Anthropic's Claude Code team describes using additional agents to challenge suspected bugs before sending findings to a human. Several agents search for problems, then further reviews test whether each finding is real. This filtering matters because parallel reviewers can otherwise produce more material than a person can use. Team discussion.
The team describes human review moving toward architectural context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →: why an interface has a particular shape, or why services are separated in a certain way. The workflow aims to automate detailed inspection while preserving a place for decisions that depend on reasons the 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 → may not know.
Key takeaways · AI-distilled
Give the agent access to the context and tools needed to develop and verify the product end to end; remote execution alone does not provide that setup.
Use code-review fan-out to find candidate issues, then independent checks to reduce false positives before presenting findings to a human.
Treat 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 → features as responses to current model limitations and reassess them when the model or task scope changes.
Terms in this piece · Glossary
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.
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.
