Scaling GPU KV-Cache Efficiency and Inference Concurrency for Autonomous Agents with HashedContext

Autonomous agentic coding systems routinely transmit source files to Large Language Models (LLMs) across multi-turn software development workflows. In conventional architectures, unchanged files are re-read from storage and redundantly re-serialized into append-only conversation histories on every interaction step. This produces an asymptotic token growth of O(M × N × P), which severely exacerbates the GPU High-Bandwidth Memory (HBM) “memory wall” by saturating key-value (KV) cache allocations and degrading multi-tenant serving concurrency. We introduce HashedContext, a content-addressed context architecture that eliminates redundant file transmission and optimizes physical KV-cache utilization across multi-turn agent sessions. HashedContext couples four complementary mechanisms: (1) SHA-256 hash-gated file state tracking, (2) historical conversation deduplication via compact references ([HASH_STUB]), (3) attention-anchored authoritative tail rebasing, and (4) on-demand historical state resolution from a local content-addressed vault, and supports a bimodal deployment topology operable either edge-natively within client editors or transparently at server-side inference gateways. Empirical evaluations on production inference endpoints (DeepSeek V4) demonstrate an immediate 57.7% to 67.2% reduction in active prompt tokens on basic 4-to-5 turn workflows, with asymptotic savings mathematically bounded to 90%–97% on extended multi-file refactoring sessions. We observe no detectable degradation in model reasoning accuracy or diff resolution fidelity.