Advanced AI Agent Collaboration & Portfolio Analytics
Implements a **multi-agent collaboration framework** with publish-subscribe messaging, NLP query parsing, portfolio analytics engine, and LP risk scoring. Agents communicate in real-time through a message bus, reach consensus on complex queries, and execute dynamic workflows based on collective decisions.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Portfolio Analytics Agent
Portfolio analysis requires comprehensive evaluation of holdings composition, performance attribution, diversification metrics, concentration risk, style analysis, and liquidity profiles.
Core Logic
Performs deep portfolio analytics including holdings breakdown by asset class, sector, and geography. Calculates performance attribution across factors, measures diversification using HHI and correlation matrices, identifies concentration risks, performs style analysis (growth/value, large/small cap), and assesses liquidity profiles across time horizons.
LP Risk Scoring Agent
Limited partners need comprehensive risk assessments of their fund exposures across multiple dimensions to inform allocation decisions and risk management.
Core Logic
Calculates LP risk scores (0-100 scale with color coding) across **6 dimensions**: **Concentration risk** from position size analysis, **Sector/geography exposure** measuring thematic concentration, **Liquidity risk** assessing redemption constraints, **Market timing risk** evaluating entry point sensitivity, **Counterparty risk** analyzing GP and fund dependencies, **Operational risk** examining administrative and custody exposures.
NLP Query Agent
Users need to query complex financial data using natural language rather than structured queries, requiring sophisticated intent recognition and entity extraction.
Core Logic
Parses natural language financial questions to identify user intent (performance query, risk analysis, comparison, forecast). Extracts entities including fund names, metrics (IRR, TVPI, DPI), date ranges, and comparison operators. Routes queries to specialized agents based on intent, aggregates multi-agent responses, and formats results with confidence scores and reasoning explanations.
Workflow Orchestrator Agent
Complex analytical queries require dynamic workflow construction with conditional branching, parallel execution paths, and decision tree navigation based on intermediate results.
Core Logic
Constructs and executes dynamic workflows based on query requirements. Manages parallel agent invocations for independent tasks, handles sequential dependencies, implements decision trees for conditional processing, tracks execution status across pipeline stages, and aggregates final results from multiple workflow branches.
Message Bus Agent
Multi-agent systems require reliable inter-agent communication with message routing, priority handling, broadcasting, and delivery confirmation.
Core Logic
Implements a **publish-subscribe architecture** for agent communication. Features priority-based message queuing (high, normal, low), targeted routing by agent ID, broadcast capability for system-wide notifications, acknowledgment tracking for delivery confirmation, and metrics collection (total messages, average latency, delivery rates).
Agent Simulator Agent
Testing and demonstrating multi-agent behaviors requires simulation capabilities that model agent interactions without invoking production AI services.
Core Logic
Simulates agent behaviors for testing and demonstration scenarios. Models agent response patterns, generates realistic message flows, simulates collaboration dynamics between agents, creates demonstration data for UI visualization, and enables testing of workflow logic without production API costs.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization