Reflection Pattern for Investment Advisory: A Fictional Take

This article explores how the reflection pattern, an agentic AI design pattern in which a model iteratively generates, critiques and refines its own work, can be applied to investment advisory. We’ll introduce fictional superstar advisors inspired by pop culture, explain how the reflection loop fits within an orchestrator–worker architecture, and provide a full Python implementation. Along the way, we highlight benefits, drawbacks and speculative future applications.

Summary at a Glance

Persona	Style	Typical Allocation (Equities / Alternatives / Fixed‑Income / Cash)
Bobby Axelrod	Aggressive hedge‑fund maverick willing to ride volatility	70% / 20% / 5% / 5%
Gordon Gekko	Opportunistic raider who loves leverage, options and arbitrage	75% / 15% / 5% / 5%
Richie Rich	Cautious wealth‑preserver who favours blue chips and bonds	30–60% / 10–20% / 15–45% / 5–15%

The Reflection Pattern Explained

The reflection pattern originates in cognitive science and has recently been adopted for large language models (LLMs). The idea is simple yet powerful: rather than trusting a single draft, the model runs a generate–critique–refine loop. According to Analytics Vidhya, the pattern “is a method where the model generates, critiques and refines its outputs through an iterative self‑assessment process” (analyticsvidhya.com). This loop mimics human editing, catching mistakes, clarifying ambiguities and improving quality across multiple passes (analyticsvidhya.com). A second LLM (or the same model with a different prompt) acts as a critic, evaluating the initial output against well‑defined criteria and feeding feedback back to the generator (philschmid.de).

Key steps in the reflection pattern include:

1. Generation: produce a first draft based on the user’s prompt. This can be done in a zero‑shot manner, generating a candidate without examples (analyticsvidhya.com).

2. Self‑reflection: the model reviews its own work, identifies errors, missing details or stylistic issues, and generates feedback (analyticsvidhya.com).

3. Iteration & refinement: the feedback is incorporated into a new draft, and the cycle repeats until the output meets quality thresholds or a maximum number of iterations is reached (tpiros.dev).  Stopping conditions prevent infinite loops and include quality targets or iteration limits (analyticsvidhya.com).

Practical applications of the reflection pattern include code generation, content drafting, data analysis and decision‑making. Because the model critiques itself, it can catch inconsistencies and gradually improve the output. However, iterative generation consumes time and compute, and repeated prompts can induce drift or hallucination. A disciplined stopping criterion and robust evaluation criteria are essential.

The Orchestrator–Worker Pattern

The reflection loop fits naturally into an orchestrator–worker architecture, another agentic design pattern. In this setup, a central orchestrator decomposes a complex task into subtasks and dispatches them to specialized worker agents. The orchestrator maintains global oversight, monitors worker performance and synthesizes partial results into a final answer (dzone.com). Workers operate as domain‑specific experts, focusing solely on their assigned tasks (dzone.com).

This separation of concerns brings several benefits:

• Modularity & extensibility: new worker agents can be added without rewriting the orchestrator, making the system scalable.

• Parallelism: tasks can run concurrently, reducing latency and improving throughput.

• Resilience: if one worker fails, the orchestrator can still produce a meaningful result from the remaining outputs (pytrick.medium.com).

• Central control: centralized monitoring ensures that quality standards are enforced across workers and that fallback strategies are applied when errors occur (dzone.com).

The orchestrator–worker pattern is particularly useful for workflows that combine planning, data retrieval, analysis and synthesis. For example, in a technical stock analysis system, an orchestrator fetches data once, sends it to workers computing RSI, MACD and other indicators, then aggregates the signals into a report (pytrick.medium.com). Our fictional investment advisor will reuse this structure: the orchestrator iterates through different personas (workers), each generating an investment plan; an evaluator agent critiques the plan; and the orchestrator decides whether to accept or request another draft.

Fictional Investment Superstars

Real investment icons like Cathie Wood or Warren Buffett offer recognisable strategies, but they also introduce brand‑specific bias. To avoid conflating advice with real identities, and to show that agentic workflows can model a variety of styles, we propose three fictional personas:

• Bobby Axelrod: loosely modelled on a hedge‑fund manager character. Bobby favours high‑growth sectors and alternative assets. In our template he allocates ~70 % to equities and 20 % to alternatives, leaving minimal fixed income and cash. This results in an aggressive risk grade.

