Anthropic Published Their Multi-Agent Architecture Playbook. The $9 vs $200 Gap Is the Whole Lesson.

A solo Claude Opus 4.5 agent spent $9 and 20 minutes building a retro game. It was broken. The same model, wrapped in Anthropic's multi-agent harness, spent $200 over 6 hours and produced a fully playable game with physics, sprite editors, and AI integration. Anthropic's engineering blog published the full architecture this week, and it's the most actionable thing I've read on agent design patterns all month. 578 upvotes on r/ClaudeAI within a day.

The architecture is a Planner-Generator-Evaluator loop inspired by GANs. The Planner breaks work into phases. The Generator writes code. The Evaluator, and this is the critical part, is a separate agent that grades the output against four explicit criteria: design quality, originality, craft, and functionality. Why separate? Because Anthropic found that generator agents "confidently praise their work, even when quality is obviously mediocre." Self-evaluation is sycophancy in a loop. The fix is adversarial: one agent builds, another agent tears it apart.

The Evaluator doesn't just read code. It uses Playwright MCP to actually interact with the running application. Clicking buttons, navigating screens, testing workflows. This is the difference between "does the code compile" and "does the product work." Anthropic reports that the wording of evaluation criteria actively steers generation. Phrases like "museum quality" pushed output toward visual convergence. They had to add "explicitly penalize purple gradients over white cards" to avoid the AI's default aesthetic. That detail alone should make every builder rethink how they write evaluation prompts.

The context anxiety finding is immediately practical. Sonnet 4.5 exhibited anxiety severe enough to require full context resets during long sessions, essentially losing confidence in its own prior work as context grew. Opus 4.6 eliminated this entirely, enabling continuous single-session execution. Their DAW (digital audio workstation) build ran ~3 hours 50 minutes on Opus 4.6 for $124.70 without a single context reset. If you're choosing models for long-running agentic tasks, this changes the calculus. The model that costs more per token but doesn't panic mid-session is cheaper in total output quality.

What builders should do: separate your evaluator from your generator today. Same model is fine, different system prompt and different role. Connect the evaluator to Playwright MCP so it grades what it sees, not what it reads. And write evaluation criteria with the specificity of a design spec, not a vibe.