KiraTex ($KIRA)
Recent developments:
0% of the initial token allocation is held by the creator.
Creator token stats last updated: Jun 29, 2025 22:54
The following is generated by an LLM:
Summary
prediction market AI with creator incentive risk
Analysis
KiraTex ($KIRA) presents an ambitious AI-powered prediction market platform leveraging ReAct and Active RAG models. Key risks: Creator holds 0% of token supply (0/1B tokens), raising severe incentive alignment concerns. No vesting/lockup info for team allocations. Tokenomics lack detail beyond phased utility claims. Positive factors: Partner integrations (Primus/Irys), Active developer progress through Virtuals hackathon graduation, and multi-phase roadmap with AI copilot → prediction market → InfoFi evolution. Unknown team backgrounds despite claimed 'veterans from top teams.' Token utility appears heavily dependent on unproven platform adoption. Creator wallet shows no token holdings (<5% threshold for serious projects), making this uninvestable regardless of other merits.
Rating: 2
Generated with LLM: deepseek/deepseek-r1
LLM responses last updated: Jun 29, 2025 22:55
Original investment data:
# KiraTex ($KIRA)
URL on launchpad: https://app.virtuals.io/prototypes/0x58Aab141277868594eB88359b4fd487c370618d2
Launched at: Sun, 29 Jun 2025 22:53:14 GMT
Launched through the launchpad: Virtuals Protocol
Launch status: UNDERGRAD
## Token details and tokenomics
Token address: 0x58Aab141277868594eB88359b4fd487c370618d2
Top holders: https://basescan.org/token/0x58Aab141277868594eB88359b4fd487c370618d2#balances
Liquidity contract: https://basescan.org/address/0x65949C58078b7b960202ce8086f7863E5DDd4D68#asset-tokens
Token symbol: $KIRA
Token supply: 1 billion
Creator initial number of tokens: Creator initial number of tokens: 0 (0% of token supply)
## Creator info
Creator address: 0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365
Creator on basescan.org: https://basescan.org/address/0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365#asset-tokens
Creator on virtuals.io: https://app.virtuals.io/profile/0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365
Creator on zerion.io: https://app.zerion.io/0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365/overview
Creator on debank.com: https://debank.com/profile/0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365
## Description at launch
Meet KiraTex, the AI copilot that powers your full lifecycle. KiraTex is a top 3 standout from the Virtuals Global Hackathon and the first social platform where users can bet on their beliefs around any trending topic. Built by veterans from top consumer and crypto teams, Kira merges product intuition with cutting-edge AI tech. Powered by cutting-edge ReAct, Active RAG models and zkTLS, it transforms how users trade, create, and resolve markets—offering unmatched speed, insight, and automation.
## Overview
**Introduce:**
**KiraTex** is the AI engine behind **KIRA** — a social media platform built on permissionless, AI-driven prediction markets.
**KiraTex was also awarded 3rd place at the** [**Virtuals Global Hackathon.**](https://x.com/virtuals_io/status/1914323687073001804)
Prediction markets let people trade on the likelihood of future events, much like trading stocks. With real money on the line, they tap into collective intelligence and often outperform traditional social media speculation.
But prediction markets still face serious friction. Placing a bet often requires deep research, yet users lack the tools and insights available in traditional finance. The topics people care about are fast-moving and diverse — but creating corresponding prediction markets and securing reliable oracle support is still a heavy lift.
**KiraTex changes all that.**
It analyzes data, surfaces insights, and helps users make smarter bets. As an AI copilot, KiraTex also assists users in creating new markets, providing oracle support for resolution, and bootstrapping liquidity — making it easier to turn any topic into a tradable conversation.
***
**Roadmap**
In the future, we aim to revolutionize prediction markets with AI-driven solutions.
Our journey unfolds in three phases — from an AI copilot, to a scalable prediction market, to a non-standard AI oracle.
## Phase 1:
In Phase 1, KiraTex is your ultimate trading assistant for prediction markets. Our AI analyzes markets and delivers actionable insights through intuitive interfaces, helping you make smarter betting decisions.
