The Most Common Questions About Agent Evals, Answered
Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down. Context & related coverage →
Live feed
AI news for platform & agent engineers
The daily paper for AI engineers.
Ranked brief · refreshes every 2 hours
Top signals · Oct 14–15, 2026 · Updated Oct 15, 19:00 UTC
RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios
Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifying and correctly applying domain-specific rules remains challenging when scenario information is dist... Context & related coverage →
AI is mass decompiling games. That's both awesome and scary.
AI is mass decompiling games. That's both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and pr... Context & related coverage →
Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy
Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based m... Context & related coverage →
New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents
Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet their effects on the recipient remain underexplored. Through con... Context & related coverage →
Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents
At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control. Context & related coverage →
Feature-Led AI Glasses: Alibaba Prices Qwen N1 Frames at $450
Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio
How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists. Context & related coverage →
Context Engineering with Deep Agents: How To Remember Without Keeping Everything In Context
Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused. Context & related coverage →
Predictable AI spend in BigQuery: Introducing cost management for generative AI functions
The generative AI functions in BigQuery ( AI.GENERATE , AI.IF , AI.CLASSIFY , AI.SCORE , and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of support tickets... Context & related coverage →
Push-Based KV Transfer to Improve TTFT and TPOT for Disaggregated Inference in vLLM
A push-based NIXL connector for vLLM disaggregated serving: the prefill instance writes KV directly into decode memory over RDMA, taking the router relay, decode-side allocation, and transfer setup off the TTFT critic... Context & related coverage →
HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost
✓ You're all caught up
Top 12 ranked stories in this snapshot · fresh brief every 2 hours