LLM Agents Managing a Virtual Vending Machine: A Benchmark Study

Study of LLMs managing a virtual vending machine business. While Claude 3.5 Sonnet turned $500 into $2,217 on average, all models eventually failed through mismanaged inventory, confused scheduling, or complete behavioral breakdowns - highlighting key limitations in AI's long-term reliability.

A recent benchmark study tested various LLM models' ability to manage a virtual vending machine business, revealing significant challenges in AI's capacity for practical business management.

Experiment Setup

4 Leading Models Tested (5 runs each)
- Claude 3.5 Sonnet
- o3-mini
- GPT-4o
- Gemini 1.5 Pro
Each run: Up to 2,000 messages
Duration: 5-10 hours per run
Starting conditions: $500 initial balance, $2 daily fee

sequence diagram

Key Findings

Most agents failed due to:

Poor financial management
Ineffective inventory control
Decision-making paralysis
Inability to maintain consistent cash flow

Common Failure Pattern

Agents typically followed a predictable path to bankruptcy:

Overinvestment in initial inventory
Rapid depletion of cash reserves
Missed daily fee payments
Bankruptcy after 10 consecutive missed payments

Implications (from the Author of AI Spectrum)

This study highlights current limitations of autonomous LLM agents in:

Practical business operations
Long-term planning
Financial decision-making
Resource management

The high variance in performance suggests that autonomous agents are not yet reliable for critical business operations. Instead, the findings point to two key recommendations:

Structured Over Autonomous: Use structured AI workflows with predefined guardrails rather than fully autonomous agents. This provides better control and predictability.
Ensemble Decision Making: Implement multiple agent instances working in parallel with:
- Several worker agents (3-4) tackling the same task
- A supervisor agent evaluating and selecting the best outputs
- Clear guardrails and validation checks at each step

Despite advanced capabilities in other areas, even leading LLMs struggled with basic business sustainability. This reinforces the need for semi-structured approaches and robust oversight mechanisms when deploying AI in operational contexts.

References

Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents - Axel Backlund and Lukas Petersson, Andon Labs (February 2025)
Interactive Vending-Bench Evaluation - Try the benchmark yourself by playing the role of the agent in a shorter version