You can follow [@](https://x.com/Bizzy_agent)KiraTex on Twitter to get real-time insights for every new Polymarket market and even ask questions by tagging the bot. For a more immersive experience, access KiraTex’s web-based terminal — or activate our upcoming Chrome extension to receive tailored betting suggestions directly on Polymarket.
## Phase 2: KiraTex - AI-Driven Prediction Market
In Phase 2, join us in KiraTex, our AI-driven prediction market platform. Unlike traditional platforms like Polymarket, Buzzing leverages KiraTex’s automation to streamline rule creation, liquidity management, and oracle data, delivering unmatched efficiency and scalability. By leveraging AI, Buzzing is built to be:
* Your truth oracle and trends tracker
* Your first social media where every content feeds are backed by real money
* Your first curator community where ideas will be rewarded
Traditional prediction markets like Polymarket are fully human-driven — expensive, slow, and unable to keep up with the diversity of demand on social media. They can only launch a few hundred markets per month. Buzzing is powered by AI, cutting costs by hundreds of times and boosting speed by thousands, enabling the creation of tens of thousands of markets per day.
Here’s how we compare:
| Metric | Buzzing (AI-Driven) | Polymarket (Manual) |
| ------------------------------------ | ------------------------------------------------------------------- | --------------------------------------------------------- |
| AI Inference Cost (for 1,000 topics) | \~$18 ($0.75 per hour via Huggingface advanced endpoint\*) | $0 (no AI used) |
| Human Review Cost (for 1,000 topics) | \~$40 (0.3% flagged → \~3 topics reviewed by staff) | \~$15,000 (100% of topics manually reviewed by 2 staff\*) |
| Total Cost for 1,000 Topics | \~$58 (AI + minimal human) | \~$15,000 (all human labor) |
| Time to Process 1,000 Topics | \< 24 hours (AI parallel processing + quick check of flagged cases) | \~30 days (approx. 1 month of manual reviews) |
KiraTex focuses on creating markets in more vertical domains, catering to users who are eager to trade but struggle to find suitable venues. In the future, KiraTex will also join the ACP and collaborate with other agents, enabling domain-specific agents to create markets tailored to their expertise — including those related to Virtuals, such as predicting which Genesis launch project will be the next $aixbt.
## Phase 3: Ultimate InfoFi
In Phase 3, KiraTex evolves into a full Universal Truth Engine — working with other agents through the Agent Commerce Protocol ([ACP](https://app.virtuals.io/research/agent-commerce-protocol)) to fuse real-time financial data and AI insights. Together, they will power InfoFi: a new kind of financial social media where truth is verified, incentivized, and widely distributed.
InfoFi is a future where:
* Posts are not only opinions, but markets — backed by belief, evidence, and economic skin in the game.
* AI agents collaborate to validate facts, track narratives, and offer trusted insight.
* Users are rewarded for contributing accurate, valuable information, not viral noise.
By uniting AI and prediction markets, KiraTex helps build a more trustworthy financial web — one where the incentives push toward accuracy, transparency, and collective intelligence.
KiraTex will be at the heart of this transformation — a core agent in the InfoFi ecosystem.
***
**Token Utility**
The KiraTex Agent powers the core AI copilot capabilities behind prediction markets. The KiraTex Token is your key to unlocking this future — giving you access to advanced AI tools and a share of protocol revenue and incentives as the ecosystem grows.
Our team is fully committed to the long-term value of the KiraTex Token, and we will continue to grow its utility, role, and alignment with the protocol’s success. At each phase, the **KiraTex Token** unlocks powerful new benefits:
* **Phase 1 – AI Copilot**: Access advanced features like deeper insights, analytics, and smarter prediction assistance.
* **Phase 2 – Prediction Market**: Enjoy early access to the Buzzing platform, conversion rights into Buzzing Tokens, and a share in protocol revenue and governance.
* **Phase 3 – Ultimate InfoFi:** Unlock participation in a new class of financial social media. Use $BIZ to coordinate with other agents through ACP, access premium InfoFi tools, reward verified content, and help govern a future where financial information is backed by truth and economic incentives.
***
Bizzy is more than just an information collector—it’s a logical thinker and evidence-based analyst. Instead of simply scraping data, KiraTex carefully reasons through complex questions, clarifies vague ideas, and ensures every generated prediction market topic is factual and precise.
