Amazon Bedrock: How Pushpay Learned to Control AI Agent Hallucinations

Amazon Bedrock: How Pushpay Learned to Control the Hallucinations of Their Agents

The AI industry is going through a strange period. On one hand, we see incredible demo videos of autonomous agents that supposedly can replace entire departments. On the other hand, any developer who has tried to implement an LLM in real production knows the dirty secret: these models are catastrophically unstable. A single extra space in a prompt or a model version update on the provider's side can turn a working product into a generator of random nonsense. This is precisely the problem that Pushpay tried to solve by choosing Amazon Bedrock as their foundation. Their journey is not just a success story, but a survival guide for those who want to build real business on AI, not toys.

The problem with most modern AI projects is the lack of a sensible evaluation system. Developers often rely on the so-called "vibe check" — when they manually review five to ten model responses and, if they look decent, ship the code to production. But when your product handles thousands of transactions or interacts with real customers, this approach becomes a dangerous gamble. Pushpay realized this early on and decided they needed an automated pipeline that would check the quality of generation as strictly as regular code is checked with unit tests. Using Amazon Bedrock gave them access to different models through a single API, but the real magic lay in creating a custom evaluation framework.

The Pushpay team focused on creating fast feedback loops. Instead of waiting for user feedback, they implemented a system of continuous quality control (QA) directly into the development process. This allowed them to iterate much faster. If a new agent version started to "hallucinate" or produce less accurate answers, the system detected it instantly. This approach changes the very paradigm of development: you stop treating AI as a magical black box and start working with it as an engineering system whose parameters can and should be measured.

Why does this matter for the entire market right now? We are transitioning from simple chatbots to "agentic" systems that make decisions and take actions on behalf of the user. Under such conditions, the cost of an error increases many times over. Pushpay's experience shows that AWS infrastructure and Bedrock tools make it possible to build a protection system that minimizes risks. They didn't just use a ready-made model from Anthropic or Meta, but created a verification layer around it. This is the "boring" part of the AI revolution, rarely written about on social media, but which separates surviving startups from those that will shut down after the first major failure.

Analyzing this case, it becomes clear that the competitive advantage in the coming years will go not to those with the largest model, but to those with the best data evaluation system. Amazon Bedrock serves here as a convenient Swiss Army knife, but the hand wielding it must precisely know what it is measuring. Pushpay proved that even in such a volatile field as generative AI, predictability can be achieved. This requires discipline and a rejection of faith in the "magic" of algorithms in favor of dry numbers and metrics.

Main point: The era of trusting AI "at face value" is officially over. The future belongs to companies that invest in evaluation and model control tools as actively as in development itself. Are you ready to admit that your agent can make mistakes and build a system that will stop it in time?