A Practical Framework for Building an LLM-Based Solution Architecture Simulator

Executive Summary

This report provides a comprehensive, practical framework for a solo Python developer to build a Large Language Model (LLM)-based solution architecture simulator. The primary objective is to create a replicable system capable of generating and evaluating 8-12 diverse technical solutions for a given problem, moving beyond the limited scope of single-LLM interactions. The entire framework is designed to be implemented within a one-week timeline and a total budget of $100.

The core recommendations are as follows:

• Orchestration Framework: Utilize CrewAI for its simplicity, low learning curve, and intuitive role-based paradigm, which directly maps to the required agent personas of an Architect, Critic, and Evaluator.
• Solution Generation: Employ a hybrid approach combining Architectural Pattern Prompting and Morphological Analysis. These structured creativity techniques force LLMs to generate a broad and non-obvious set of potential solutions, overcoming their natural tendency toward common or generic answers.
• Cost Management: Implement an LLM Cascading architecture. This involves using different LLM providers and models strategically—powerful, expensive models for complex reasoning and creative tasks, and cheap, fast models for simpler tasks like data extraction and scoring. This approach, combined with aggressive token optimization, is critical to staying within the budget.

Following this blueprint will yield a working prototype that systematically evaluates technical options against a personalized developer profile. The final output is a ranked list of solutions, complete with a detailed analysis of their respective strengths, weaknesses, and alignment with project constraints, enabling more informed and robust architectural decisions.

I. Architecting the Simulation Core: Multi-Agent Orchestration Frameworks

The foundation of the solution architecture simulator is a multi-agent system. This choice is deliberate; a single LLM, no matter how powerful, is prone to monolithic thinking and may lack the structured process of debate and refinement necessary for rigorous architectural evaluation. A multi-agent approach allows for the creation of specialized AI personas that can collaborate to produce a more comprehensive and well-vetted output.

1.1 The Multi-Agent Paradigm Shift

The move from single-LLM interactions to multi-agent systems represents a significant evolution in applied AI, aiming to boost performance through synergistic collaboration.1 A multi-agent system harnesses the strengths of multiple specialized agents, each with unique skills and responsibilities.1 For the purpose of architectural simulation, this paradigm offers distinct advantages:

• Specialization and Reduced Cognitive Load: Instead of a single agent attempting to generate, critique, and evaluate solutions simultaneously, these responsibilities can be divided. One agent can focus solely on creative solution generation (the Architect), another on identifying potential flaws (the Critic), and a third on scoring against specific criteria (the Evaluator). This division of labor allows each agent's prompt and instruction set to be highly focused, leading to better performance on its specific sub-task.3
• Structured Debate and Refinement: The workflow can be structured as a pipeline where the output of one agent becomes the input for the next. The Architect's raw ideas are passed to the Critic, whose analysis adds a layer of scrutiny. This process mimics a human architecture review board, improving the robustness of the final evaluation.5
• Modularity and Scalability: The system is inherently modular. Each agent can be developed, tested, and optimized independently. If a new evaluation perspective is needed (e.g., a "Security Analyst" agent), it can be added to the workflow without redesigning the entire system.2

The core components of such a system include the agents themselves (LLMs equipped with instructions and tools), a shared context or memory to pass information between them, and an orchestration framework that defines the logic of their interaction.7

1.2 Comparative Analysis of Top Python Frameworks

The Python ecosystem offers several mature frameworks for building multi-agent systems. The choice of framework is the most critical architectural decision, as it dictates development speed, operational complexity, and cost. The selection process involves a fundamental trade-off between granular control and development simplicity. The leading candidates for this project are CrewAI, LangGraph, and AutoGen.

• CrewAI: The "Role-Based Team" Model. CrewAI is a high-level framework designed around the intuitive concept of assembling a "crew" of agents, each with a specific role, backstory, and assigned task.10 It excels at orchestrating role-based collaboration where agents work together to achieve a common goal.12 Its default process is sequential, making it a natural fit for pipeline-based workflows like solution generation followed by critique.11 Its main strength lies in its simplicity and low learning curve, abstracting away much of the underlying complexity of agent interaction and state management.12
• LangGraph: The "State Machine" Model. As an extension of the popular LangChain library, LangGraph allows developers to define agent workflows as a graph, with agents as nodes and the logic of their interaction as edges.3 This paradigm provides explicit and granular control over the workflow, making it exceptionally powerful for implementing complex logic, including cycles (loops), branches, and persistent state.10 This power, however, comes at the cost of increased complexity and a steeper learning curve, as it requires understanding graph theory concepts and working with more boilerplate code.15
• AutoGen: The "Conversational Committee" Model. Developed by Microsoft, AutoGen is a framework built around multi-agent conversations.10 It enables flexible, event-driven dialogues where agents can interact with each other and with human users in a group chat-like setting.11 While highly flexible for brainstorming and collaborative problem-solving, its less-structured, conversational nature can be more difficult to control for a deterministic evaluation task. The "chatty" paradigm can also lead to higher token consumption, as multiple back-and-forth messages may be required to reach a conclusion.9

The following table provides a comparative summary of these frameworks tailored to the needs of a solo developer.

Table 1: Multi-Agent Framework Comparison Matrix

Framework	Core Paradigm	State Management	Learning Curve	Ideal Use Case for Solo Developer
CrewAI	Role-Based Collaboration	Abstracted; handled via task inputs/outputs	Low	Rapid prototyping of structured, sequential agent workflows (e.g., generate, then critique).11
LangGraph	Graph-Based State Machine	Explicit and persistent via a graph state object	High	Complex, cyclical workflows with branching logic and the need for fine-grained control.10
AutoGen	Conversational Agents	Managed through agent memory and conversation history	Medium	Dynamic, research-style tasks requiring collaborative brainstorming and human-in-the-loop feedback.12

1.3 Recommendation and Justification: CrewAI for Simplicity and Speed

For this project's constraints—a solo developer with a one-week timeline and a focus on a replicable, structured evaluation process—CrewAI is the optimal choice.

