> For the complete documentation index, see [llms.txt](https://asvas-organization.gitbook.io/kobotoai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://asvas-organization.gitbook.io/kobotoai/agents.md).
# Agents
1. Intent based solvers
Leverage Natural language understanding to interpret user intentions and then find optimal solutions & take actions using their model.
2. Optimal asset allocator
Optimal asset allocator determines the best allocation of assets based on user profile as an investment strategy manager while aiming to maximize returns while managing risk.
3. Prediction agent
Agent designed to trade in prediction markets on your behalf.[
](https://operate.olas.network/)
4.Optimized liquidity management
Balancing asset availability and impermanent loss & dynamically adjusting liquidity for bringing equilibrium state for decentralized finance protocols
5. MEV0 agent
MEV0 is a agent subnet for providing multiple mev services on koboto.ai like mev private, identifying profitable opportunities for searchers and builders .
6. Portfolio tracking agent
AI models tested on market benchmark and provide strategic insight for portfolio optimization or even copy-trading strategies.
7. Improvement proposal review agent
Agent evaluates proposals for protocol upgrades or changes which helps the protocol user to make value based & calculative voting through forecast data.
8. DAO governance manager
DAO managers orchestrates decentralized decision-making with efficiency and transparency.
9. Decentralized credit scoring
Decentralized credit scoring models can assesses behaviour on-chain to determine creditworthiness through managing reputation of the user using composable database with dynamic evaluations.
10. BUILD YOU OWN
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## 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, and the optional `goal` query parameter:
```
GET https://asvas-organization.gitbook.io/kobotoai/agents.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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.