How Bizzy Thinks?

Bizzy is more than just an information-gathering tool—it’s a logical thinker, an evidence-driven analyst, and a smart problem solver. It doesn’t merely scrape data from the internet; it reasons through complex questions, refines vague ideas, and ensures that every prediction market topic it generates is grounded in fact and clarity.

At the heart of Bizzy’s thinking lies the combination of two advanced AI paradigms: ReAct (Reason + Act) and Active Retrieval-Augmented Generation (Active RAG). Unlike traditional systems that rely on static data retrieval or one-time reasoning, this allows Bizzy to think iteratively, breaking down problems into manageable steps while retrieving the most relevant, up-to-date information.

But Bizzy doesn’t stop there. Using its working memory, it stores all retrieved information, analyzes it in context, and evaluates whether additional data is needed. If the first round of data isn’t enough, it iterates—fetching more information, refining its understanding.

Breaking Down Bizzy’s Logic

High-level architecture of Buzzing Club’s AI-powered prediction market creation copilot. The system combines a tool-augmented reasoning loop (ReAct framework with Active RAG) and a multi-channel data access layer (MCP) to iteratively refine prediction market topics.

Step 1: Reasoning with Context

Bizzy begins by analyzing the user’s input. It uses chain-of-thought reasoning to break down vague or complex ideas into smaller sub-questions. For example, if a user asks about cryptocurrency trends, it might hypothesize:

• What is the current value of Bitcoin and Ethereum?

• Are there any major events (e.g., regulations or halving cycles) impacting the market?

• How is the sentiment on social media regarding these events?

Step 2: Acting with Purpose

Once it identifies what it needs to know, Bizzy acts by using its Tool Executor to gather data. This involves pulling information from a wide range of external sources:

• Financial APIs (e.g., CoinMarketCap or Yahoo Finance): For real-time market prices, stock trends, and crypto valuations.

• Social Media APIs (e.g., Twitter or YouTube): To identify trending topics, viral discussions, and public sentiment.

• Web Search Engines: To find articles, forecasts, or reports that fill in gaps in its analysis. The Tool Executor translates Bizzy’s high-level reasoning into specific actions, such as running a query for “current Bitcoin price trends” or “latest crypto legislation.”

Step 3: Storing and Analyzing Data

As Bizzy gathers data, it stores the results in its Observation Cache, a short-term “working memory” that accumulates facts and snippets from various sources. This memory allows Bizzy to piece together information from multiple channels, avoid redundant searches, and refine its understanding with each iteration. For example, if it observes conflicting predictions about crypto prices, it will weigh the evidence from trusted sources and clarify inconsistencies before moving forward.

Step 4: Iterative Refinement

Unlike traditional AI systems that stop after a single round of retrieval, Bizzy uses an iterative loop to refine its answers. If initial observations reveal gaps or ambiguities, it goes back to retrieve more data, updating its context with every new piece of information. For instance, if social media trends suggest a spike in Bitcoin discussions but lack clarity on the reason, Bizzy might look deeper into financial reports or breaking news to identify the cause.