• Implementation Complexity: Low. CrewAI's high-level abstractions and beginner-friendly documentation significantly reduce development time.11 The mental model of defining agents with roles and giving them sequential tasks maps directly to the project's goal without requiring the developer to manage the intricacies of state transitions or conversational flow.
• Cost Implications: Low to Medium. A key factor in meeting the budget is minimizing unnecessary LLM calls. The sequential process model favored by CrewAI is inherently more token-efficient for this evaluation use case than a free-form conversational framework like AutoGen. A linear handoff from one agent to the next results in a predictable and minimal number of API calls per simulation run.11
• Integration: Excellent. CrewAI is built on top of LangChain, giving it access to a vast ecosystem of tools and integrations. It supports virtually all major LLM providers (OpenAI, Anthropic, Google) through standardized interfaces, which is essential for implementing the cost-saving strategy of LLM cascading discussed later in this report.12

1.4 Practical Implementation Pathway: Building Your First "Architecture Review Crew"

This section provides a step-by-step blueprint to create a working prototype of the simulator using CrewAI. The expected timeline from setup to a functioning prototype is approximately 2-3 days.

Step 1: Environment Setup
First, install the necessary Python libraries and configure the environment with the required API keys.

Bash

pip install crewai crewai-tools langchain-openai langchain-anthropic langchain-google-genai

Set the API keys for the desired LLM providers as environment variables:

Bash

export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export GOOGLE_API_KEY="your_google_api_key"

Step 2: Defining Agent Personas
In a Python script, define the three core agent personas. The backstory is a critical part of the prompt that provides the LLM with context and guides its behavior.

Python

from crewai import Agent
from langchain_openai import ChatOpenAI

# Initialize the LLM you want to use for the agents
# This can be swapped for Anthropic or Google models
llm \= ChatOpenAI(model="gpt-4o")

# Agent 1: The Solution Architect
solution_architect_agent \= Agent(
role='Expert Software Architect',
goal='Generate a diverse set of 8-12 innovative and practical technical solutions for a given problem.',
backstory=(
"You are a seasoned software architect with decades of experience across multiple domains. "
"You are known for your creative, out-of-the-box thinking and your ability to propose solutions "
"that others might overlook, ranging from simple scripts to complex, scalable systems."
),
llm=llm,
verbose=True
)

# Agent 2: The Technical Critic
technical_critic_agent \= Agent(
role='Pragmatic Technical Critic',
goal='Analyze proposed technical solutions to identify potential flaws, risks, and hidden complexities.',
backstory=(
"You are a cynical but highly respected principal engineer. Your job is to poke holes in "
"architectural proposals. You have a keen eye for scalability bottlenecks, security vulnerabilities, "
"maintenance nightmares, and operational complexities. You value simplicity and robustness over novelty."
),
llm=llm,
verbose=True
)

# Agent 3: The Developer Profile Agent
developer_profile_agent \= Agent(
role='Developer Profile Analyst',
goal='Evaluate proposed solutions against a specific developer profile, scoring them for alignment.',
backstory=(
"You are an AI assistant that embodies the preferences of a specific developer. You must evaluate "
"solutions strictly based on this profile: \n"
"- Strong preference for Python solutions. \n"
"- Dislikes complex JavaScript and front-end development. \n"
"- Values automation and 'set it and forget it' approaches. \n"
"- Prioritizes solutions that integrate well with a VSCode workflow. \n"
"- Low tolerance for solutions with high maintenance overhead."
),
llm=llm,
verbose=True
)

Step 3: Crafting Tasks
Define the sequence of tasks that the crew will execute. The output of each task is automatically passed as context to the next one in a sequential process.

Python

from crewai import Task, Crew, Process

# Define the problem to be solved
problem_description \= (
"I need a tool that watches a specific file for changes. When the file is saved, "
"the tool should trigger the Gemini CLI with the file's content as a prompt. "
"The response from the Gemini CLI should then be appended to a specific markdown file."
)

# Task 1: Generate Solutions
generate_solutions_task \= Task(
description=f"Generate 8-12 diverse technical solutions for the following problem: {problem_description}",
expected_output="A numbered list of 8-12 distinct solution approaches, each with a brief description of the technology stack and implementation strategy.",
agent=solution_architect_agent
)

# Task 2: Critique Solutions
critique_solutions_task \= Task(
description="For each solution provided, conduct a critical analysis. Identify potential weaknesses, risks, and trade-offs.",
expected_output="For each solution, provide a 'Critique' section detailing its potential downsides regarding scalability, security, complexity, and maintenance.",
agent=technical_critic_agent
)

# Task 3: Score Solutions Against Profile
score_solutions_task \= Task(
description="Evaluate each of the critiqued solutions against the developer's profile. Provide an alignment score from 1 (poor fit) to 5 (perfect fit) and a rationale.",
expected_output="A final report that includes each solution, its critique, a 'Profile Alignment Score' (1-5), and a detailed justification for the score.",
agent=developer_profile_agent
)

Step 4: Assembling and Running the Crew
Finally, instantiate the Crew with the defined agents and tasks, and kick off the process.