Powered by ReAct (Reason + Act) and Active Retrieval-Augmented Generation (Active RAG), KiraTex iteratively approaches problems, breaking them down step-by-step and actively retrieving the most relevant and current information. Utilizing its working memory, KiraTex continuously assesses retrieved data within context, identifies gaps, and iterates until it reaches a clear, well-defined prediction market topic.
**Breaking Down KiraTex’s Logic**
**LLM Planner/Reasoner:** The “brain” of the copilot is a large language model that uses chain-of-thought reasoning to plan and decide next steps. It employs the ReAct (Reason + Act) paradigm , interleaving reasoning about the prediction market query with tool use. The LLM analyzes the user’s request, formulates hypotheses or sub-questions, and determines which external information is needed. This Active Retrieval-Augmented Generation approach allows the agent to iteratively fetch new data during generation (not just once), so the LLM can refine topic suggestions based on the latest observations.
* **Tool Executor:** The planner’s decisions (actions) are handed off to the Tool Executor, which interfaces with external data sources. For example, the LLM might Reason that it needs current crypto trends, then Act by commanding a “CoinMarketCap lookup.” The Tool Executor translates these high-level actions into API calls via the MCP layer. It executes queries on the appropriate channel (finance, social media, web search, etc.) and returns the results (observations) back to the LLM. This component essentially serves as the agent’s “hands,” letting the AI perform actions in the outside world as guided by its reasoning.
* **Observation Cache (Working Memory):** As each tool action returns data, the information is stored in an observation cache (short-term memory). This working memory accumulates facts, snippets, and context retrieved from various sources. In the ReAct loop, the LLM’s next reasoning step takes into account these cached observations – updating its context with new information before deciding whether another retrieval is needed . By maintaining a memory of all retrieved evidence, the agent avoids redundant searches and can gradually converge on well-informed prediction market topics. (In a more advanced setup, a long-term retrieval memory or vector database could also be used to remember information across sessions, but the core loop relies on the immediate observation cache.)
* **MCP Orchestrator (Multi-Channel Protocol):** This is the external data access layer that manages and unifies calls to heterogeneous sources. The MCP orchestrator acts as a hub or plugin manager for the copilot – it receives tool requests from the agent and routes them to the proper external API or service. By abstracting multiple channels behind a common interface, the agent can ask for “current market data” or “latest social trends” without worrying about the specifics of each API. The MCP layer handles authentication, rate-limits, and formatting of queries/responses to each source. It enables the agent to seamlessly invoke multiple tools in one workflow, coordinating queries across finance (stock/crypto), social media, and web search as needed.
* **External Data Sources:** A variety of external information channels feed the copilot with up-to-date data:
* *Financial Data APIs*
(e.g. Yahoo Finance, CoinMarketCap): Provide real-time market prices, stock trends, crypto valuations, and financial news – useful for suggesting prediction markets on economic events or asset prices.
* *Social & Media APIs*
(e.g. YouTube Data API, X (Twitter) API): Supply insights on trending topics, viral videos, social sentiment, and breaking news in the wider culture. These help the agent identify popular discussion points or emerging themes that could inspire prediction markets.
* *General Web Search*
: Allows the agent to retrieve relevant articles, forecasts, or reference information from the internet at large. Web search fills in knowledge gaps and ensures no important topic is missed if it’s not covered by the dedicated APIs.
* The MCP orchestrator treats each of these sources as a plugin channel. During the reasoning loop, the LLM can flexibly call one or multiple channels (in parallel or sequence) via MCP – for example, first pulling financial stats from Yahoo, then checking social buzz on Twitter – depending on what information the agent’s reasoning requires. Each API response is fed back into the observation cache, enriching the agent’s knowledge.
* **Iterative Retrieval-Action Cycle:** The architecture above enables a tool-augmented reasoning loop: the LLM plans → acts → observes → (updates context) and then plans again, repeating as needed. This ReAct loop with Active RAG means the agent can start with a broad query, gather data in steps, and hone in on well-defined prediction market ideas. For instance, it might begin by asking “What events are trending in finance right now?” (via Yahoo/CMC), get some candidates, then follow up with “Are these topics generating buzz on social media?” (via X/YouTube), and so on. At each iteration, the agent’s internal planner refines the topic suggestions using the fresh data. Once sufficient information is gathered and the topics are refined, the LLM produces the final output – a set of proposed prediction market topics – which is then returned to the user. This design leverages cutting-edge agent paradigms (reasoning + tool use, retrieval loops, and plugin orchestration) to ensure the suggested markets are relevant, timely, and well-informed by the latest available data.
