How to Build a Multi-Agent Loan Underwriting Committee in Python¶

Good credit decisions surface both the case for and against an application before a senior decides. This recipe uses the Debate pattern: an approve-advocate and a decline-advocate argue the loan with specific evidence, and a senior-underwriter judge weighs the exchange into a decision with conditions — an auditable alternative to a single opaque score.

Patterns used: Debate

Architecture¶

flowchart TD
    A[Loan Application] --> D{Debate}
    D --> AP[Approve Advocate]
    D --> DC[Decline Advocate]
    AP <-->|rounds| DC
    AP --> J[Senior Underwriter\nJudge]
    DC --> J
    J --> R[Decision + Conditions]

Implementation¶

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.resolution import Debate
from pyagent_providers import AnthropicLLM, GeminiLLM

committee = Debate(
    debaters=[
        Agent(
            "approve_advocate",
            GeminiLLM("gemini-2.5-pro"),
            system_prompt=(
                "Argue to APPROVE this loan. Cite debt-to-income, credit history, collateral, and "
                "stable income. Pre-empt the decline case with mitigants and proposed conditions."
            ),
        ),
        Agent(
            "decline_advocate",
            GeminiLLM("gemini-2.5-pro"),
            system_prompt=(
                "Argue to DECLINE this loan. Focus on repayment risk, thin file, concentration, and "
                "downside scenarios. Rebut the approve case with specific counter-evidence."
            ),
        ),
    ],
    judge=Agent(
        "senior_underwriter",
        AnthropicLLM("claude-sonnet-4-20250514"),
        system_prompt=(
            "You are the senior underwriter. Weigh both arguments and decide: APPROVE, "
            "APPROVE WITH CONDITIONS, or DECLINE. State the key reasons and any conditions "
            "(rate, term, collateral, covenants). Be explicit about the deciding factor."
        ),
    ),
    rounds=2,
)

result = asyncio.run(committee.run(
    "Application: $250k 7-year business loan. DTI 38%, credit score 690, 2 years trading, "
    "revenue $480k (growing), $90k equipment as collateral, one prior late payment."
))
print(result.output)
print(f"Rounds: {result.metadata['rounds']}")

Expected output¶

DECISION — APPROVE WITH CONDITIONS

Deciding factor: collateral + revenue growth offset the thin file and one late payment.
Conditions: rate +1.5% risk premium, personal guarantee, quarterly financials covenant,
            cap utilization until 12 clean months.
Approve case: serviceable DTI, growing revenue, secured.
Decline case: short trading history, single collateral asset.

Rounds: 2

The transcript of both advocates and the judge's reasoning is your audit trail for regulators.

Customization¶

Add a third advocate¶

committee.debaters.append(
    Agent("policy_advocate", GeminiLLM("gemini-2.5-pro"),
          system_prompt="Argue from lending-policy and regulatory limits (LTV, DTI caps)."),
)

Tune the debate depth¶

committee = Debate(debaters=committee._debaters, judge=committee._judge, rounds=3)

Structured decision¶

committee.judge.system_prompt += " Output as JSON: {decision, rate, conditions[], deciding_factor}."

When to Use¶

Situation	Use Debate?
A decision benefits from explicit for/against argument	✅ Yes
You need an auditable rationale, not just a score	✅ Yes
Independent graders should vote a label	❌ Use Voting
A document moves through fixed processing steps	❌ Use Loan Origination Workflow

Cost Profile¶

Driver	Typical model	Avg cost	Volume (1k apps/mo)
2 debaters × 2 rounds	gemini-pro	$0.012	$360/mo
Judge	claude-sonnet	$0.006	$180/mo
Per application	mix	~$0.018	~$540/mo