Back office ops · Production

Chaos Labs builds Edge AI Oracle — a multi-agent council for prediction market resolution using LangChain and LangGraph

The problem

Prediction markets require an oracle to determine outcomes and resolve bets, but traditional oracles suffer from the limitations and bias of single-model solutions, making resolutions lack the objectivity and explainability required for high-stakes markets.

First attempt

Traditional oracles rely on a single AI model, introducing inherent bias and limiting objectivity — a multi-perspective approach is needed for trustworthy query resolution.

Workflow diagram · grounded in source
1
Query submitted to Oracle
trigger
“from "Who won the election?" to "How many goals did Messi score?" and "Who are the latest Nobel Prize winners?"”
2
Research Analyst parses query
ai_action
“the research_analyst, which reviews the query, identifying key data points and required sources”
3
Web Scraper retrieves data
integration
“the web_scraper, which retrieves data from external sources and databases and prioritizes reputable, verified information”
4
Bias Analyst filters retrieved data
validation
“the document_bias_analyst reviews the gathered data, applying filters and checking for bias, ensuring the data pool remains neutral and credible”
5
Report Writer synthesizes findings
ai_action
“the report_writer synthesizes findings into a cohesive report, presenting an initial answer based on the research and analysis conducted”
6
Summarizer distills key insights
ai_action
“the summarizer condenses the report, distilling the key insights and findings into a concise form suitable for final processing”
7
Classifier validates output
validation
“the classifier evaluates the summarized output, categorizing and validating it against preset criteria before reaching the END of the workflow”
Reported outcome

Chaos Labs released the alpha version of Edge AI Oracle, a decentralized AI Oracle Council that requires unanimous agreement with over 95% confidence from each Oracle AI Agent, delivering resolutions described as objective, accurate, and fully explainable.

Reported metrics
Oracle AI Agent confidence thresholdover 95%
Reported stack
LangChainLangGraphRAGEdge Oracle NetworkOpenAIAnthropicMeta
Source
https://blog.langchain.dev/how-chaos-labs-built-a-multi-agent-system-for-resolution-in-prediction-markets/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Chaos Labs released the alpha version of Edge AI Oracle, a decentralized AI Oracle Council that requires unanimous agreement with over 95% confidence from each Oracle AI Agent, delivering resolutions described as obje…

What tools did this team use?

LangChain, LangGraph, RAG, Edge Oracle Network, OpenAI, Anthropic, Meta.

What results were reported?

Oracle AI Agent confidence threshold: over 95% (source-reported, not independently verified).

What failed first in this deployment?

Traditional oracles rely on a single AI model, introducing inherent bias and limiting objectivity — a multi-perspective approach is needed for trustworthy query resolution.

How is this back office ops AI workflow structured?

Query submitted to Oracle → Research Analyst parses query → Web Scraper retrieves data → Bias Analyst filters retrieved data → Report Writer synthesizes findings → Summarizer distills key insights → Classifier validates output.

Related back office ops cases