StableWorks partnered with a client in the digital asset space to design and launch a peer‑to‑peer loan platform. The aim was to create an experience where borrowers could make requests in plain language and have an AI system handle the complexity behind the scenes. What used to require navigating token addresses, ratios, and risk parameters would instead feel like a guided conversation.

The Challenge

Borrowing against crypto assets can be intimidating for users and operationally risky for platforms. A simple request such as “I’d like a loan against 200 UNI” actually involves several complicated steps: the platform has to recognize the correct token, evaluate market conditions, calculate safe borrowing terms, and then handle collateral and disbursement on‑chain without mistakes.

The issues were not just technical. Most borrowers are not fluent in smart contract details or trading metrics. Asking them to manage those inputs increases confusion and errors. On the platform side, speed and safety were paramount. Once funds move on‑chain, there is no undo button. Any solution had to be clear to borrowers, strict in execution, and fully traceable for audits and compliance.

Our Solution

To meet these challenges, we built a system that combined multiple specialized AI agents with a supervisory layer that keeps them working in sequence. Each agent had a clear role, from resolving tokens to analyzing market risk. The process was structured but never exposed the borrower to unnecessary complexity.

The workflow began with interpretation. A resolver agent translated user input like “UNI” into the exact token contract and network. That was followed by a data agent that pulled pricing and liquidity information from reliable sources. The numbers themselves were never left to AI improvisation; they came directly from deterministic feeds. Once those facts were established, an AI risk agent stepped in to interpret them. This was the reasoning layer—turning volatility, liquidity depth, and concentration metrics into a risk rating and borrowing thresholds.

With risk understood, the loan agent proposed terms: how much could safely be borrowed, what interest would apply, and what the liquidation threshold should be. Finally, an on‑chain agent enforced a two‑step process: first verifying collateral deposits, and only then disbursing funds. At every stage, structured records were written to an immutable ledger, ensuring transparency and accountability.

The Borrower Journey

From the borrower’s perspective, the experience felt straightforward. They connected a wallet, typed a request in natural language, and were guided through clear steps. The system confirmed which token was being used, explained the loan terms, and walked them through the deposit and disbursement process.

Behind the scenes, much more was happening. Each agent was recording events into a permanent timeline, giving both the borrower and the platform an exact history of what occurred. If a regulator, support team, or auditor needed to review the loan, they could reconstruct every step without ambiguity.

AI Reasoning for Judgment

One of the most important lessons in this project was how to balance AI reasoning with deterministic checks. Numbers such as prices, balances, and volatility came from hard data—there was no room for model “hallucinations.” The role of AI was to interpret that data.

For instance, if the market showed high volatility and thin liquidity, the AI explained that borrowing power would be reduced and the liquidation buffer increased. Conversely, for stable and liquid tokens, the terms could be more favorable. This approach made the system both safer and more transparent. Borrowers didn’t just see a decision; they saw the reasoning behind it in plain English.

Business Impact

The result was a platform that simplified borrowing while protecting both users and the business. Borrowers no longer needed to know technical details to access a loan, and the platform could enforce strict safety protocols automatically. Every transaction was logged in an auditable event trail, creating confidence for compliance teams and reducing support costs.

Operational efficiency improved as well. Because the system cached market data and separated deterministic facts from AI judgment, it reduced API usage and model costs. That balance of usability, safety, and cost control made the platform more sustainable for long‑term growth.

What We Learned

This project confirmed that AI delivers the most value when paired with clear structure and safeguards. Deterministic systems establish the facts, while AI reasoning provides interpretation and flexibility. Keeping those roles separate is what makes the workflow both reliable and explainable.

We also learned that user experience matters just as much as engineering. Borrowers preferred a guided conversation to a dashboard of inputs and metrics. By meeting them in plain language, adoption improved, and so did trust.

Finally, businesses looking to run critical processes with AI should keep three principles in mind:

• Ensure the system is auditable.

• Separate facts from judgment.

• Design for safety at every step.

These are not just technical decisions—they are business decisions that determine whether AI adoption succeeds or stalls.

About StableWorks

StableWorks is an AI consultancy and development partner. We help companies move from ideas to production systems, building solutions that fit existing workflows while keeping safety, cost, and adoption in focus.