# Multi-Agent Pattern Comparison

This document compares the monolithic "Agent" pattern previously used in the Fraud Model Explainability Assistant with the newly implemented "Workflow" pattern.

## Overview

The transition from a single, monolithic agent to a structured workflow represents a shift from implicit, LLM-driven control flow to explicit, code-driven orchestration. This is particularly valuable for high-stakes domains like fraud analysis where auditability and reliability are paramount.

## Architecture Comparison

| Feature | Monolithic Agent (Legacy) | Workflow Pattern (New) |
| :--- | :--- | :--- |
| **Control Flow** | **Implicit**: The LLM decides the loop (Reason -> Act -> Observe) entirely. | **Explicit**: Python code defines the steps (Plan -> Execute -> Synthesize). The LLM is a component called within steps. |
| **Determinism** | **Low**: The agent might skip steps, loop indefinitely, or halluncinate tool calls depending on the prompt. | **High**: The process is guaranteed to follow the defined path. It will always plan first, then execute, then respond. |
| **Auditability** | **Difficult**: Logs are a mix of thought chains and tool outputs. Hard to programmatically verify if a specific check was performed. | **High**: The `WorkflowState` object captures exactly what intent was classified, which tools were planned, and the result of each. |
| **Error Handling** | **Fragile**: If a tool fails, the agent might get confused or try to "talk its way out" of the error. | **Robust**: Errors are caught at the step level. The workflow can implement specific fallback logic (e.g., if a tool fails, log it and proceed with partial data). |
| **Latency** | **Variable**: Depends on how many "thoughts" the agent has. | **Predictable**: Evaluating intent and generating a response are fixed cognitive steps. |
| **Human-in-the-Loop** | **Complex**: Hard to interrupt the ReAct loop to ask for confirmation. | **Native**: Easy to insert a "wait for approval" step between Planning and Execution. |

## Why the Workflow Pattern Wins for Fraud Analysis

1.  **Regulatory Compliance**: We need to prove that every high-risk application undergoes specific checks (e.g., Fair Lending). A workflow guarantees this step happens; an agent does not.
2.  **Debugging**: When an answer is wrong, we can pinpoint exactly where it failed:
    - Did the Router misclassify the intent?
    - Did the Tool return the wrong data?
    - Did the Response Generator hallucinate?
3.  **Integration**: The workflow is easier to integrate into a larger system (e.g., a credit decisioning pipeline) because it has a predictable input/output contract.