Python

# Create the crew
architecture_crew \= Crew(
agents=[solution_architect_agent, technical_critic_agent, developer_profile_agent],
tasks=[generate_solutions_task, critique_solutions_task, score_solutions_task],
process=Process.sequential,
verbose=2
)

# Execute the crew's tasks
result \= architecture_crew.kickoff()

print("## Final Report ##")
print(result)

1.5 Alternative Pathway: When to Graduate to LangGraph

While CrewAI is the ideal starting point, there are scenarios where its high-level abstractions may become limiting. LangGraph should be considered when the simulation requires more sophisticated control flow.10 Such scenarios include:

• Complex Feedback Loops: If a solution is rejected by the critic, the workflow could loop back to the architect with specific instructions for revision. This cyclical process is a natural fit for a graph structure but is difficult to implement in a strictly sequential framework.14
• Dynamic Branching: The workflow could branch based on the content of a solution. For example, if a solution involves JavaScript, it could be routed to a specialized "JavaScript Expert" agent for a deeper critique. LangGraph's conditional edges are designed for this type of dynamic routing.10
• Persistent, Granular State Management: For very long, multi-step simulations where a shared state object needs to be precisely updated and accessed by different agents throughout the process, LangGraph's explicit state management provides more control and visibility than CrewAI's implicit context passing.14

II. Fueling the Simulator: Systematic Generation of Diverse Technical Solutions

The primary value of the simulator lies in its ability to explore a wide solution space, moving beyond the one or two generic answers typically provided by a single LLM query. Standard brainstorming prompts often lead to "convergent thinking," where the model defaults to the most statistically probable (and often least innovative) solutions.17 To counteract this, it is necessary to employ structured creativity techniques that force the

SolutionArchitectAgent to engage in "divergent reasoning" and explore less common but potentially superior architectural paths.18

2.1 Method 1: Architectural Pattern Prompting

This method leverages the LLM's extensive training data on established software architecture patterns to generate solutions that conform to specific, well-defined structures. By providing the pattern as a constraint, the prompt guides the model to generate a solution within that framework.20

• Concept: The core idea is to treat architectural patterns as templates for ideation. Instead of asking for a solution in general, one asks for a solution that implements, for example, a Microservices pattern or an Event-Driven pattern for the specific problem. This systematically produces structurally different solutions.
• Prompt Template: The following template can be used within the generate_solutions_task for the SolutionArchitectAgent. The process involves running this task multiple times, each time with a different and.
  Code snippet
  Act as a senior software architect. You are tasked with solving the following problem:
  Problem:

Your task is to generate a specific, implementable solution that strictly adheres to the **** architectural pattern.

**Pattern Description:**
Example: "Event-Driven Architecture: A system where decoupled components communicate asynchronously by producing and consuming events. This pattern enhances scalability and resilience."

**Constraints:**
- The solution must be implementable primarily in Python.
- It should align with the preferences of a developer who values automation and low maintenance.

Provide a high-level design, identify the key components, and suggest specific libraries or technologies for implementation.

• Application: To generate a diverse set of 8-12 solutions, the orchestrator would invoke the agent with a list of patterns to explore, such as:
• Layered Monolith
• Microservices
• Event-Driven Architecture
• Pipes and Filters
• Plugin Architecture
• Serverless Architecture
• Space-Based Architecture
• Client-Server

2.2 Method 2: Morphological Analysis for Solution Decomposition

Morphological analysis is a powerful, systematic method for breaking down a complex problem into its fundamental components and exploring all possible combinations of their respective solutions.22 This technique, developed by astrophysicist Fritz Zwicky, is exceptionally effective at uncovering novel and unexpected solutions that might be missed by conventional brainstorming.23

• Step 1: Deconstruct the Problem into Parameters. The first step is to identify the core, independent functions that any solution must perform. For the "file-watching tool" problem, these parameters are:
• File System Listener: How is the file change detected?
• Trigger Mechanism: How is the main logic initiated after detection?
• LLM Interaction: How is the LLM API called?
• Output Handling: How is the result saved?
• Hosting Environment: Where does the code run?
• Step 2: Brainstorm Variations for Each Parameter. For each parameter identified, generate a list of different ways it can be implemented. This creates a "Morphological Box" or matrix.

Parameter	Variation 1	Variation 2	Variation 3	Variation 4	Variation 5
Listener	Python watchdog	Node.js chokidar	Go fsnotify	OS-level inotify	VSCode API
Trigger	Direct Function Call	Message Queue	HTTP Request	Cron Job	Database Trigger
LLM Interaction	Gemini CLI	OpenAI SDK	Anthropic SDK	Local LLM API	Replicate API
Output Handling	Simple File I/O	sed Command	Database Append	REST API Call	Markdown Library
Hosting	Local Script	Docker Container	Serverless Fn	VSCode Extension	Systemd Service

• Step 3: Prompt the LLM to Synthesize Novel Solutions. The true power of this method comes from combining variations across the parameters to create novel solution architectures. The SolutionArchitectAgent can be prompted to synthesize and elaborate on these combinations.
  Code snippet
  Design a complete technical solution for the file-watching problem by integrating the following specific components:
  - Listener: Use the **VSCode File Watcher API**.
  - Trigger: Use an **HTTP Request** to a local server.
  - LLM Interaction: Use the **Gemini CLI**.
  - Output Handling: Use a dedicated **Python Markdown library**.
  - Hosting: Package the entire solution as a **VSCode Extension**.

Explain how these components would integrate, the data flow between them, and the key implementation challenges.

By systematically generating and evaluating different paths through the morphological box, the simulator can explore a vast and diverse solution space far beyond the obvious choices.

2.3 Method 3: Adapting TRIZ for Contradiction-Driven Innovation

TRIZ, the Theory of Inventive Problem Solving, is a sophisticated engineering methodology developed from the analysis of millions of patents.26 While a full implementation is complex, its core concept of resolving "technical contradictions" can be adapted into a powerful prompting strategy to generate non-obvious solutions.

• Simplified Concept for LLMs: A technical contradiction occurs when improving one aspect of a system inevitably worsens another.28 For example, increasing a system's performance often increases its complexity. TRIZ provides a set of "Inventive Principles"—generalized strategies for resolving such contradictions. We can prompt the LLM to apply these principles to a core contradiction in the software problem.
• Application Example:
• Identify the Core Contradiction: For the file-watching tool, a key contradiction is: "The solution must be highly responsive to file changes (improving feature) but must also have minimal idle resource consumption (worsening feature)." A naive solution that constantly polls the file system would be responsive but resource-intensive.
• Select an Inventive Principle: From the 40 TRIZ principles, "Segmentation" (Principle 1) suggests breaking an object or system into independent, separable parts.26

• Formulate the Prompt:
  Code snippet
  Act as an inventive problem solver. The goal is to design a file-watching tool.
  The core technical conflict is achieving **high responsiveness** while maintaining **minimal resource consumption**.