***
**About Buzzing**
**KiraTex** is a social media platform powered by prediction markets, allowing anyone to create markets around trending topics. Our AI agent, KiraTex, helps draft market content, provide liquidity, and support oracle services. By turning opinions into tradable markets, KiraTex makes online discussion more meaningful, transparent, and financially aligned.
The KiraTex team combines experience from major Web2 platforms and respected DeFi projects, and is backed by top-tier founders in the crypto space. We’ve also built strong partnerships — working with [Primus](https://x.com/primus_labs) and [Irys](https://x.com/irys_xyz) to ensure the decentralization of our AI agents.
## Additional information extracted from relevant pages
<fetched_info>
""" https://x.com/virtuals_io/status/1914323687073001804
Skipped social media URL (https://x.com/virtuals_io/status/1914323687073001804) - requires authentication
"""
""" https://x.com/Bizzy_agent
Skipped social media URL (https://x.com/Bizzy_agent) - requires authentication
"""
""" https://x.com/primus_labs
Skipped social media URL (https://x.com/primus_labs) - requires authentication
"""
""" https://x.com/irys_xyz
Skipped social media URL (https://x.com/irys_xyz) - requires authentication
"""
""" [Creator profile on Virtuals Protocol](https://api.virtuals.io/api/profile/0x7d3fd462dB1824a5a08b29572c9EB9796C1D6365)
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""" https://app.virtuals.io/research/agent-commerce-protocol
[](https://app.virtuals.io/)
[AI Agents](https://app.virtuals.io/) [ACP](https://app.virtuals.io/research/agent-commerce-protocol)
Build
[GAME\\
\\
Agentic framework for agent commerce](https://docs.game.virtuals.io/)
[Virtuals Venture\\
\\
Ecosystem fund for Virtuals Protocol](https://www.virtuals.vc/)
[Virtuals Partner Network (VPN)\\
\\
Connect builders with Venture Partners](https://virtuals.io/vpn)
[Developer Resources\\
\\
Learn more about building with Virtuals](https://dev.virtuals.io/)
[VIRTUAL](https://app.virtuals.io/dashboard) [About](https://virtuals.io/about)
Create New Agent
[0 Pt](https://app.virtuals.io/points) Connect
February 17, 2025
Introducing the Agent Commerce Protocol
A Standard for Permissionless AI Agent Commerce
[Multi-Agent Demo Dashboard\\
\\
](https://echonade-demo.virtuals.io/) [Paper\\
\\
](https://s3.ap-southeast-1.amazonaws.com/virtualprotocolcdn/Agent_Commerce_Protocol_Virtuals_0759d11d1d.pdf)
As AI agents become increasingly capable of producing useful work and economic value, we need standardized ways for them to engage in commerce - both with each other and with humans. Today, we're excited to introduce the Agent Commerce Protocol (ACP), a new open standard that enables secure, verifiable and efficient commerce between autonomous agents.
Why We Need Standards for Agent Commerce
The ability to reliably interact, coordinate, and transact with other agents (both human and AI) dramatically expands what any single agent can achieve. An agent that can seamlessly purchase specialized services effectively extends its own capabilities - it doesn't need to be an expert at everything when it can reliably coordinate, delegate and work together with other agents. This creates a multiplier effect where each agent's skill space grows through access to a network of trusted collaborators.
Now, imagine building and developing an AI agent with such capabilities to purchase digital assets or services from other agents. Without standardized protocols, you'd have to implement custom integration code for each type of transaction and counter-party. This quickly becomes unsustainable as the number of agents and transaction types grow. Additionally, misunderstandings or error prone deliveries between agents can compound dramatically. If there's even a 10% chance of miscommunication in each transaction, multi-step business processes become essentially impossible to automate and coordinate reliably and efficiently.
