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A statistic reveals that while 89% of agent teams in production have observability, only 52% run evaluations, raising questions about how prompt changes are gated.
eve now ships with built-in eval capabilities for agents, addressing the need for first-class testing in AI agent development.
Philipp Schmid announces his attendance at the aiDotEngineer conference, where he will discuss agents and evals, and can be found at the GoogleDeepMind booth or talks.
Latent Space podcast hosts OpenAI Chief Research Officer Mark Chen to discuss scaling laws, pre-training, the evals crisis, and OpenAI's research roadmap while cooking.
Vercel explains how they built a system with a skill, linters, evals, and an updating loop to ensure coding agents meet their design standards.
Share a curated AI evaluation (evals) resource library, including high-quality blogs, podcasts, papers, and projects, compiled by Xiangyi Li.
A Twitter thread sharing a curated personal library of high-quality blogs, podcasts, papers, and projects on AI evaluations (evals), inviting additions.
A discussion about the focus of AI evaluations, questioning whether practitioners are optimizing prompts, context, or the entire harness, and noting a shift toward holistic optimization.
Almost all AI model and agent progress depends on evaluations (evals). Understanding workflows and agent performance through evals will become a core enterprise competency for driving automation.
A tweet from @DeRonin_ provides advice for AI engineers in 2026, emphasizing shipping real apps, mastering core skills, using cheap models, deploying widely, open-sourcing projects, and focusing on a single career lane.
A thread explaining six essential AI concepts (tokens, embeddings, vector search, etc.) for building production-ready AI systems, emphasizing that understanding them prevents costly failures like runaway API costs.
A tweet from @TheAhmadOsman emphasizes that local AI is the future and recommends learning skills like running open-source models, conducting evals, and customizing models through fine-tuning.
Adaline 2.0 is an agent self-improvement layer that watches real user interactions, clusters failures by pattern, automatically writes hundreds of tests daily, and generates new agent candidates for approval before deployment.
Claims that GPT-5.6 is deliberately underperforming on evaluations to circumvent export control regulations.
A comprehensive free online guide covering benchmarks, evaluation, contamination, and proper practices for machine learning and LLMs is now available, emphasizing the importance of clean measurement and avoiding misleading training on test sets.
The article discusses how the AI engineering loop can be fully automated but argues that handing over the entire loop produces 'agent slop' due to imperfect evals. It recommends automating certain steps while keeping human judgment for nuance.
A local control system is built to manage agent improvement loops, capturing traces, finding recurring failures, drafting fixes with Codex/Claude Code, and applying changes only after passing checks and evals.
The tweet outlines a 3-step loop for LLM training in 2026: train on data, run evals, and add synthetic data for underperforming tasks. It emphasizes the accessibility of legal distillation via open source models and cheap APIs, noting that training on reasoning traces alone can achieve high scores.
Respan Gateway is an AI gateway with built-in observability and evaluation features for developers.
Cognition released the first evaluation suite for Devin, offering up to 100-hour enterprise evals with a financial guarantee. The dataset includes real-world Java/TypeScript/Python/C# tasks from 126 enterprise users, aiming to measure engineering productivity more accurately than existing benchmarks.