  Apply the inventive principle of **'Segmentation'** to resolve this conflict. Describe a software architecture that breaks the system into independent parts, where each part only runs when absolutely necessary. How can you separate the 'detection' mechanism from the 'processing' mechanism to optimize for both responsiveness and efficiency?

• Expected Output: This type of prompt steers the LLM away from standard library-based solutions and toward architectures that inherently resolve the stated conflict. A likely output would be a serverless architecture (e.g., using AWS S3 event notifications to trigger a Lambda function). This is a non-obvious but highly effective solution that perfectly embodies the "Segmentation" principle by separating the storage/detection mechanism (S3) from the processing logic (Lambda), ensuring zero resource consumption when idle.

III. Running the Simulation: Iterative Refinement and Decision Analysis

Once the agents can generate and critique solutions, the next step is to build the simulation framework itself. This involves managing the state of the simulation between runs, allowing for parameter adjustments, and incorporating methods to handle uncertainty in the evaluation process.

3.1 Managing Simulation State for Iterative Refinement

A key requirement is the ability to tweak parameters and re-run the simulation without starting from scratch. This requires a mechanism for managing state. While some agentic frameworks offer complex state management capabilities, a lightweight, file-based approach is sufficient and cost-effective for this project's scope.29

• Proposed State Management Architecture:
• Configuration File: A central config.py or config.json file holds all adjustable parameters, including the developer profile, the problem description, and any evaluation weights.
• Initial Run: The main script reads the configuration and executes the full CrewAI workflow (Generate -> Critique -> Score). The final, detailed report is saved as a structured file (e.g., run_01_output.json).
• Parameter Adjustment: The user modifies a value in the config.py file. For example, changing the developer profile's tolerance for maintenance from 'low' to 'medium'.
• Iterative Run: The main script is executed again. This time, it can be designed to be "state-aware." If only the developer profile has changed, it can skip the computationally expensive "Generate" and "Critique" tasks. It loads the solutions from run_01_output.json and re-runs only the final "Score" task with the updated agent profile.

This approach separates the simulation's state (the generated solutions and their objective critiques) from the parameters (the subjective evaluation criteria). This separation is a crucial token optimization strategy, as it avoids re-running expensive generation steps for minor tweaks to the evaluation logic.31 For more advanced use cases, frameworks like StateFlow, which model LLM workflows as finite state machines, offer more robust control over state transitions and actions within each state.33

3.2 Lightweight Simulation Frameworks in Python

While large-scale business process simulation software like AnyLogic provides powerful features for modeling complex systems, they are not suitable for this project.34 The principles of discrete-event simulation, however, are highly relevant.36 The goal is to find a lightweight, Python-native approach.

• SimService: This library allows simulations to be deployed as memory-isolated services that can be controlled via interactive proxy objects.37 In this context, each agent in the crew could be wrapped in a SimService, allowing the main script to interact with them, change their internal parameters (like the developer profile), and re-trigger their tasks without re-instantiating the entire system.
• Eclypse: This is an event-driven simulation framework for cloud-edge systems.40 Its event-based architecture is conceptually a good fit for modeling the file-watching use case, where a "file change" event triggers a sequence of actions.
• Practical Recommendation: For the initial prototype, the overhead of learning and integrating a dedicated simulation library is unnecessary. A custom Python script that implements the file-based state management described above provides the most direct and efficient path to a working solution. The core logic can be encapsulated in functions, and the simulation "state" can be managed using simple Python dictionaries and JSON files.

3.3 Adapting Monte Carlo Methods for Technical Decision-Making

A significant limitation of a simple scoring rubric is that it treats all criteria as deterministic, single-point values. In reality, factors like "Implementation Complexity" or "Maintenance Cost" are uncertain. Monte Carlo simulation is a powerful mathematical technique for modeling this uncertainty by representing variables as probability distributions rather than single values.41

• Concept: Instead of the critic agent assigning a single score of 3/5 for complexity, it can be prompted to estimate a range or a distribution. The simulation then runs thousands of iterations, sampling random values from these distributions to generate a distribution of possible outcomes, providing a much richer understanding of the risks associated with each solution.44
• High-Level Implementation Steps:
• Model the Evaluation: Create a Python function that calculates a total score for a solution based on weighted criteria (complexity, cost, performance, etc.).
• Define Probability Distributions: Modify the critic and profile agents' prompts. Instead of asking for a single score, ask for a distribution's parameters.
  • Prompt Example: "For the 'Implementation Complexity' of this solution, estimate a likely score (mean) and a measure of uncertainty (standard deviation) on a scale of 1 to 5."
  • The agent might return {'mean': 2.0, 'std_dev': 0.5} for a simple Python solution, and {'mean': 4.5, 'std_dev': 1.0} for a complex one involving unfamiliar technology.
• Run the Simulation: Using a loop, perform 10,000 to 100,000 iterations. In each iteration:
  • For each criterion of each solution, draw a random sample from its defined probability distribution (e.g., using numpy.random.normal(mean, std_dev)).
  • Calculate the total score for that iteration.
• Analyze the Results: After the loop completes, there will be a distribution of 10,000 possible scores for each solution. This allows for a more nuanced comparison. Solution A might have a higher average score but also a much wider distribution (higher risk), while Solution B might have a slightly lower average but be far more predictable (lower risk).
• Recommended Libraries: NumPy and SciPy are essential for handling the statistical distributions and random sampling. Matplotlib or Seaborn can be used to visualize the resulting outcome distributions, making the risk profiles of different solutions immediately apparent.

IV. Personalizing the Evaluation: Predicting Solution Acceptance and Rejection

A generic evaluation is of limited use; the simulator's true power comes from its ability to tailor its analysis to the specific preferences and constraints of the individual developer. This requires creating a quantifiable model of the developer's profile and using it to score the generated solutions.

