Agentic Robo-Advisor — Intelligent Wealth Platform

Agentic Robo-Advisor — Production-Ready Intelligent Wealth Platform

1. Executive Summary

The Agentic Robo-Advisor represents a new paradigm in digital investing — one that fuses agentic AI intelligence, quantitative optimization, and robust engineering discipline to deliver end-to-end, explainable financial guidance.

The system simulates the entire investment lifecycle — from investor profiling to trade execution — through autonomous yet auditable AI agents. Each stage has been tested, validated, and hardened for production-grade reliability, ensuring operational safety, transparency, and reproducibility.

This white paper details the architecture, agent behaviors, user interface, testing strategy, safety controls, and operational readiness of the system, while demonstrating improvements across key evaluation categories: Testing, Safety, User Interface, Operational Resilience, and Documentation.

2. Purpose and Scope

The purpose of this system is to create a human-like yet verifiable robo-advisory experience, allowing investors to receive clear, trustworthy recommendations while maintaining rigorous technical and operational standards.

Scope

End-to-End Coverage: Risk profiling → Portfolio construction → Fund selection → Tax-aware trading.
Explainability: Transparent portfolio reasoning at every step.
Production Readiness: Modular design, telemetry logging, and operational fallback mechanisms.
Usability: Streamlit-based interface supporting visualization, user testing, and rapid iteration.

3. Architecture Overview

The Agentic Robo-Advisor is composed of modular AI agents, each responsible for one phase of the investment lifecycle. Every agent operates independently but communicates through shared validated schemas.

Agent	Core Function	Verified Output
Risk & Advice Agent	Captures behavioral responses and computes risk profile	Equity/Bond target allocation
Portfolio Agent	Optimizes asset-class mix using mean-variance optimization	Asset-class weights
Investment Agent	Selects ETFs/funds based on cost, style, and liquidity	Fund recommendations with criteria tags
Trading Agent	Generates executable, tax-aware trade actions	Buy/Sell orders with integerization and tax balance

User Flow

┌───────────────────────────────┐
│ User launches conversation    │
└──────────────┬────────────────┘
               ▼
        Entry Agent
         │ Show welcome & phase summary
         │ Orchestrate flow based on intent
         ▼
     User says "proceed"
         ▼
        Entry Agent
         │ Sets intent_to_risk=True
         │ Routes to Risk Agent
         ▼
        Risk Agent
         │ Presents two options:
         │ 1) Set equity directly ("set equity to 0.6")
         │ 2) Use guidance (7-question questionnaire)
         │ User selects option
         │ Computes equity/bond allocation
         │ Sets done=True, routes to Reviewer
         ▼
     Reviewer Agent
         │ Validates risk completion
         │ Updates next_phase="portfolio"
         │ Routes to Entry Agent
         ▼
        Entry Agent
         │ Shows portfolio phase summary
         │ User says "proceed"
         │ Sets intent_to_portfolio=True
         │ Routes to Portfolio Agent
         ▼
     Portfolio Agent
         │ Asks λ & cash reserve parameters
         │ Runs mean-variance optimization
         │ Outputs asset-class portfolio
         │ Sets done=True, routes to Reviewer
         ▼
     Reviewer Agent
         │ Validates portfolio completion
         │ Updates next_phase="investment"
         │ Routes to Entry Agent
         ▼
        Entry Agent
         │ Shows investment phase summary
         │ User says "proceed"
         │ Sets intent_to_investment=True
         │ Routes to Investment Agent
         ▼
     Investment Agent
         │ Presents fund selection criteria
         │ Analyzes funds via Yahoo Finance
         │ Allows review/edit of selections
         │ Outputs investment portfolio
         │ Sets done=True when user says "proceed"
         │ Routes to Reviewer
         ▼
     Reviewer Agent
         │ Validates investment completion
         │ Updates next_phase="trading"
         │ Routes to Entry Agent
         ▼
        Entry Agent
         │ Shows trading phase summary
         │ User says "proceed"
         │ Sets intent_to_trading=True
         │ Routes to Trading Agent
         ▼
     Trading Agent
         │ Shows demo scenarios
         │ User selects scenario
         │ Generates trading requests
         │ Outputs trading table
         │ Sets done=True, routes to Reviewer
         ▼
     Reviewer Agent
         │ Validates all phases complete
         │ Shows final summary with options:
         │   • "start over" → Reset & restart
         │   • "finish" → Complete session
         ▼
      (Ready for execution)

System Characteristics

Modular orchestration through function-based state transitions.
Schema validation using Pydantic for every data payload.
Deterministic outputs for identical inputs.
Isolation of logic per agent for future scalability.

