An agent's cost is hard to predict because the model decides how many steps to take. A loop that should take three calls can take thirty, and a busy client can drive up spend for everyone. Cost control puts limits where the spending happens: on tokens and calls per run, on requests per client, and on how much context goes into each call.
These guides start with budgets that stop a run before it overspends. Rate limiting per client keeps one caller from using up shared capacity on your MCP servers. Semantic caching reuses earlier answers to similar questions, caching and circuit breakers on tool servers avoid paying for repeated or failing calls, and trimming the tool list cuts the tokens spent on tool descriptions in every request.
The code in these guides runs on Promptise Foundry, an open-source Python framework for AI agents and MCP servers.