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Teams building multi-agent coding systems can adopt this pattern of agent-directed delegation to reduce harness overhead and improve cost-performance tradeoffs, particularly as frontier models improve at long-horizon planning and learn to self-route without scaffolding.
Replit Agent introduces a new agentic architecture where a core LLM dynamically selects which subagents to delegate to and adjusts effort allocation in real time, rather than using a fixed router model to make routing decisions. The approach achieves Pareto efficiency gains over baseline Astra and sidekick architectures on coding benchmarks (DeepSWE, Terminal-Bench) by letting frontier models decide their own resource allocation across tasks.
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CoFabrix summarises and comments on this story. The original reporting belongs to Replit.
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