Stage 3 synthesis executed in parallel using Haiku agents with 53K-token prompt caching
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obs· Harvested: 2026-05-03 · Original date: 2026-05-03T13:18:06.358Z Metadata:{"project":"lunhsiangyuan","type":"feature","obs_id":65037}
obs/65037 · feature · 2026-05-03T13:18:06.358Z
Stage 3 synthesis executed in parallel using Haiku agents with 53K-token prompt caching
Stage 3 synthesis execution demonstrates highly optimized parallel narrative generation using Haiku subagents. The system spawned three agents simultaneously, each reading its topic-specific synthesis prompt (containing full embedded unit content) and generating Karpathy-style narratives. Massive prompt caching efficiency was achieved with 53K+ cached tokens reused versus only 5 fresh input tokens per agent, reducing API costs dramatically. Each agent completed synthesis in 15-23 seconds using a single Read operation - the self-contained prompt architecture eliminated need for additional context lookups. Output token counts (713-1,148) scaled with input unit count (6-15 units), demonstrating predictable resource usage. Narratives were returned as agent content blocks rather than written to files, enabling the orchestrator to validate citations and quality before persistence. The parallel execution pattern with Haiku model choice (per CLAUDE.md cost optimization rules) balances synthesis quality with operational efficiency.
Concepts: [“how-it-works”,“pattern”,“trade-off”]
Facts: [“Three synthesis agents spawned in parallel for software-devops (15 units), grants-compliance (10 units), strategy-analysis (6 units)”,“All agents used Haiku model completing in 14.5-22.7 seconds with 1 Read tool call per agent”,“Prompt caching achieved 53,384-53,388 cache read tokens versus only 5 input tokens per agent”,“Cache creation tokens varied by prompt size: 19,026 (software-devops), 17,018 (grants-compliance), 13,767 (strategy-analysis)”,“Output lengths proportional to unit count: 713 tokens (15 units), 1,028 tokens (10 units), 1,148 tokens (6 units)”,“All narratives returned as agent content with proper unit_id citations in [📎 unit-id] format, no file writes performed”]
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