4.1 Creating a Quantifiable Developer Profile

The first step is to translate the qualitative preferences from the user query into a structured format that an AI agent can process for scoring.46 A weighted feature vector, implemented as a simple Python dictionary, is an effective and straightforward method.

• Technique: Each key in the dictionary represents a specific technical attribute (e.g., language, environment, architectural principle), and the value represents the strength and direction of the preference (positive for preferred, negative for disliked).
• Example Developer Profile Vector:
  Python
  DEVELOPER_PROFILE \= {
  "language:python": 1.0,
  "language:javascript": -0.8,
  "language:go": 0.2,
  "environment:vscode": 0.9,
  "maintenance:low": 1.0,
  "maintenance:medium": -0.5,
  "maintenance:high": -1.0,
  "complexity:low": 0.7,
  "complexity:high": -0.9,
  "automation:set_and_forget": 1.0,
  "setup:manual_config": -0.6
  }

This profile serves as the "ground truth" for the DeveloperProfileAgent and is included in its system prompt to guide all evaluations.

4.2 Scoring Solutions with Content-Based Filtering

This approach adapts a core technique from recommender systems, where the goal is to recommend "items" (technical solutions) to a "user" (the developer) based on their profile.48

• Step 1: Feature Extraction from Solutions. The DeveloperProfileAgent is tasked with analyzing the natural language description of each generated solution and extracting a corresponding feature vector.

• Prompt for Feature Extraction:
  Code snippet
  Analyze the following solution description and extract its key technical attributes. Present these attributes as a JSON object with keys matching the format 'category:value' and a value of 1.0.

  Solution: "This approach uses a Node.js script with the 'chokidar' library to watch the file system. It runs as a persistent background process that requires initial setup and periodic checks. It is highly responsive but adds a dependency on the Node.js ecosystem."

  Extract the feature vector.

• Expected LLM Output (JSON):
  JSON
  {
  "language:javascript": 1.0,
  "maintenance:medium": 1.0,
  "complexity:low": 1.0,
  "setup:manual_config": 1.0
  }

• Step 2: Calculate Profile Alignment Score. Once the solution's feature vector is extracted, the alignment score is calculated by taking the dot product of the solution vector and the developer profile vector. This is a simple multiplication of corresponding feature weights.

• Calculation Example:
  • Score \= (profile["language:javascript"] * solution["language:javascript"]) + (profile["maintenance:medium"] * solution["maintenance:medium"]) +...
  • Score \= (-0.8 * 1.0) + (-0.5 * 1.0) +...
    A positive final score indicates alignment, while a negative score indicates a conflict with the developer's preferences. This quantitative score can then be normalized to the 1-5 scale required by the evaluation rubric.

4.3 Predicting User Satisfaction and Rejection Patterns

By analyzing the features that consistently contribute to low alignment scores across multiple simulations, the system can begin to identify and flag common "rejection patterns".50 This moves beyond simple scoring to proactive identification of friction points.

The DeveloperProfileAgent's task can be expanded to not only calculate a score but also to provide a qualitative analysis of why a solution might be rejected.

• Prompt for Identifying Friction Points:
  Code snippet
  Based on the developer's profile and your analysis of this solution, identify the top 1-2 factors that would most likely cause the developer to reject this solution before implementation. Explain your reasoning.

This provides valuable insight into which constraints are the most significant drivers of the final decision.

4.4 Implementation with Python Libraries

While the core logic can be implemented with standard Python dictionaries and loops, more sophisticated feature extraction can be achieved using common NLP and data science libraries.

• scikit-learn: The TfidfVectorizer or CountVectorizer can be used to create more nuanced vector representations of the solution descriptions, moving beyond simple keyword matching.
• numpy: This library is ideal for performing the vector operations (like dot product) efficiently, especially if the feature sets become large.

For the initial prototype, a simple keyword-based feature extraction process executed by the LLM itself is the most direct and cost-effective approach.

The final output of this entire multi-agent process is a populated evaluation rubric for each solution, providing a structured basis for the final human decision.

Table 2: Solution Evaluation Rubric Template

Criterion	Score (1-5)	Rationale / Evidence (Agent Generated)	Key Risks (Critic Generated)
Implementation Complexity	4	"Low complexity. Uses the standard Python watchdog library, which is well-documented. Implementation requires less than 50 lines of code."	"The watchdog library has known issues with certain network file systems, which could be a problem in some environments."
Estimated Cost	5	"Negligible cost. Runs as a local script with no cloud hosting or paid API dependencies beyond the core LLM calls."	"If the file changes very frequently, this could lead to high LLM API costs due to the number of triggers."
Performance/Scalability	2	"Performance is tied to the local machine's resources. Does not scale beyond a single instance without significant re-architecture."	"A high frequency of file changes could lead to a backlog of processing tasks, causing significant delays."
Maintenance Overhead	4	"Low maintenance. It is a single script with one major dependency. 'Set it and forget it' once running."	"Dependency updates for watchdog could introduce breaking changes over time."
Profile Alignment Score	5	"Perfect alignment. Uses Python, runs locally, requires minimal setup, and fits a 'set it and forget it' model."	"None."
Overall Score	4.0

V. The Economic Engine: Budget-Conscious Implementation and Optimization

Meeting the stringent budget of $100 for development and multiple simulation runs is a primary engineering constraint. The cost of multi-agent systems is driven almost entirely by LLM API calls, which are priced per token of input and output.52 Unchecked, a single complex simulation run could easily exceed the entire project budget. Therefore, designing for cost-effectiveness is not an afterthought but a core architectural principle.

5.1 Architecture Pattern for Cost Control: LLM Cascading

The single most effective strategy for cost control is LLM Cascading, also known as dynamic model selection or routing.32 The principle is to use a tiered approach: apply cheap, fast models for simple, high-volume tasks, and reserve expensive, powerful models only for tasks that require deep reasoning or creativity.54 A multi-agent architecture is perfectly suited for this, as each agent can be powered by a different, appropriately chosen LLM.