• Gordon Gekko: inspired by the 1980s corporate raider, Gordon prioritises concentrated bets, options and arbitrage. He rarely recommends conservative portfolios, pushing equities to 75 % and leaving a token 5 % to fixed income. Another aggressive profile.

• Richie Rich: a wealthy heir who values security. Richie’s allocation varies by target grade: 60–30 % equities, 10–20 % alternatives, 15–45 % fixed income and the rest cash. He can satisfy conservative or moderate risk tolerances.

These personas serve as “workers” in our orchestrator. They each provide a distinct draft plan based on the investor’s profile and desired risk grade. A simple evaluator reads the plan, calculates the equity allocation and assigns a risk grade (conservative, moderate or aggressive). If the assigned grade doesn’t match the target, the orchestrator calls another persona to propose a new plan. After a preset number of iterations, the best plan is returned.

LLM Integration with LangChain

While deterministic templates offer repeatable outputs, they lack the spontaneity and nuance of natural language generation. To make the advisor more generative and human‑like, the latest version integrates an optional language model using LangChain’s ChatOpenAI interface. At the top of the code we attempt to import ChatOpenAI and ChatPromptTemplate. A helper function get_llm() tries to instantiate a gpt‑4o‑mini model with a moderate temperature (0.7). When this succeeds, each persona constructs a structured prompt containing the investor’s profile and target grade; the model then generates a bespoke Markdown plan with asset allocations, rationale and rebalancing advice.

The beauty of this design is its graceful fallback: if LangChain isn’t installed or the model fails to initialize (for instance, due to missing API keys), the code reverts to deterministic templates. The reflection loop and evaluator remain unchanged, so the orchestrator–worker architecture stays intact. Only the plan generation mechanism becomes dynamic when an LLM is available. This modularity demonstrates how real‑world systems can incrementally adopt generative AI without sacrificing robustness.

Code Implementation

To put these ideas into practice, we provide a fully working Python module that mirrors the reflection loop described in the Skills Network lab while swapping the real investors for our fictional superstars. The module defines a shared state dictionary, prompts for each persona, an evaluator inspired by Richie Rich’s prudence, and a simple Python loop that iteratively generates and critiques investment plans until the risk grade matches the target.

Key components include:

• determine_target_grade – uses an LLM to pick a suitable risk grade for the investor based on their profile.

• investment_plan_generator – produces an initial Bobby Axelrod–style plan and refines it using Gordon Gekko’s opportunistic rules when feedback exists.

• evaluate_plan – calls a Richie Rich–style evaluator to classify the risk level and provide feedback on diversification, volatility and capital preservation.

• route_investment – decides whether to accept the current plan or request another iteration, enforcing a maximum number of loops.

• run_reflection_workflow – orchestrates the entire generate–evaluate–route cycle in plain Python without relying on LangGraph, returning the final plan along with feedback and iteration count.

Here is a high‑level sketch of the module’s structure (see langgraph-cookbook/06 - Investment Agent at main · JordiCorbilla/langgraph-cookbook for complete code):

from typing import TypedDict, Dict, Literal, Annotated
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

grades = Literal["ultra-conservative", "conservative", "moderate", "aggressive", "high risk"]

class State(TypedDict):
    investment_plan: str
    investor_profile: str
    target_grade: grades
    feedback: str
    grade: grades
    n: int

# Initialise the LLM (gpt-4o-mini) and build prompt templates for each persona

# … Bobby Axelrod prompt and pipeline
# … Gordon Gekko prompt and pipeline with adaptation rules
# … Richie Rich evaluator prompt and structured output

def determine_target_grade(state: State) -> Dict[str, grades]:
    # Ask the LLM to choose the target risk grade based on the profile
    pass

def investment_plan_generator(state: State) -> Dict[str, str]:
    # Generate or refine the plan using Bobby or Gordon prompts
    pass

def evaluate_plan(state: State) -> Dict[str, object]:
    # Evaluate the plan with the Richie Rich evaluator
    pass

def route_investment(state: State, iteration_limit: int = 5) -> str:
    # Decide whether to accept the plan or loop again
    pass

def run_reflection_workflow(profile: str) -> State:
    # Orchestrate the reflection loop until the target grade is met or the iteration limit is reached
    pass

The complete implementation can be downloaded as custom_investment_reflection.py (file ID :agentCitation{citationIndex='0'}). This script uses LangChain’s ChatOpenAI to generate and evaluate plans, falls back gracefully if the LLM isn’t available, and demonstrates how to adapt the reflection pattern to custom personas.

