Subagents Account for Most Token Costs in Long Agent Runs: Fixes That Cut Usage 70 to 90 Percent in Practice

Reddit r/artificial / 6/3/2026

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Key Points

A 2026 study (Bai et al.) analyzing SWE-bench agentic coding found that multi-agent/agentic tasks use about 1,000x more tokens than normal chat and show large token variance, with accuracy not improving proportionally to spend.
In practice, a tracked research synthesis run that hit 450,000 context tokens was reduced to around 85,000 tokens after adding controls that keep key files (PLAN.md/INVARIANTS.md) and fresh budgets out of the rolling conversation window.
The article’s proposed fixes include using a per-turn 2,000-line read-budget gate, keeping coordination notes for subagents out of the main transcript, and rethinking what historical content is re-queried.
For dynamic tool discovery, one harness cut input tokens by 96% and total cost by 90% by loading tool schemas only for tools the agent actually selects instead of injecting a full catalog every time.
The post concludes with an implementation checklist and invites readers to share token/cost issues seen in their own long-running agent sessions.

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