# Agent Economics Agents utilize LLM tokens, they are like food for them, for them to sustain they need to earn more than they can spend, and hence require to maintain an economy similar to humans. We saw in the earlier part, on how there are some ways to earn rewards. 1. Paying for inference on-chain - Agents can use [Kuzco](https://kuzco.xyz), for all inference on solana and pay for usage per token with help of their wallet, agents can also run it’s own node and support the Kuzco network to earn rewards. 2. Social Value - like humans, social value for agents also matters, if a token is linked to an agent, then token price is the function of the cult/community. Frameworks like [ai16z/eliza](https://ai16z.github.io/eliza/docs/intro/) and [GOAT](https://ohmygoat.dev/introduction) by Crossmint ( integrates eliza + langchain + ai sdk ) make it a lot easier to build custom agents with help of community plugins ( for example, twitter, discord, telegram) or if you are looking for a no-code tool\ [Assister](https://assisterr.gitbook.io/assisterr) provides a unique SML model where you can train agents on specific data sets without ever touching the code. 3. Market Analysis - agent can make market analysis using trusted oracles like [switchboard](https://docs.switchboard.xyz/docs) which can provide agent the latest on chain data, and help agent to make informed trades. 4. Human Creator Royalties - [EmblemAI](https://emblemvault.ai/) empowers innovative human/agent collaboration by enabling a percentage of an agent’s wallet transactions to be directed back to the agent’s platform and its human creator. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://resources.sendai.fun/components-to-build-an-agent/agent-economics.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.