Sample Execution

As an example, suppose you run run_reflection_workflow() from the custom_investment_reflection.py module with a 35‑year‑old investor earning $100 k, holding $100 k in assets, aiming to retire by 55 with a generous travel budget, and tolerating high risk. The workflow determines an aggressive target grade and iteratively refines the plan. One possible output (actual results vary slightly with the generative model) is summarised below:

🎯 Final Investment Plan Summary
========================================

📌 Investor Profile:
Age: 35
Salary: $100,000
Assets: $100,000
Goal: Retire comfortably by age 55 with ample travel budget
Risk tolerance: high

📈 Target Risk Grade: aggressive
📊 Final Assigned Grade: aggressive
🔁 Iterations Taken: 2

📝 Evaluator Feedback:
----------------------------------
This portfolio is classified as aggressive due to its heavy allocation to equities (75%) and lower commitment to fixed income.  Ensure the investor is comfortable with higher volatility and periodic drawdowns.

📃 Final Investment Plan:
----------------------------------
Gordon Gekko advises the investor to pursue an assertive course:

### Asset Allocation
- **Equities (75%)**: Concentrated bets in blue chips and cyclical industries, supplemented by option strategies.
- **Alternatives (15%)**: Distressed debt, merger arbitrage and real‑estate investment trusts.
- **Fixed Income (5%)**: Opportunistic short‑dated corporate notes.
- **Cash (5%)**: Dry powder for takeover opportunities.

### Rationale
Prioritizing blue chip equities and sector ETFs with active options overlays, complemented by distressed debt and merger arbitrage funds.  Minimal fixed income ensures capital is working hard.

### Risk Management
Use stop‑loss orders and hedges; rebalance monthly to maintain leverage discipline.

The first iteration used the Bobby Axelrod persona, which produced a 70 % equity allocation (moderate grade). The evaluator recommended increasing risk exposure, and the orchestrator rotated to Gordon Gekko, whose 75 % equity allocation met the target. The system stopped after two iterations.

Pros and Cons of the Reflection Pattern in Finance

Advantages

• Improved quality through self‑critique: By iteratively evaluating its own recommendations, the advisor reduces the likelihood of omissions or contradictory allocations. It mirrors the human practice of drafting and revising (philschmid.de).

• Personalisation: Different personas allow the system to explore varied strategies. Investors can compare aggressive, moderate and conservative approaches without requiring multiple human advisors.

• Generative creativity: When an LLM is available, each persona can craft bespoke investment narratives instead of fixed templates. This results in plans that read more naturally and incorporate subtle nuances while still adhering to the requested risk grade.

• Reduced human bias: Using fictional characters avoids overreliance on real gurus. The system still captures different risk appetites but does not misappropriate any celebrity’s actual statements.

• Extensibility: New personas or evaluators can be added without changing the core loop, demonstrating the modularity of the orchestrator–worker pattern (dzone.com).

• Autonomy and scalability: The loop can be run asynchronously, and evaluation metrics can be refined over time to align with regulatory guidelines or individual preferences.

Limitations

• Heuristic evaluator: Our evaluation function parses simple percentages and assigns grades. Real‑world risk analysis is far more complex, involving volatility, correlation and drawdown metrics. More sophisticated evaluators (e.g., risk‑parity or Monte Carlo simulators) could improve accuracy but also increase complexity.

• Computational cost: Iterative generation and evaluation consume more compute cycles and may require multiple LLM calls. For high‑frequency use cases, latency and API cost could become prohibitive.

• Unpredictability: Generative models may occasionally omit required allocation details or introduce inconsistent formatting. Although the evaluator catches gross mismatches, manual review is advisable when using LLM‑generated plans.

• Potential oscillations: Without careful stopping rules, the system might ping‑pong between personas or produce contradictory adjustments. Clear termination criteria and penalizing repeated mistakes are essential (analyticsvidhya.com).

• Lack of external context: Our fictional personas rely on templates rather than real market data. Integrating live data feeds and dynamic modelling would make the advice more actionable but also raises regulatory and liability considerations.

Conclusion

The reflection pattern offers a simple yet powerful mechanism for improving AI outputs by letting the model serve as its own critic. When combined with an orchestrator–worker architecture, it provides a modular and extensible framework for tasks like investment advice, where balancing risk and return requires careful iteration. By experimenting with fictional personas, we avoid real‑world bias while illustrating how different risk appetites can be encoded in code. Although our current implementation uses heuristic evaluators and templated advice, it lays the foundation for more sophisticated systems that combine generative AI, financial models and ethical constraints.