How 12 teams assemble agent context: fixed prefixes for cache, retrieval policy set by miss cost
Gooner-Psy · reddit · 2026-10-26
Follow-up to the author's 12-team agent-harness comparison, this long piece covers one job: assembling context.
Consensus: keep the prompt start fixed
- Codex makes each new prompt an exact prefix of the last for caching; Claude Code treats low cache hit rate as an incident; Manus calls KV-cache hit rate its most important production metric and never adds/removes tools mid-run; Shopify returns instructions alongside tool results so they can change without breaking the cache
Instruction files stay small: OpenAI's harness team uses a 100-line AGENTS.md pointing elsewhere; Codex caps combined instruction files at 32 KiB; Stripe uses global rules sparingly
The split: who fetches the rest
- Coding agents let the model search: Cursor moved MCP tool descriptions into files, cutting total agent tokens by 46.9% on MCP runs; Cognition argues polluted context beats missing context since the agent is a few searches from recovery
- Agents whose misses reach customers retrieve first: Fin's gate is tuned for recall and fails open; Hebbia found chunked retrieval failing on 84%+ of finance queries and moved to reading relevant document pieces in full
Author's stance: cheap misses → let the agent search; customer-facing misses → retrieve for recall and pay for tokens. Gap: nobody publishes cost per successful task. The post doubles as a test case for his own pipeline, with a planted contradictory source.
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