• Proposed LLM Cascade for the Simulator:
• SolutionArchitectAgent (Generation): This task requires creativity and knowledge of diverse architectures. A high-performance, mid-cost model is ideal.
  • Recommendation: Anthropic's Claude 3.5 Sonnet or OpenAI's GPT-4o.
• TechnicalCriticAgent (Critique): This task requires deep reasoning to identify subtle flaws and second-order effects. A powerful model is necessary.
  • Recommendation: OpenAI's GPT-4o or Anthropic's Claude 3 Opus (if budget allows for specific runs).
• DeveloperProfileAgent (Scoring & Feature Extraction): This is a relatively simple classification and extraction task. It does not require complex reasoning. A cheap, high-speed model is the best choice.
  • Recommendation: Anthropic's Claude 3 Haiku or OpenAI's GPT-4o mini.

Implementing this cascade can reduce the total cost of a simulation run by 50-90% compared to a naive approach of using a single high-end model for all agents.54

5.2 Advanced Token Optimization Strategies

Beyond model selection, several other techniques can be employed to minimize token consumption at every stage of the workflow.

• Context Compression and Summarization: The communication between agents is a major source of token usage. Instead of passing the full, verbose output of one agent to the next, use a cheap model (like Haiku) to create a concise summary. For example, before the 5000-token output from the Architect is passed to the Critic, a summarization step can condense it to a 500-token brief containing only the essential information.32
• Prompt Engineering for Brevity:
• Concise Instructions: Remove all unnecessary words, examples, and conversational filler from agent prompts. Every token in the system prompt is paid for on every single API call.32
• Structured Outputs: Instruct the models to respond in a specific format like JSON. Many modern APIs (including OpenAI's and Anthropic's) have a dedicated "JSON mode" that ensures valid output without needing to waste tokens on verbose formatting examples in the prompt.55
• Intelligent Caching: For deterministic sub-tasks, cache the results. If an agent is asked to perform the exact same analysis twice, the result should be served from a local cache instead of making a redundant API call.32

5.3 Practical Cost Analysis and Comparison

To demonstrate the financial viability of this approach, this section presents a cost estimate for a single simulation run that generates and evaluates 10 technical solutions. The analysis compares a "Naive Approach" (using GPT-4o for all tasks) with an "Optimized Cascading Approach."

Table 3: LLM API Cost Comparison (USD per 1 Million Tokens)

Provider	Model	Input Cost / 1M tokens	Output Cost / 1M tokens	Key Considerations
OpenAI	GPT-4o	$5.00	$15.00	High performance, multimodal, fast. Excellent for reasoning and critique.59
OpenAI	GPT-4o mini	$0.15	$0.60	Extremely cost-effective, very fast. Ideal for simple, high-volume tasks like classification and summarization.59
Anthropic	Claude 3 Opus	$15.00	$75.00	Top-tier reasoning, very large context window. Use sparingly for the most complex tasks.59
Anthropic	Claude 3.5 Sonnet	$3.00	$15.00	Excellent balance of performance and cost. Strong choice for creative generation.59
Anthropic	Claude 3 Haiku	$0.25	$1.25	Fastest model in its class, very low cost. Perfect for routing, summarization, and data extraction.59
Google	Gemini 1.5 Pro	$3.50	$10.50	Very large context window (1M tokens), strong multimodal capabilities. Billed per 1k characters.60
Google	Gemini 1.5 Flash	$0.35	$1.05	Speed and cost-optimized version of Pro. Competitive with Haiku and GPT-4o mini.60

Note: Prices are based on publicly available data as of late 2024 and are subject to change. Google's pricing is often per-character and has been converted to an approximate per-token cost for comparison.

Cost Estimation for One Simulation Run (10 Solutions):

• Assumptions:
• Architect generates 10 solutions: (1k input + 8k output tokens)
• Critic analyzes 10 solutions: (8k input + 3k output tokens per solution) -> (80k input + 30k output total)
• Profile Agent scores 10 solutions: (3k input + 0.5k output tokens per solution) -> (30k input + 5k output total)
• Context Summarization (Optimized only): (11k input + 1k output tokens per solution) -> (110k input + 10k output total)
• Naive Approach (GPT-4o for all tasks):
• Architect: (1k/1M * $5) + (8k/1M * $15) \= $0.125
• Critic: (80k/1M * $5) + (30k/1M * $15) \= $0.85
• Profile Agent: (30k/1M * $5) + (5k/1M * $15) \= $0.225
• Total Estimated Cost: $1.20
• Optimized Cascading Approach:
• Architect (Claude 3.5 Sonnet): (1k/1M * $3) + (8k/1M * $15) \= $0.123
• Critic (GPT-4o): (80k/1M * $5) + (30k/1M * $15) \= $0.85
• Profile Agent (Claude 3 Haiku): (30k/1M * $0.25) + (5k/1M * $1.25) \= $0.014
• No summarization needed in this simple flow, but if added with Haiku, it would be minimal.
• Total Estimated Cost: $0.987

This detailed analysis shows that even for a relatively simple workflow, strategic model selection provides a noticeable cost reduction. For more complex workflows with more inter-agent communication and summarization steps, the savings become far more dramatic. Both approaches are well under the $20 per-run target, demonstrating the financial feasibility of the project. The entire $100 budget would allow for approximately 80-100 full simulation runs, providing ample capacity for development, testing, and repeated use.

Conclusion

This report has outlined a detailed, actionable, and budget-conscious framework for developing an LLM-based solution architecture simulator. By adopting a multi-agent paradigm, the proposed system transcends the limitations of single-LLM interactions, enabling a structured and rigorous process of solution generation, critique, and evaluation.

The recommended implementation path is clear and pragmatic, designed specifically for a solo Python developer operating under tight constraints.

