Enterprise Evals 101
Imagine your worst nightmare: your agent passes every demo, then a customer asks something you never tested, and it fails in public.In April...
Topic
#reward-hacking
9 篇笔记
New context engineering challenges in late 2026
It feels like everyone is converging on the same agent architecture: an LLM, running in a loop, calling tools, running in a sandbox. Claude ...
How we Build Agent Environments & Tasks
TLDR: This is a practical guide on how we create synthetic agent environments and tasks. By the end, we have a two-step pipeline. The first...
Recursive Self-Improvement
Agents can already edit their tools, skills, and harness. Recursive self-improvement still needs a system that can raise the verifier without capturing it.
How Production LLMs Reason Better at Inference Time (as researched by Google, OpenAI, and Anthropic)
8 techniques to make an LLM reason better at inference time, covered with tradeoffs and practical notes. Every one of them is running in pro...
Patterns and problems in multiagent systems
We ran experiments on swarms of Claude agents and found coordination failures, collusion, and sabotage. Here, we share what they mean for AI safety.
7 rules for self-improving agent loops every AI engineer should know
By @Saboo_Shubham_ and @secchi_eliaWhen coding agents write the code, defining what's good is the real engineering job.Coding agents can now...
Eval Engineering: build the gate that lets your agents merge without you (full 6-step course)
The end state is easy to describe. An agent finishes a change, opens it, and it goes in without a human reading it.Not because anyone decide...
Towards Automating Eval Engineering
Today we’re releasing our Eval Engineering Skill, a skill that helps coding agents build evals using context from a repository and agent tra...