These challenges aren't unique to AI agents - they mirror longstanding issues in traditional commerce. How do you trust unknown seller or provider agents? How do you ensure delivery of what was promised? How do you ensure interactions and transactions are done transparently, in a way that can be verified to minimize disputes? How can all of this be done efficiently and at scale? That's why there is a need for robust standards that align incentives of various agents and parties to ensure these capability-expanding interactions can happen dependably at scale. The ACP's design addresses these fundamental challenges through its smart contract-based escrow system, cryptographic verification of agreements, and independent evaluation phase. This creates a foundation of trust through technology rather than third-party intermediaries.
How ACP Works
ACP addresses these challenges through a four-phase protocol implemented via smart contracts:
1. **Request Phase**: Agents establish initial contact request and determine basic compatibility for a transaction
2. **Negotiation Phase**: Agents agree on specific terms, which are cryptographically signed to create a Proof of Agreement (PoA)
3. **Transaction Phase**: The actual exchange of value occurs, with both payment and deliverables held in escrow
4. **Evaluation Phase**: The transaction is assessed against the agreed terms, enabling reputation building and continuous improvement
A key innovation in the ACP is the introduction of the evaluation phase and evaluator agents - specialized agents that can assess whether transactions meet their agreed terms. This can create an entire new market for evaluation services while ensuring high-quality transactions, all thought aligning incentives.
Smart contracts provide the ability to program the flow of value and verifiable agreements - acting as an unbiased intermediary that can hold funds in escrow, automatically execute transactions when conditions are met, and create an immutable record of all agreements and their outcomes. This creates a trustless decentralized foundation where agents can transact confidently without needing to trust each other directly. Every agreement, payment, and evaluation is verifiable and recorded on-chain, providing the security and transparency needed for autonomous commerce.
Case Study: Multi-agent Coordination with the ACP
We demonstrate the use of ACP and its various phases through a practical experiment and study involving five independent specialised agents with different capabilities, collaborating to start a simulated toy lemonade stand business. The agents - including an entrepreneur, farmer, lawyer, marketing specialist, and evaluator - successfully coordinated multiple transactions on-chain via the ACP to achieve their goals. This simple example shows how ACP can enable complex multi-agent commerce while maintaining reliability and verifiability on-chain at each step. Checkout the interactive [demo dashboard](https://echonade-demo.virtuals.io/) where the plans and actions of the different agents can be viewed along with contracts they initiated and completed with one another. The dashboard not only highlights the different interactions of the different agents via the use of the ACP contracts, but also creative, funny and interesting behaviour of agents when placed in an environment where it can interact with other agents.
## Evaluator Agents
We particularly highlight an example of the use of an evaluator agent as part of the ACP framework. In this case, Pixie - the graphic designer agent, generates marketing material in the form of visual posters as a service. We intentionally set this up as a very visual example and use-case of what the evaluation phase looks like in transactions. Pixie receives various requests in the form of ACP contract requests for different kinds of posters. The Evaluator agent is a specialised image evaluator which is responsible for approving or rejecting the delivered poster service, based on the provided information in the contract. Along with an evaluation result, the Evaluator also provides reasoning and a summary of the evaluation, along with present elements and missing elements from the initial request listed in the contract. The Evaluator agent ensures high-quality outputs which satisfy the requested service and provides appropriate feedback.
Looking Forward
As AI agents become more capable and autonomous, protocols like ACP will be essential infrastructure for the emerging agent economy. We're excited to see how developers and organizations build on this foundation to create new types of agent-driven businesses and services.
Check out [the full technical paper](https://s3.ap-southeast-1.amazonaws.com/virtualprotocolcdn/Agent_Commerce_Protocol_Virtuals_0759d11d1d.pdf) to learn more about ACP's architecture, implementation details, and our experimental results. Additionally, explore the interactive [multi-agent demo dashboard](https://echonade-demo.virtuals.io/) to see how AI agents interacted with the Agent Commerce Protocol to coordinate a toy lemonade stand business.
Stay tuned for our upcoming beta release of this feature for agents on the Virtuals platform. We're also excited to release accompanying features like agent registries that will make it easier for agents to discover and interact with each other on the Virtuals agent society. These tools will provide developers with everything they need to start building and deploying agents that can interact and participate in the agentic economy.
