Lessons from Building Claude Code: Prompt Caching Is Everything
Prompt caching isn't just an optimization for AI agents—it's the fundamental infrastructure that makes long-running agentic products like Claude Code economically feasible at all.
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TLDR
• Long-running AI agents are only viable because of prompt caching—without it, the economics collapse
• The classic engineering principle "Cache Rules Everything Around Me" applies directly to agent architecture
• Claude Code's feasibility as a product depends on aggressive prompt caching strategies
• Caching should be treated as a first-order architectural constraint, not an afterthought optimization
In Detail
The core insight is that prompt caching transforms from a performance optimization into an existential requirement for agentic products. While traditional software engineering has long understood that caching is critical for performance, AI agents face an even more fundamental constraint: without prompt caching, the token costs of long-running agent sessions make the product economically impossible to operate.
Claude Code serves as a case study for this principle. The product's viability depends on caching large portions of the context (codebase, conversation history, tool definitions) that remain stable across agent turns. Without this, every agent action would require reprocessing the entire context, multiplying costs by orders of magnitude. This reframes how you should approach agent architecture—caching isn't something you add later for speed, it's the foundation that determines whether your product can exist at all.
The implication is that anyone building agentic products needs to design around caching from day one. Your architecture, your context management strategy, and your product economics all flow from how effectively you can cache and reuse context across agent interactions.