3 posts tagged with "llm-optimization"

Your AI agent is brilliant at reasoning, planning, and generating natural language. Then you point it at your enterprise SAP endpoint and it spends 4,000 tokens trying to understand a SOAP envelope. Welcome to the impedance mismatch — the quiet tax that turns every enterprise AI integration into a token bonfire.

The mismatch isn't just about XML versus JSON. It's a fundamental collision between how LLMs think — natural language, flat key-value structures, concise context — and how enterprise systems communicate: deeply nested schemas, implementation-specific naming, pagination cursors, and decades of accumulated protocol conventions. Unlike a human developer who reads WSDL documentation once and moves on, your agent re-parses that complexity on every single invocation.

Most teams deploying AI agents implement prompt caching, maybe add a semantic cache, and call it done. They're leaving 40-60% of their potential savings on the table. The reason isn't laziness — it's that agentic workloads create caching problems that don't exist in simple request-response LLM calls, and the solutions require thinking in layers that traditional web caching never needed.

A single agent task might involve a 4,000-token system prompt, three tool calls that each return different-shaped data, a multi-step plan that's structurally identical to yesterday's plan, and session context that needs to persist across a conversation but never across users. Each of these represents a different caching opportunity with different TTL requirements, different invalidation triggers, and different failure modes when the cache goes stale.

Most teams know how many tokens their users send. Almost none know how many tokens they spend before a user says anything at all.

In a typical production LLM pipeline, system prompts, tool schemas, chat history, safety preambles, and RAG prologues silently consume 30–60% of your context window before the actual user query arrives. For agentic systems with dozens of registered tools, that overhead can hit 45% of a 128k window — roughly 55,000 tokens — on tool definitions that never get called.

This is the hidden token tax. It inflates costs, increases latency, and degrades output quality — yet it never shows up in any user-facing metric.