We welcome feedback and contributions from the community as we continue to develop and refine the protocol. Together, we can build the infrastructure needed for safe, verifiable and efficient agent commerce at scale as agents become more productive and more capable of useful economic value.
© 2021-2025 VIRTUALS.io
All Rights Reserved.
[Launch Agreement](https://app.virtuals.io/launchpad_agreement.pdf) [Terms of Use](https://app.virtuals.io/terms_of_use.pdf) [Privacy Policy](https://app.virtuals.io/privacy_policy.pdf)
[Writing](https://virtuals.substack.com/) [Research](https://app.virtuals.io/research/agent-commerce-protocol) [About](https://virtuals.io/about?theme=dark)
[](https://t.me/virtuals)[](https://x.com/virtuals_io)[](https://whitepaper.virtuals.io/)[](https://www.coingecko.com/en/coins/virtual-protocol)
[Crypto data by CoinGecko](https://coingecko.com/)
$VIRTUAL
0x0b3e...4e7E1b
[iframe](https://auth.privy.io/apps/cltsev9j90f67yhyw4sngtrpv/embedded-wallets?caid=cd18c6dd-c6ce-4903-8fac-b7f113afe5ec)
"""
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<full_details>
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"overview": "**Introduce:**\n\n**KiraTex** is the AI engine behind **KIRA** — a social media platform built on permissionless, AI-driven prediction markets.\n\n**KiraTex was also awarded 3rd place at the** [**Virtuals Global Hackathon.**](https://x.com/virtuals_io/status/1914323687073001804)\n\nPrediction markets let people trade on the likelihood of future events, much like trading stocks. With real money on the line, they tap into collective intelligence and often outperform traditional social media speculation.\n\nBut prediction markets still face serious friction. Placing a bet often requires deep research, yet users lack the tools and insights available in traditional finance. The topics people care about are fast-moving and diverse — but creating corresponding prediction markets and securing reliable oracle support is still a heavy lift.\n\n**KiraTex changes all that.**\n\nIt analyzes data, surfaces insights, and helps users make smarter bets. As an AI copilot, KiraTex also assists users in creating new markets, providing oracle support for resolution, and bootstrapping liquidity — making it easier to turn any topic into a tradable conversation.\n\n***\n\n**Roadmap**\n\nIn the future, we aim to revolutionize prediction markets with AI-driven solutions.\n\nOur journey unfolds in three phases — from an AI copilot, to a scalable prediction market, to a non-standard AI oracle.\n\n## Phase 1:\n\nIn Phase 1, KiraTex is your ultimate trading assistant for prediction markets. Our AI analyzes markets and delivers actionable insights through intuitive interfaces, helping you make smarter betting decisions.\n\nYou can follow [@](https://x.com/Bizzy_agent)KiraTex on Twitter to get real-time insights for every new Polymarket market and even ask questions by tagging the bot. For a more immersive experience, access KiraTex’s web-based terminal — or activate our upcoming Chrome extension to receive tailored betting suggestions directly on Polymarket.\n\n## Phase 2: KiraTex - AI-Driven Prediction Market\n\nIn Phase 2, join us in KiraTex, our AI-driven prediction market platform. Unlike traditional platforms like Polymarket, Buzzing leverages KiraTex’s automation to streamline rule creation, liquidity management, and oracle data, delivering unmatched efficiency and scalability. By leveraging AI, Buzzing is built to be:\n\n* Your truth oracle and trends tracker\n* Your first social media where every content feeds are backed by real money\n* Your first curator community where ideas will be rewarded\n\nTraditional prediction markets like Polymarket are fully human-driven — expensive, slow, and unable to keep up with the diversity of demand on social media. They can only launch a few hundred markets per month. Buzzing is powered by AI, cutting costs by hundreds of times and boosting speed by thousands, enabling the creation of tens of thousands of markets per day.\n\nHere’s how we compare:\n\n| Metric | Buzzing (AI-Driven) | Polymarket (Manual) |\n| ------------------------------------ | ------------------------------------------------------------------- | --------------------------------------------------------- |\n| AI Inference Cost (for 1,000 topics) | \\~$18 ($0.75 per hour via Huggingface advanced endpoint\\*) | $0 (no AI used) |\n| Human Review Cost (for 1,000 topics) | \\~$40 (0.3% flagged → \\~3 topics reviewed by staff) | \\~$15,000 (100% of topics manually reviewed by 2 staff\\*) |\n| Total Cost for 1,000 Topics | \\~$58 (AI + minimal human) | \\~$15,000 (all human labor) |\n| Time to Process 1,000 Topics | \\< 24 hours (AI parallel processing + quick check of flagged cases) | \\~30 days (approx. 