Richard - Autonomous AI Research Engine
Self-evolving multi-agent system that autonomously detects trends, simulates scenarios, and generates strategic insights
Overview
Richard is a proactive AI research engine that solves a critical knowledge work problem: strategic blind spots. Traditional tools are reactive-they only answer what you ask. Richard autonomously monitors diverse data sources, detects weak signals, and generates predictive hypotheses before users even know what questions to ask. I architected a modular multi-agent system using LangGraph orchestration, vectorized memory with Pinecone, and custom DAG-based simulation for scenario modeling. The system reduced typical research workflows from 5-8 hours to under 30 minutes while generating original strategic insights with full traceability.
Challenges & Solutions
- 1Architecting autonomous agents that proactively surface insights without prompting
- 2Building recursive feedback loops enabling emergent intelligence over time
- 3Implementing DAG-based scenario simulation with probabilistic confidence modeling
- 4Designing modular system architecture for independent agent iteration
- 5Creating vectorized memory layer for persistent signal correlation across domains
- 6Orchestrating stateful multi-agent chains with real-time reasoning explainability
Results & Impact
Reduced strategic research time from 5-8 hours to <30 minutes (94% efficiency gain)
Generated 400+ unique insights across 8 weeks with <5% redundancy
Built modular architecture enabling rapid agent swapping without system breakage
Detected cross-domain pattern correlations (crypto trends + geopolitical sentiment)
Delivered explainable AI with full decision tree traceability for compliance
Demonstrated ROI through early detection of contrarian investment opportunities
Role
Founding Engineer & System Architect
Duration
Mar 2025 - Present
Tech Stack
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