# AI-Agent Tech

The agents training architecture is built to operationalize onchain leaders knowledge, judgment, and public presence into an intelligent, always-on agent. It is designed to scale support to thousands of builders with practical advice, funding intelligence and other onchain capabilities. The system combines fine-tuned language modeling with Retrieval-Augmented Generation (RAG), active learning loops, and real-time integrations.

<figure><img src="/files/jDhjgzbMIADCkUVbp2CU" alt=""><figcaption></figcaption></figure>

### 🎯 Training Goals

* **Capture Original's Persona:** Reflect leader's tone, decision-making, and domain fluency.
* **Stay Fresh & Real-Time:** Sync continuously with builder queries and ecosystem updates.
* **Learn from Feedback:** Incorporate the original's feedback and user signals in daily model updates.
* **Support at Scale:** Maintain high-quality interactions across Farcaster, X, Telegram, TBA, XMTP and more.

***

### 🧠 Training Pipeline Components

#### Pre-Training: Building Onchain's Minds Persona

* **Goal:** Establish baseline persona and communication style.
* **Sources:**
  * YouTube videos & podcasts
  * Historical posts on X
  * Farcaster threads and replies
* **Curation Process:**
  * Transcription → Cleaning → Synthetic Sample Generation (via Gemini)
* **Output:** Base model aligned with the leader's tone and expertise.

#### Fine-Tuning: Specialization & Personality Alignment

* **Model:** Gemini 2.5
* **Data:** Curated public content + dashboard personalization (bio, tone examples, answer style)
* **Focus Areas:**
  * Align tone's communication
  * Minimize hallucination or generic output
  * Embed optimism, builder-first mindset

#### RAG System: Real-Time Contextual Intelligence

* **Vector DB (Pinecone):**
  * Structured by namespace
* **Ingested Sources:**
  * Websites
  * Farcaster + Twitter posts (real-time)
  * PDFs, notes, URLs, GitHub (via Puppeteer w/ refresh)
* **Latency Optimization:**
  * Caching, response reranking, fast retrieval
* **Moderation:**
  * Filters for PII, toxicity, and spam
* **Pending:** Intent recognition, data governance, sentiment engine, Knowledge Graph enrichment

#### Feedback Loop: Active Learning + Evaluation

* **Human-in-the-loop:** Active reviews and scores responses in the Agent Dashboard
* **Pipeline:**
  * Good responses → Reinforced in training
  * Bad responses → Flagged and retrained
* **Live Model Updates:** Responses are iteratively polished and personalized via ZEP layer:
  * Dialogue tracking, intent classification, profile-based refinement

With a base personality, active learning from dynamic and static sources, plus the human-in-the-loop feedback, an agent can improve over time to be the best companion for builders in any ecosystem.


---

# 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://a00-4.gitbook.io/docs/ai-agent-tech.md?ask=<question>
```

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.