1. Foundation: Begin with the CrewAI framework for its simplicity and rapid development cycle. Its role-based structure provides an intuitive and effective way to orchestrate the specialized agents required for the simulation.
2. Ideation: Fuel the simulator with diverse ideas by moving beyond simple brainstorming. Employ structured prompting techniques such as Architectural Pattern Prompting and Morphological Analysis to force the generation of a wide and non-obvious solution space.
3. Evaluation: Implement a personalized evaluation mechanism by creating a quantifiable developer profile and using it to score solutions. This ensures the final output is not just technically sound but also aligned with the developer's specific skills, preferences, and constraints.
4. Cost Control: Architect the system for financial sustainability from the outset. The LLM Cascading pattern is the most critical component for staying within budget, allowing for the strategic use of different models from providers like OpenAI, Anthropic, and Google to match task complexity with cost.

By following this blueprint, it is entirely feasible to build a working prototype within one week and for well under the $100 budget. The resulting tool will not only solve the immediate problem of evaluating file-watching solutions but will also serve as a replicable and extensible framework for making more informed, data-driven architectural decisions in any future project. This approach transforms LLMs from simple task-executors into a powerful, collaborative system for strategic technical simulation.

Works cited

1. LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead - arXiv, accessed August 22, 2025, https://arxiv.org/html/2404.04834v4
2. A Tour of Popular Open Source Frameworks for LLM-Powered Agents - Dataiku Blog, accessed August 22, 2025, https://blog.dataiku.com/open-source-frameworks-for-llm-powered-agents
3. LangGraph: Multi-Agent Workflows - LangChain Blog, accessed August 22, 2025, https://blog.langchain.com/langgraph-multi-agent-workflows/
4. AI Agent Orchestration Patterns - Azure Architecture Center | Microsoft Learn, accessed August 22, 2025, https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
5. Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency | Request PDF - ResearchGate, accessed August 22, 2025, https://www.researchgate.net/publication/391461646_Enhancing_LLM_Code_Generation_A_Systematic_Evaluation_of_Multi-Agent_Collaboration_and_Runtime_Debugging_for_Improved_Accuracy_Reliability_and_Latency
6. Multi-agent LLMs in 2024 [+frameworks] | SuperAnnotate, accessed August 22, 2025, https://www.superannotate.com/blog/multi-agent-llms
7. LLM Agent Frameworks 2025: Guide & Comparison - Chatbase, accessed August 22, 2025, https://www.chatbase.co/blog/llm-agent-framework-guide
8. LLM agent orchestration: step by step guide with LangChain and Granite - IBM, accessed August 22, 2025, https://www.ibm.com/think/tutorials/llm-agent-orchestration-with-langchain-and-granite
9. Multi-agent Orchestration Overview | by Yugank .Aman - Medium, accessed August 22, 2025, https://medium.com/@yugank.aman/multi-agent-orchestration-overview-aa7e27c4e99e
10. Comparing Open-Source AI Agent Frameworks - Langfuse Blog, accessed August 22, 2025, https://langfuse.com/blog/2025-03-19-ai-agent-comparison
11. LangChain, AutoGen, and CrewAI. Which AI Framework is Right for You in… | by Yashwant Deshmukh | Medium, accessed August 22, 2025, https://medium.com/@yashwant.deshmukh23/langchain-autogen-and-crewai-2593e7645de7
12. Autogen vs LangChain vs CrewAI | *instinctools, accessed August 22, 2025, https://www.instinctools.com/blog/autogen-vs-langchain-vs-crewai/
13. Best Architectures to Build Agentic AI: Comparing LangChain, AutoGen, CrewAI, and More, accessed August 22, 2025, https://destinovaailabs.com/blog/best-architectures-to-build-agentic-ai-comparing-langchain-autogen-crewai-and-more/
14. OpenAI Agents SDK vs LangGraph vs Autogen vs CrewAI - Composio, accessed August 22, 2025, https://composio.dev/blog/openai-agents-sdk-vs-langgraph-vs-autogen-vs-crewai
15. Choosing Between LLM Agent Frameworks | by Aparna Dhinakaran | TDS Archive - Medium, accessed August 22, 2025, https://medium.com/data-science/choosing-between-llm-agent-frameworks-69019493b259
16. Langgraph vs CrewAI vs AutoGen vs PydanticAI vs Agno vs OpenAI Swarm : r/LangChain - Reddit, accessed August 22, 2025, https://www.reddit.com/r/LangChain/comments/1jpk1vn/langgraph_vs_crewai_vs_autogen_vs_pydanticai_vs/
17. LLMs for Code Generation: A summary of the research on quality - Sonar, accessed August 22, 2025, https://www.sonarsource.com/learn/llm-code-generation/
18. Flow of Reasoning: Training LLMs for Divergent Problem Solving with Minimal Examples, accessed August 22, 2025, https://openreview.net/forum?id=HHmnfVQagN
19. Systematic Task Exploration with LLMs: A Study in Citation Text Generation - ACL Anthology, accessed August 22, 2025, https://aclanthology.org/2024.acl-long.265.pdf
20. Prompt patterns for LLMs that help you design better software - Chuniversiteit.nl, accessed August 22, 2025, https://chuniversiteit.nl/papers/prompt-patterns-for-software-design
21. How LLMs Can Help You Choose the Right Architecture Patterns ..., accessed August 22, 2025, https://www.softwarearchitectskills.com/how-llms-can-help-you-choose-the-right-architecture-patterns/
22. Morphological Analysis - (Intro to Civil Engineering) - Vocab, Definition, Explanations | Fiveable, accessed August 22, 2025, https://library.fiveable.me/key-terms/introduction-civil-engineering/morphological-analysis
23. Unlock Problem-Solving with Morphological Analysis - TinkTide, accessed August 22, 2025, https://tinktide.com/resources/discover-morphological-analysis-method
24. Morphological analysis (problem-solving) - Wikipedia, accessed August 22, 2025, https://en.wikipedia.org/wiki/Morphological_analysis_(problem-solving)
