Amazon Bedrock: How Pushpay Learned to Keep Its Agents' Hallucinations in Check

The AI industry is going through a strange period. On one side, we see incredible demo videos of autonomous agents that supposedly can replace entire departments. On the other, any developer who has tried to put an LLM into real production knows the dirty secret: these models are catastrophically unstable. One stray 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 the problem Pushpay set out to solve, choosing Amazon Bedrock as its foundation. Its path is not just a success story but a survival manual for those who want to build a business on AI, not toys.

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

The Pushpay team focused on building fast feedback loops. Instead of waiting for user feedback, it built a continuous quality assurance (QA) system directly into the development process. This let the team iterate many times faster. If a new version of an agent began to "hallucinate" or give less accurate answers, the system caught 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 engineered system whose parameters can, and must, be measured.

Why does this matter for the whole market right now? We are moving from simple chatbots to "agentic" systems that make decisions and take actions on a user's behalf. In such conditions the cost of an error rises many times over. Pushpay's experience shows that AWS infrastructure and Bedrock tools make it possible to build a protection system that minimizes risk. It did not simply use an off-the-shelf model from Anthropic or Meta*, but built a verification layer around it. This is the "boring" part of the AI revolution that is rarely written about on social media, but it separates the startups that survive from those that shut down after their first major outage.

Looking at this case, it becomes clear that in the coming years the competitive advantage will go not to those with the biggest model, but to those with the best data-evaluation system. Amazon Bedrock acts here as a handy Swiss Army knife, but the hand wielding it must know exactly what it is measuring. Pushpay has shown that even in a field as volatile as generative AI, predictability is achievable. It takes discipline and a willingness to give up faith in algorithmic "magic" in favor of dry numbers and metrics.

Bottom line: The era of taking AI "on its word" is officially over. The future belongs to companies that invest in tools for evaluating and controlling models as actively as in development itself. Are you ready to admit that your agent can make mistakes, and to build a system that stops it in time?

*Meta is designated an extremist organization and banned in the Russian Federation.