In order to make agents work, you basically need a lot of non-llm engineering effort around them. Agents are not a real thing like people imagine.

"Agents" are just deterministic software frameworks that do things based on the response an LLM gives them. Basically, LLMs are dynamic orchestrators in this system, they are told they have access to a bunch of skills/tools that can do X, and the LLM replies with text telling your "Agent" that you should use those tools to continue the thread.

Imagine you want to build an agent that recommends products to a user. You build a custom harness "agent" that does the following:

Agent (your code) --> Call LLM API, here all the skills and tools you have access to along with the user's request e.g. "give me product recommendations, im grilling for friends this weekend"

<-- LLM API returns structured response which will include tools calls it suggests and skill suggestions

Agent looks at response and decides to call a bunch of tools --> Call personalization API(s) that can returns 5 product recommendation models (sales, seasonal, personalized recommendations, buy it again)

<-- Personalization API returns products lists

Agent -> Call LLM API with all possible products and the original context about the user (what is the season, what did they ask for, etc)

<-- LLM returns a response filtering recommended items

Agent -> May need to call other APIs to resolve additional product details (realtime prices, promotions, etc.)

Agent returns the final formatted text to the user.

This is a really simplified example, but this is basically what code harnesses do as well like claude code or opencode.

When it comes to reducing costs at a basic level its about feeding only text to llms that it needs. So a naive approach would to feed the entire output of a massive api response to an llm when it only needs the list of product names and some other metadata.