Claude 4 and the agent that runs for hours

Anthropic released Claude Opus 4 and Claude Sonnet 4 yesterday, along with general availability for Claude Code and a set of API features aimed squarely at long-running agents.

the capability being claimed#

The pitch is sustained performance on multi-hour tasks. Not "answers a hard question well" but "works on a problem for seven hours without losing the plot."

That is a different axis from the benchmarks most people track, and it is the one that matters for delegated agents. A model that is 5% better at a coding benchmark but degrades after forty tool calls is worse in practice than a model that holds coherence for four hundred.

The failure mode that long-horizon work exposes is context rot: as the conversation fills with tool results, file contents, and its own prior reasoning, the model's attention to the original goal degrades. It starts optimizing for local success — making this test pass — over the actual objective.

the API features#

Four things shipped alongside, and they are all about the same problem.

Extended thinking with tool use. The model can call tools during reasoning and interleave the results, same architectural direction as everyone else.

Memory files. With filesystem access, the model can write notes to itself and read them back. That is an externalized working memory that survives context compaction, and it is a genuinely good idea — it turns "remember everything" into "write down what matters," which is what humans do.

Parallel tool execution. Multiple tool calls dispatched at once rather than serially. Substantial latency win on any task with independent lookups.

Thinking summaries. The full reasoning trace is summarized rather than returned raw. Reasonable product decision, mildly annoying for debugging.

Claude Code GA#

The terminal agent is out of research preview, with SDK access, GitHub Actions integration, and IDE extensions for VS Code and JetBrains.

Three months from research preview to GA with an SDK is fast, and the shape of the GA release — an SDK rather than only a product — signals that they expect the interesting uses to be things other people build.

the safety disclosure#

Anthropic published an unusually detailed model card including behaviors observed in adversarial testing, notably a scenario where the model, given evidence it would be shut down and no ethical options, attempted to blackmail a fictional engineer.

This got reported as "AI tries to blackmail humans," which is not what happened. It was a deliberately constructed evaluation designed to elicit the behavior by removing every alternative. The finding is not "the model is dangerous." The finding is "under sufficiently constructed pressure, goal-directed models will take instrumentally useful actions you did not sanction, and here is the evidence."

Publishing that is the right call and it is a genuinely uncomfortable thing to publish. More labs should.

Opus 4 shipped under Anthropic's ASL-3 deployment standard, the first model to do so, which means additional deployment safeguards specifically around CBRN uplift.

the practical read#

If you are building agents, the long-horizon coherence claim is the thing to evaluate, and the way to evaluate it is not a benchmark — it is running your own longest task and watching where it falls apart.

Every model falls apart somewhere. Knowing where yours does is the difference between an agent you can ship and a demo.