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VOOZH | about |
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VOOZH | about |
AI Technical Writer
Every AI agent eventually runs into the same structural problem: the model can reason, but it cannot act without tools. Someone has to run those tools, for example, fetch the search results, query the database, call the API, and that someone is usually your code.
Most teams build this the same way. The model returns a tool call. Your code catches it, runs the tool, formats the result, and sends it back. Repeat until the model has what it needs to answer. The loop works, but it means your team owns the full tool layer: connections, credentials, retry logic, error handling, and observability. None of that is your product — it’s just the behind-the-scenes infrastructure that is working.
There is an alternative: move tool execution into the inference layer itself, so the tools run as part of the API call rather than between API calls.
DigitalOcean’s Server-Side Tools for Inference Engine does this. This article explains what that shift actually changes, the architecture, the latency profile, and the cases where it does not make sense, so you can decide whether it fits your use case.
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With a strong background in data science and over six years of experience, I am passionate about creating in-depth content on technologies. Currently focused on AI, machine learning, and GPU computing, working on topics ranging from deep learning frameworks to optimizing GPU-based workloads.
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