> For the complete documentation index, see [llms.txt](https://docs.lazai.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lazai.network/quorum-based-bft-consensus/introduction.md).

# Introduction

LazAI’s Quorum-Based BFT (QBFT) consensus is a modular and scalable consensus protocol optimized for AI-centric decentralized systems. It blends practical Byzantine Fault Tolerance (pBFT) with a Quorum-based voting mechanism to ensure efficient validation, integrity, and liveness in a multi-agent AI data network.

<figure><img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdBvvR52Ynowbhty0CCtXQ7qRKga0pEy0hZ1NvGfsdvbObfmI70HSRsE9eD8bv1tEhTkUZCzrGBWWqh4gxdkcdp33moIgMEadUW6RbugmavxhTt_pMJIv6KtW-Bf_CfKQ6U6Y-ZcQ?key=S_AVxe4Pa4t7GMdXHRa1L20C" alt=""><figcaption></figcaption></figure>

### Key Entities

<table data-header-hidden><thead><tr><th width="154.265625"></th><th></th></tr></thead><tbody><tr><td><strong>Component</strong></td><td><strong>Role</strong></td></tr><tr><td>Quorum</td><td>A logical group of validators (iDAO members) responsible for data validation.</td></tr><tr><td>Proposer</td><td>A rotating member of the quorum responsible for proposing the next block/data state.</td></tr><tr><td>Validator</td><td>iDAO participant who verifies, signs, and votes on proposed blocks.</td></tr><tr><td>Challenger</td><td>External party capable of submitting fraud proofs or challenges.</td></tr></tbody></table>

### Design Principles

* **AI Data-Aware Consensus:** Handles metadata-rich, non-deterministic AI data (e.g., dataset hashes, ZK proofs, computation logs).
* **Quorum-Based Delegation:** Only assigned quorums participate in consensus, improving scalability.
* **Dynamic Quorum Rotation:** Quorum members are periodically re-elected or rotated for decentralization.
* **Hybrid Validity Layer:** Supports ZK-SNARK/TEE proofs and Optimistic Proofs (OP) as validity inputs.


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.lazai.network/quorum-based-bft-consensus/introduction.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.
