You have written this loop before. Eighteen months ago, when you first put a Claude agent into production, you wrote a rubric. You wrote a grader. You wrote retry logic for when the grader said no. The pieces broke. You patched them. The rubric drifted. You rewrote it.

On May 6 at Code with Claude San Francisco, Anthropic shipped your loop as an API endpoint and called it Outcomes.

That is the news. The story underneath it is bigger. Outcomes is the first harness layer Anthropic decided to sell. Dreams, Multi-Agent, and Webhooks are the same move on memory, orchestration, and lifecycle. The harness used to be code you wrote. It is becoming a stack of products you compose. — Read More 

For six weeks you fought Claude Code. Prompts that used to work stopped working. Usage limits drained twice as fast. Sessions felt forgetful, repetitive, oddly lazy. You blamed yourself. You blamed your prompts. You read the Reddit threads where someone calmly explained that the model is fine and you’re holding it wrong.

On April 23, Anthropic published the receipts. The model was fine. Three things in the harness around it were not.

Three changes. Three schedules. Three bug fixes. Each shipped through code review, internal evals, and dogfooding. Each survived weeks before users forced the diagnosis. The post mortem is the cleanest field experiment in harness engineering anyone has published. The pattern in it is more important than the bugs. — Read More

#devops

Everyone wants to build an AI agent right now.

Not just a chatbot. Not just a prompt wrapper.

A real AI agent — something that can understand goals, use tools, remember context, interact with users, and improve over time.

…[B]uilding an agent is not one decision. It’s a system design problem. An AI agent only becomes useful when several layers work together — purpose, prompts, models, memory, orchestration, interfaces, and evaluation. — Read More

#devops