25. Mastering Morphological Analysis in Engineering - Number Analytics, accessed August 22, 2025, https://www.numberanalytics.com/blog/ultimate-guide-morphological-analysis-engineering-design
26. (PDF) TRIZ for software architecture - ResearchGate, accessed August 22, 2025, https://www.researchgate.net/publication/251716004_TRIZ_for_software_architecture
27. Theory of Inventive Problem Solving (TRIZ) Methodology - Flevy.com, accessed August 22, 2025, https://flevy.com/blog/theory-of-inventive-problem-solving-triz-methodology/
28. Unlocking Innovative Solutions with TRIZ: A Powerful Problem-Solving Methodology - SixSigma.us, accessed August 22, 2025, https://www.6sigma.us/six-sigma-in-focus/triz-inventive-problem-solving-methodology/
29. Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI. - arXiv, accessed August 22, 2025, https://arxiv.org/html/2501.18668
30. Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI - ChatPaper, accessed August 22, 2025, https://chatpaper.com/paper/103879
31. Why is simulating and evaluating LLM agents still this painful? : r/AI_Agents - Reddit, accessed August 22, 2025, https://www.reddit.com/r/AI_Agents/comments/1meveri/why_is_simulating_and_evaluating_llm_agents_still/
32. Cost Optimization Strategies for Enterprise AI Agents | Datagrid ..., accessed August 22, 2025, https://www.datagrid.com/blog/8-strategies-cut-ai-agent-costs
33. StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows - arXiv, accessed August 22, 2025, https://arxiv.org/html/2403.11322v3
34. Business Process Simulation Software - AnyLogic, accessed August 22, 2025, https://www.anylogic.com/business-processes/
35. From what-if to what-now: AI and simulation for business decisions - AnyLogic, accessed August 22, 2025, https://www.anylogic.com/blog/from-what-if-to-what-now-ai-and-simulation-for-business-decisions/
36. Process Simulation Software vs Traditional Business Modeling: The Real Differences, accessed August 22, 2025, https://www.simio.com/process-simulation-software-vs-traditional-business-modeling/
37. SimService: a lightweight library for building simulation services in Python - Oxford Academic, accessed August 22, 2025, https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btae009/56416842/btae009.pdf
38. SimService: a lightweight library for building simulation services in Python | Bioinformatics, accessed August 22, 2025, https://academic.oup.com/bioinformatics/article/40/1/btae009/7574575
39. SimService: a lightweight library for building simulation services in Python - PubMed, accessed August 22, 2025, https://pubmed.ncbi.nlm.nih.gov/38237907/
40. Eclypse: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum, accessed August 22, 2025, https://arxiv.org/html/2501.17126v1
41. What is The Monte Carlo Simulation? - AWS, accessed August 22, 2025, https://aws.amazon.com/what-is/monte-carlo-simulation/
42. What is Monte Carlo Simulation? - Lumivero, accessed August 22, 2025, https://lumivero.com/software-features/monte-carlo-simulation/
43. Monte Carlo Simulation: What It Is, How It Works, History, 4 Key Steps - Investopedia, accessed August 22, 2025, https://www.investopedia.com/terms/m/montecarlosimulation.asp
44. An Introduction to Monte Carlo Simulation - Lumivero, accessed August 22, 2025, https://lumivero.com/resources/blog/an-introduction-to-monte-carlo-simulation/
45. Monte Carlo Simulation: Make Better Decisions - Statistics By Jim, accessed August 22, 2025, https://statisticsbyjim.com/probability/monte-carlo-simulation/
46. What is a Recommendation System? | Data Science | NVIDIA Glossary, accessed August 22, 2025, https://www.nvidia.com/en-us/glossary/recommendation-system/
47. What is User preference modeling | AI Basics | AI Online Course, accessed August 22, 2025, https://www.aionlinecourse.com/ai-basics/user-preference-modeling
48. Recommender Systems in Python 101 - Kaggle, accessed August 22, 2025, https://www.kaggle.com/code/gspmoreira/recommender-systems-in-python-101
49. Recommendation System in Python - GeeksforGeeks, accessed August 22, 2025, https://www.geeksforgeeks.org/machine-learning/recommendation-system-in-python/
50. Predicting life satisfaction using machine learning and explainable AI - PMC, accessed August 22, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11137391/
51. Machine Learning with a Reject Option: A survey - arXiv, accessed August 22, 2025, https://arxiv.org/html/2107.11277v3
52. APIs pricing comparison - Technical Knowledge Base, accessed August 22, 2025, https://ersantana.com/llm/api_pricing_comparison
53. Cost Management for LLM Agents - ApX Machine Learning, accessed August 22, 2025, https://apxml.com/courses/multi-agent-llm-systems-design-implementation/chapter-6-system-evaluation-debugging-tuning/managing-llm-agent-costs
54. A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks - arXiv, accessed August 22, 2025, https://arxiv.org/html/2411.07464v1
55. Token Optimization Strategies for AI Agents | by Netanel Avraham | Elementor Engineers | Aug, 2025 | Medium, accessed August 22, 2025, https://medium.com/elementor-engineers/optimizing-token-usage-in-agent-based-assistants-ffd1822ece9c
56. [2411.07464] BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks - arXiv, accessed August 22, 2025, https://arxiv.org/abs/2411.07464
57. How to Build Multi Agent AI Systems With Context Engineering - Vellum AI, accessed August 22, 2025, https://www.vellum.ai/blog/multi-agent-systems-building-with-context-engineering
58. Mastering AI Token Optimization: Proven Strategies to Cut AI Cost - 10Clouds, accessed August 22, 2025, https://10clouds.com/blog/a-i/mastering-ai-token-optimization-proven-strategies-to-cut-ai-cost/
59. LLM Cost Calculator: Compare OpenAI, Claude2, PaLM, Cohere & More - YourGPT, accessed August 22, 2025, https://yourgpt.ai/tools/openai-and-other-llm-api-pricing-calculator
60. Best Enterprise LLM Solutions - AI Comparisons - Aloa, accessed August 22, 2025, https://aloa.co/ai/comparisons/llm-comparison/best-enterprise-llm-solutions