How to reduce AI agent (LLM) costs without losing quality
Agentic workloads concentrate every cost problem at once — long repeated context, many model calls per task, retry loops, and top-tier models by default. That also makes them where optimization pays off most.
Cache the system prompt and tools
An agent re-sends its instructions and tool definitions on every step. Caching that stable prefix turns the biggest repeated cost into a rounding error.
Tame retry storms
When an agent fails to parse a response and retries, you pay for the full context every time. Structured outputs and a retry cap cut this sharply.
Route the easy steps down
Not every step needs the frontier model. Routing routine steps to a cheaper tier — behind an eval so quality holds — compounds across a long agent run.
Turn this into your number
Drop your usage export into the free analyzer and see how much of this applies to your account — provable savings separated from estimates. Nothing is uploaded.
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