Clarifies what the renamed Claude Developer Platform includes (APIs, SDKs, docs, console) and that Anthropic's own products run on it, useful for developers building agents on Claude.
Key takeaways · AI-distilled
The platform leads argue heavy scaffolding caps what a new model can show. When customers said a new model was only slightly better, Anthropic often found they were constraining it in ways that hid its added intelligence.
They recommend the Claude Code SDK as a general agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition →, not just for coding: once the team stripped Claude Code down, what remained was an agentic loopThe cycle an agent runs in: decide, call a tool, read the result, decide again — repeating until the goal is met or a stop condition fires.Full definition → with a file system, Linux command-line tools and code execution.
A platform lead notes Claude defaults to 200K tokens of context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, with 1M in beta on Sonnet, yet customers report better outputs when they use only part of it, as AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → loops of 10 to 100 tool calls fill the window quickly.
The context-editing feature removes tool results from several turns back that the model already acted on, keeps the most recent ones, and leaves a tombstone note saying results were removed, which they found helps the model.
The memory tool lets Claude take notes during a task and review them later, aiming to make it improve on repeated tasks the way people do. Developers, not Anthropic, currently choose where that memory is stored.
Terms in this piece · Glossary
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
agentic loop — The cycle an agent runs in: decide, call a tool, read the result, decide again — repeating until the goal is met or a stop condition fires.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