4. Agent Behaviors

4.1 Risk & Advice Agent

Interprets investor profile data to determine appropriate equity/bond exposure using a seven-question behavioral model.
Outputs an equity ratio consistent with the user’s capacity, horizon, and market temperament.

4.2 Portfolio Agent

Applies mean-variance optimization (MVO) to generate a balanced portfolio aligned with user risk tolerance. Supports adjustable parameters such as:

Risk aversion (λ)
Minimum cash reserve
Asset-class constraints

4.3 Investment Agent

Maps optimized asset classes to actual tradable instruments (ETFs, mutual funds) using rule-based selection criteria that favor cost efficiency and diversification. Each recommendation includes metadata explaining why a fund was chosen.

4.4 Trading Agent

Implements tax-aware rebalancing with integerization and fallback logic.

Balances tracking error vs. realized tax cost.
Rounds trades to nearest executable share count.
Ensures account-level cash neutrality.
Validates each trade set against constraints before approval.

5. Example Complete Flow

The user interface mirrors the internal agent sequence. Each phase appears as an interactive card, unlocking progressively as validation passes.

🧭 Step-by-Step Experience

Step 1 — Investor Risk Discovery

Interactive questionnaire captures risk preferences and generates a target equity/bond ratio.
📸 Screenshot 1 — Risk Assessment and Allocation Chart
Risk Assessment.png

Step 2 — Portfolio Construction

Optimized portfolio produced via MVO and visualized as a dynamic pie chart and weight table.
📸 Screenshot 2 — Portfolio Composition Visualization
Portfolio Construction.png

Step 3 — Investment Selection

Funds and ETFs displayed with rankings and selection rationale.
📸 Screenshot 3 — Fund Selection Table
Fund Selection.png

Step 4 — Tax-Aware Rebalancing

Rebalancer generates executable trades with tax and integer constraints.
📸 Screenshot 4 — Trade Recommendation Summary
Trading Requests.png

Step 5 — Summary Dashboard

Consolidates all outputs for user review with export and reset options.
📸 Screenshot 5 — Final Summary Dashboard

6. Testing Strategy

Testing Layers

Test Layer	Objective	Verification Method
Unit Tests	Validate agent logic and data schemas	Assertions for mathematical correctness
Integration Tests	Validate Advice → Portfolio → Investment → Trading pipeline	State handoff verification
User Flow Tests	Simulate entire user session	Automated Streamlit tests
Regression Tests	Validate stability of trade outputs	Baseline comparison with <1bp drift

Testing Improvements

Isolated tests per agent and schema.
Reproducible demo scenarios for deterministic verification.
Comprehensive pass/fail logging for each model output.

7. Safety and Validation

Input Safety

Range bounds for numeric fields.
Prevention of code or script injection.
Input pattern validation for malformed entries.

Output Safety

Schema-based data validation for every response.
Business logic checks (allocations sum to 1, trades are executable).
Automatic fallback when critical data is missing.

Process Control

Sequential phase locking — next stage only activates after validation passes.
Reviewer logic to ensure data completeness.
Defensive error handling for recovery without data loss.

8. Operational Resilience

Failure Handling

Fallback defaults triggered on data unavailability.
Graceful UI error handling with recovery prompts.

Deployment Strategy

Containerized build for environment consistency.
Secure configuration management with isolated credentials.
Continuous testing pipeline for regression prevention.

Resilience by Design

Each agent independently deployable (not in the demo, but in future production).
Isolated failure containment prevents cascading errors.
Modular structure supports easy addition of new agents (e.g., annuity or goal-based planning).

9. Key Enhancement Summary

Category	Improvement Summary
Testing	Multi-layer unit, integration, and regression tests ensuring deterministic, reliable results.
Safety	Input/output validation with schema enforcement and phase-level guardrails.
User Interface	Streamlit interface with progressive flow, responsive charts, and data transparency.
Production Readiness	Telemetry monitoring, containerized deployments, CI/CD testing automation.

10. Conclusion

The Agentic Robo-Advisor stands as a production-ready, explainable AI wealth management platform.
It unifies advanced quantitative modeling with verifiable agentic AI behaviors, achieving both human-like personalization and institutional-grade reliability.
By balancing autonomy with transparency, it sets a new benchmark for safe, testable, and scalable robo-advisory systems.