Determinism in Agentic Payments: An Empirical Analysis of Payment Architecture Failures in LLM-Integrated Systems

AI agents are rapidly becoming the interface layer for modern commerce. From conversational shopping assistants to autonomous checkout and subscription management, agentic systems are beginning to execute workflows that were once explicitly human-controlled.

But there is a critical architectural question that the industry has largely avoided:

Can probabilistic AI systems safely execute deterministic financial transactions?

This research answers that question empirically.

Based on 160,000 simulated payment transactions and 1,100+ real LLM API validations, our findings show that direct payment gateway integration with LLM-based agents introduces systemic, unfixable failure modes — independent of model quality, prompt engineering, or provider choice.

Executive Summary (What This Study Proves)

This study evaluates two architectural approaches used in agentic payment systems:

1. Direct Integration Architecture
   LLM agents directly invoke payment gateway APIs using tool/function calling.
2. Mandate-Based Architecture
   LLM agents are restricted to intent capture, while a deterministic, cryptographically verified layer authorizes and executes payments.

The results are unambiguous.

• Direct LLM–payment integrations failed in 36.98% of transactions
• Mandate-based architecture recorded a 0.00% failure rate
• Prompt-injection attacks succeeded in 51.09% of direct integrations
• 59.78% of direct transactions violated regulatory authorization requirements

These are not edge cases. They are structural failures.

Why Agentic Payments Are Uniquely Risky

Large Language Models are probabilistic systems by design. Even when configured with deterministic decoding strategies, they exhibit:

• Numerical hallucination
• Floating-point inconsistencies
• Context window degradation
• Ambiguous intent interpretation

These behaviors are acceptable — even desirable — in conversational systems.

They are catastrophic in financial systems.

Payment infrastructure, by contrast, requires:

• Exact amount calculation
• Deterministic execution
• Explicit authorization
• Cryptographic auditability

When these two worlds are merged without architectural separation, failures become inevitable.

What We Tested (At Scale)

The study evaluated eight real-world failure modes, each tested across 10,000 controlled trials per architecture:

• Price hallucination
• Prompt injection attacks
• Context window overflow
• Floating-point rounding errors
• Authorization ambiguity
• Race conditions and duplicate charges
• UPI mandate frequency misinterpretation
• Currency confusion

To validate realism, simulations were cross-checked against real API behavior using nine production LLMs across proprietary and open-source ecosystems.

In several cases, real-world models performed worse than the simulations, especially in long multi-turn payment flows.

Key Finding: This Is Not a Model Quality Problem

One of the most important conclusions of this research is that better models do not solve this problem.

Across GPT-class models, Claude, Gemini, and open-source systems:

• Calculation errors persisted
• Context loss increased with conversation length
• Currency handling failed at high rates
• Deterministic guarantees could not be enforced

The root cause is architectural:

A probabilistic system cannot be made reliably deterministic through prompting or fine-tuning alone.

This means the common industry assumption — “we’ll fix this as models improve” — is fundamentally flawed.

The Financial and Regulatory Impact

From a business and compliance standpoint, the implications are severe.

For a mid-sized platform processing 100,000 transactions annually, direct LLM-payment integration resulted in:

• Tens of thousands of failed or disputed transactions
• Significant financial exposure from incorrect charges
• Systematic violations of PCI DSS, PSD2, and RBI guidelines
• High customer churn driven by trust erosion

These risks are non-linear — they increase as agent autonomy and transaction volume grow.

The Architectural Insight That Eliminates All Failures

The study validates a single, clear principle:

LLMs must never be trusted to calculate, authorize, or execute payments.

Instead, safe agentic payment systems require:

• A deterministic source of truth for pricing and tax
• Cryptographically signed payment mandates
• Explicit, non-ambiguous authorization
• Idempotent execution guarantees

Under this architecture:

• LLMs handle conversation and intent
• Deterministic systems handle money

When this separation is enforced, all eight tested failure modes were eliminated.

Why This Research Matters Now

Agentic commerce is moving from experimentation to production.

Without architectural guardrails, the industry risks deploying payment systems that:

• Cannot be made compliant
• Cannot be made auditable
• Cannot be made safe at scale

This research provides the first empirical evidence that agentic payments require a fundamentally new architectural approach — not better prompts, not stronger models, but deterministic separation by design.

Determinism in Agentic Payments
A research whitepaper by Phronetic AI Research

Closing Note

Agentic systems will define the next era of digital commerce.

Deterministic payments will define which of those systems survive in production.