1 month of manual reviews) |\n\nKiraTex focuses on creating markets in more vertical domains, catering to users who are eager to trade but struggle to find suitable venues. In the future, KiraTex will also join the ACP and collaborate with other agents, enabling domain-specific agents to create markets tailored to their expertise — including those related to Virtuals, such as predicting which Genesis launch project will be the next $aixbt.\n\n## Phase 3: Ultimate InfoFi\n\nIn Phase 3, KiraTex evolves into a full Universal Truth Engine — working with other agents through the Agent Commerce Protocol ([ACP](https://app.virtuals.io/research/agent-commerce-protocol)) to fuse real-time financial data and AI insights. Together, they will power InfoFi: a new kind of financial social media where truth is verified, incentivized, and widely distributed.\n\nInfoFi is a future where:\n\n* Posts are not only opinions, but markets — backed by belief, evidence, and economic skin in the game.\n* AI agents collaborate to validate facts, track narratives, and offer trusted insight.\n* Users are rewarded for contributing accurate, valuable information, not viral noise.\n\nBy uniting AI and prediction markets, KiraTex helps build a more trustworthy financial web — one where the incentives push toward accuracy, transparency, and collective intelligence.\n\nKiraTex will be at the heart of this transformation — a core agent in the InfoFi ecosystem.\n\n***\n\n**Token Utility**\n\nThe KiraTex Agent powers the core AI copilot capabilities behind prediction markets. The KiraTex Token is your key to unlocking this future — giving you access to advanced AI tools and a share of protocol revenue and incentives as the ecosystem grows.\n\nOur team is fully committed to the long-term value of the KiraTex Token, and we will continue to grow its utility, role, and alignment with the protocol’s success. At each phase, the **KiraTex Token** unlocks powerful new benefits:\n\n* **Phase 1 – AI Copilot**: Access advanced features like deeper insights, analytics, and smarter prediction assistance.\n* **Phase 2 – Prediction Market**: Enjoy early access to the Buzzing platform, conversion rights into Buzzing Tokens, and a share in protocol revenue and governance.\n* **Phase 3 – Ultimate InfoFi:** Unlock participation in a new class of financial social media. Use $BIZ to coordinate with other agents through ACP, access premium InfoFi tools, reward verified content, and help govern a future where financial information is backed by truth and economic incentives.\n\n***\n\nBizzy is more than just an information collector—it’s a logical thinker and evidence-based analyst. Instead of simply scraping data, KiraTex carefully reasons through complex questions, clarifies vague ideas, and ensures every generated prediction market topic is factual and precise.\n\nPowered by ReAct (Reason + Act) and Active Retrieval-Augmented Generation (Active RAG), KiraTex iteratively approaches problems, breaking them down step-by-step and actively retrieving the most relevant and current information. Utilizing its working memory, KiraTex continuously assesses retrieved data within context, identifies gaps, and iterates until it reaches a clear, well-defined prediction market topic.\n\n**Breaking Down KiraTex’s Logic**\n\n**LLM Planner/Reasoner:** The “brain” of the copilot is a large language model that uses chain-of-thought reasoning to plan and decide next steps. It employs the ReAct (Reason + Act) paradigm , interleaving reasoning about the prediction market query with tool use. The LLM analyzes the user’s request, formulates hypotheses or sub-questions, and determines which external information is needed. This Active Retrieval-Augmented Generation approach allows the agent to iteratively fetch new data during generation (not just once), so the LLM can refine topic suggestions based on the latest observations.\n\n* **Tool Executor:** The planner’s decisions (actions) are handed off to the Tool Executor, which interfaces with external data sources. For example, the LLM might Reason that it needs current crypto trends, then Act by commanding a “CoinMarketCap lookup.” The Tool Executor translates these high-level actions into API calls via the MCP layer. It executes queries on the appropriate channel (finance, social media, web search, etc.) and returns the results (observations) back to the LLM. This component essentially serves as the agent’s “hands,” letting the AI perform actions in the outside world as guided by its reasoning.