Production AI systems are here – built on decades of research and validated in data centers around the world.  Frameworks for training and running machine learning models have matured to the point where they are accessible to any developer. The challenge now is how to bring AI into production in a way that feels natural, responsive, and scalable.

Harper can help. Harper is a distributed application platform that combines database, cache, messaging, and application functions into a single runtime that runs at the edge – close to users. The future will include models everywhere because we want models as close to decisions as possible, so we (or our AI agents, or copilots) can make the best choices possible in the least amount of time. Harper is uniquely capable of pushing AI to the edge.  By pushing models to the edge in Harper, we reduce latency, capture valuable feedback, and integrate machine learning models into applications without the complexity of additional infrastructure.

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Why Edge Deployment Changes the Game

The speed of a system directly shapes how people perceive it. In digital experiences, even a few hundred milliseconds of delay can alter engagement and conversion rates. Think of e-commerce: a shopper considering a purchase doesn’t want to wait for a recommendation engine to query a distant cloud server. They expect results instantly—as they are typing in the search bar.

Inferencing at the edge in Harper minimizes any delay. The model’s predictions or recommendations are delivered in real time, and the interaction is seamless. At the same time, every user action—whether they click on a suggestion, scroll past it, or choose something else—becomes a signal. Harper can capture these signals and feed them back into training pipelines, allowing the models to improve continuously.

This feedback loop ensures that AI agents deployed in Harper are living components that learn and adapt based on real-time usage.

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From Training to Deployment with Harper

Most training will continue to happen in the cloud or data centers, where GPUs and large datasets are available. But once a model is trained, Harper provides immediate value through deployment. Developers can wrap a pre-trained model with a thin layer of code—an API that accepts inputs and returns predictions—and then deploy that model directly into Harper.

Because Harper treats models as part of the runtime environment, the deployment process feels similar to shipping any other application component. An edge inferencing API can be co-located with or without a React frontend, making it simple to integrate high-performance, high-quality AI services. This simplicity eliminates the need for managing separate microservices, load balancers, or specialized serving layers and integrates seamlessly into existing observability, logging, and performance management systems.

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A Practical Starting Point

To make this more tangible, we’ve published an example project on GitHub. It demonstrates the basics of running an edge AI agent in Harper. Setting it up requires only a few straightforward steps: clone the repository, install dependencies, and deploy into a Harper instance. From there, the project shows how pre-trained models can be integrated into the runtime and exposed through an API accessible to multiple tenants.

This example is intentionally lightweight, introducing a fictional e-commerce company, Alpine Gear Company (the sole example tenant), which will be featured in future posts. It provides developers with a clear, working template for hosting AI agents in Harper, without requiring extensive knowledge of machine learning internals. Once the basics are in place, it’s easy to substitute a different pre-trained model or connect the workflow to your own training pipeline.

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Building Toward Continuous Learning

What makes Harper especially powerful is that deployment is not the end of the journey. Every inference and every user action creates a log that can be aggregated and evaluated. If an inference proves successful, it strengthens confidence in the model. If it falls flat, that feedback becomes data for retraining. Harper supports this cycle without interruption: applications continue running while models are retrained offline and then rolled forward into production.

Over time, this creates a virtuous cycle where AI agents grow smarter and more attuned to user needs, while applications remain fast and resilient. The edge location ensures responsiveness, while the Harper platform ensures that learning never stops.

The example shows how to collect inferencing data and trigger retraining when thresholds are exceeded, providing the first steps towards continuously self-updating models.

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Closing Thoughts

AI frameworks are powerful, but their value truly emerges when models are deployed into real-world contexts, where they can interact with users and evolve through feedback. Harper provides a natural home for this work, making it straightforward for developers to deploy, observe, and improve AI agents at the edge.

The example project is a great way to get started. By experimenting with it, developers can see how Harper’s fused stack simplifies deployment and unlocks the full potential of AI-powered applications. What begins with a simple pre-trained model can quickly evolve into a production-ready system that learns from every interaction, delivering both immediate performance and long-term value.

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