"Separating the agent doing the work from the agent judging it proves to be a strong lever to address this issue."
"The evaluator navigates live pages, interacts with the interface using Playwright MCP, and provides detailed critiques to guide the generator in iterative cycles."
"Each cycle produces progressively refined outputs. Iterations range from five to fifteen per run, sometimes taking up to four hours."
"Long-running AI agents fail for a simple reason: every new context window is amnesia."
Anthropic's multi-agent harness design enhances long-running autonomous application development by assigning distinct roles to agents for planning, generation, and evaluation. This structure addresses common issues like context loss and task termination. Context resets and structured handoff artifacts allow agents to continue from defined states. A separate evaluator agent mitigates overrating of outputs, especially in subjective tasks. For frontend design, grading criteria include design quality and functionality, with iterative cycles producing refined outputs over several hours.
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