# Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform

DevFeed: [Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform](<https://devfeed.tech/articles/building-a-robust-documentation-agent-with-digitalocean-gradient-ai-platform-19874.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/documentation-agent>)

Author: Anna Lushnikova

Published: 2026-04-13T16:59:45Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Documentation](<https://devfeed.tech/topics/documentation.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [demo](<https://devfeed.tech/tags/demo.md>), [documentation](<https://devfeed.tech/tags/documentation.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [llm](<https://devfeed.tech/tags/llm.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [rag](<https://devfeed.tech/tags/rag.md>)

## AI overview

DigitalOcean describes how it built and shipped a production documentation agent on Gradient AI Platform. The article covers the agent architecture, knowledge-base and retrieval setup, prompt engineering, golden datasets, LLM-judged evaluations, and CI/CD-gated prompt iteration used to improve and monitor answer quality.

## Source excerpt

At DigitalOcean, documentation has always been a priority. Developers come to our docs to get unstuck, and the faster they find what they need, the better. Traditional docs pages work, but they require users to know which page to visit, scan for the relevant section, and map generic instructions to their specific setup. That process takes minutes (or longer) when it could take seconds. So we built an AI documentation assistant. Ask a question in plain language, get an answer with working links and ready-to-use commands. Simple enough as a demo. Getting it production-ready was a different story. It took us several iterations to reach an agent we were confident enough to ship. The LLM could generate plausible-sounding answers from day one. Knowing whether those answers were grounded, and keeping them grounded after every model update and prompt change -- that's where we spent most of our time. This post covers what we built, how we validated it, and the specific decisions that moved our metrics from "not great" to "ready for launch." We'll walk through prompt engineering, evaluation pipelines, and the CI/CD glue that holds it all together. Throughout, we relied on DigitalOcean's Agentic Inference Cloud so we could focus on product behavior instead of stitching together inference, RAG, and evaluation tooling ourselves -- one place to run agents, attach knowledge bases, and measure quality, with scale when we need it, and straightforward operational patterns. Architecture Gradient™ AI Platform is the control plane and runtime for standing up production AI agents without assembling pieces by hand. In one place you can attach a knowledge base, define an agent, and tune how it behaves: pick an LLM (managed or open-source), set temperature and top P, choose retrieval behavior, and edit the system prompt. The goal is to get from an empty project to a working agent in minutes, not to wire up inference, RAG, and evaluation from scratch. A Gradient AI Agent is the concrete thing