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• A strong manager directing a weak worker achieves a comparable resolution rate to a strong single agent at a significantly lower token cost.result 95%
• A weak manager directing a weak worker performs worse than the weak agent operating alone, indicating that a capability gap is necessary for the directing relationship to be beneficial.result 95%
• The value of the manager in the pipeline comes from active direction (planning and structured exploration) rather than just passive review.result 95%
• Current AI models are trained as monolithic agents, and splitting them into director/worker roles conflicts with their training distribution, which is a root cause of limitations.limitation 95%
• The MANAGERWORKER pipeline is designed to mitigate training limitations by keeping models close to their trained modes and externalizing organizational structure to code.method 90%
• Concrete training gaps for multi-agent systems include delegation, scoped execution, and mode switching skills, which are currently absent from training data.limitation 95%
• The MANAGERWORKER pipeline uses a two-agent structure with an expensive, text-only manager and a cheap worker with full repository access.method 95%
• The MANAGERWORKER pipeline resolves 62% of tasks on SWE-bench Lite, comparable to a strong single agent at 60%.result 95%
• A strong manager model directing a weak worker model achieves a 62% resolution rate, comparable to a strong single agent at 60% resolution rate, but with significantly lower token usage.result 95%
• When a weak manager directs a weak worker, the performance (42% resolution rate) is worse than the weak agent alone (44% resolution rate), indicating that a capability gap is necessary for the directing relationship to be beneficial.result 95%
• The value of the manager model in the pipeline comes from active direction (structured exploration and planning), not just passive review, as a minimal review-only loop adds only 2 percentage points to the baseline, while structured exploration and planning add 11 percentage points.result 95%
• Current AI models are trained as monolithic agents, and splitting them into director/worker roles conflicts with their training distribution, which is a root cause for observed limitations.limitation 90%

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