10 Ways to Cut AI Agent Token Usage by 72% Without Losing Accuracy
Emotional_Collar_449 · reddit · 2026-08-10
The author shares a practical guide on reducing token consumption for a multi-role AI agent (planner → tool-user → writer) by 72% (from 11.9k to 3.3k per task). Over 200 evaluation runs, the success rate remained flat (93.8% → 93.1%).
Key optimization techniques include:
- System Prompt Diet: Rewrote instructions as checklists, used short IDs for tools, and referenced schemas by name (saved 960 tokens).
- Tight RAG Retrieval: Used 256-token chunks, top-6 results, and passed only exact snippets instead of whole documents (saved 2,120 tokens).
- Project Tool Outputs: Modified tools to return only downstream-required fields instead of full blobs (saved 1,600 tokens).
- Pass Deltas: Kept long-term memory server-side and passed only new/changed facts instead of the whole scratchpad (saved 820 tokens).
- Cap Planning Loops: Forced summaries if no new entities/tools were introduced, reducing planner turns from 5.2 to 2.1 (saved 1,100 tokens).
- Classify & Cache: Routed trivial tasks via a lightweight gate and cached deterministic answers (saved 1,000 tokens on average).
- Response-Length Contracts: Enforced hard caps like '<= 5 bullets' and set strict maxoutputtokens limits.
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