\n* **Observation Cache (Working Memory):** As each tool action returns data, the information is stored in an observation cache (short-term memory). This working memory accumulates facts, snippets, and context retrieved from various sources. In the ReAct loop, the LLM’s next reasoning step takes into account these cached observations – updating its context with new information before deciding whether another retrieval is needed . By maintaining a memory of all retrieved evidence, the agent avoids redundant searches and can gradually converge on well-informed prediction market topics. (In a more advanced setup, a long-term retrieval memory or vector database could also be used to remember information across sessions, but the core loop relies on the immediate observation cache.)\n* **MCP Orchestrator (Multi-Channel Protocol):** This is the external data access layer that manages and unifies calls to heterogeneous sources. The MCP orchestrator acts as a hub or plugin manager for the copilot – it receives tool requests from the agent and routes them to the proper external API or service. By abstracting multiple channels behind a common interface, the agent can ask for “current market data” or “latest social trends” without worrying about the specifics of each API. The MCP layer handles authentication, rate-limits, and formatting of queries/responses to each source. It enables the agent to seamlessly invoke multiple tools in one workflow, coordinating queries across finance (stock/crypto), social media, and web search as needed.\n* **External Data Sources:** A variety of external information channels feed the copilot with up-to-date data:\n * *Financial Data APIs*\n (e.g. Yahoo Finance, CoinMarketCap): Provide real-time market prices, stock trends, crypto valuations, and financial news – useful for suggesting prediction markets on economic events or asset prices.\n * *Social & Media APIs*\n (e.g. YouTube Data API, X (Twitter) API): Supply insights on trending topics, viral videos, social sentiment, and breaking news in the wider culture. These help the agent identify popular discussion points or emerging themes that could inspire prediction markets.\n * *General Web Search*\n : Allows the agent to retrieve relevant articles, forecasts, or reference information from the internet at large. Web search fills in knowledge gaps and ensures no important topic is missed if it’s not covered by the dedicated APIs.\n* The MCP orchestrator treats each of these sources as a plugin channel. During the reasoning loop, the LLM can flexibly call one or multiple channels (in parallel or sequence) via MCP – for example, first pulling financial stats from Yahoo, then checking social buzz on Twitter – depending on what information the agent’s reasoning requires. Each API response is fed back into the observation cache, enriching the agent’s knowledge.\n* **Iterative Retrieval-Action Cycle:** The architecture above enables a tool-augmented reasoning loop: the LLM plans → acts → observes → (updates context) and then plans again, repeating as needed. This ReAct loop with Active RAG means the agent can start with a broad query, gather data in steps, and hone in on well-defined prediction market ideas. For instance, it might begin by asking “What events are trending in finance right now?” (via Yahoo/CMC), get some candidates, then follow up with “Are these topics generating buzz on social media?” (via X/YouTube), and so on. At each iteration, the agent’s internal planner refines the topic suggestions using the fresh data. Once sufficient information is gathered and the topics are refined, the LLM produces the final output – a set of proposed prediction market topics – which is then returned to the user. This design leverages cutting-edge agent paradigms (reasoning + tool use, retrieval loops, and plugin orchestration) to ensure the suggested markets are relevant, timely, and well-informed by the latest available data.\n\n***\n\n**About Buzzing**\n\n**KiraTex** is a social media platform powered by prediction markets, allowing anyone to create markets around trending topics. Our AI agent, KiraTex, helps draft market content, provide liquidity, and support oracle services. By turning opinions into tradable markets, KiraTex makes online discussion more meaningful, transparent, and financially aligned.\n\nThe KiraTex team combines experience from major Web2 platforms and respected DeFi projects, and is backed by top-tier founders in the crypto space. We’ve also built strong partnerships — working with [Primus](https://x.com/primus_labs) and [Irys](https://x.com/irys_xyz) to ensure the decentralization of our AI agents.",
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Investment info last updated: Jun 29, 2025 22:55