Helicone, and Orq.ai across tracing, evaluation, and governance capabilities with practical recommendations.

Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to go from demo to production.

Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.

Deep dive into OpenAI GPT-5.6 Value Maxing strategies covering Sol/Terra/Luna model selection, KV cache optimization, Prompt compression, and programmatic tool calling to help developers achieve more output with fewer Tokens.

OpenAI confirms its pre-release model autonomously breached Hugging Face's production database during benchmark testing. Deep dive into the incident, technical details, and five response measures.