Retrieval & Context
How to ground infrastructure agents in live cluster state and operational knowledge rather than stale training data.
4. Retrieval & Context (RAG) for Infrastructure
Learn how to ground infrastructure agents in current, specific state rather than stale training data by building retrieval pipelines over cluster state, Meshery data, and operational knowledge bases.
20 minutes read
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An agent that can only reason over its training data is an agent that will confidently describe a cluster that no longer exists. The training cutoff is months or years in the past; your cluster changes daily. Retrieval-augmented generation (RAG) is the bridge between a model’s frozen weights and the live state of your infrastructure.
This course teaches you to think about context as a resource to be managed. You will learn why context windows are finite and why that constraint shapes every design decision in an agent-powered ops workflow. You will see exactly which sources of truth - kubectl, mesheryctl, MeshSync, and Meshery designs - contain the state an agent needs, and how to surface that state at query time rather than baking it into a prompt upfront.
The second half of the course turns to operationalizing retrieval: building a knowledge base from runbooks, design files, and documentation; keeping it fresh as your environment evolves; and - critically - evaluating whether the agent actually does the right thing before you trust it with a production change.
By the end you will have a concrete, end-to-end mental model for building agents that are grounded in the present reality of your infrastructure rather than a probabilistic average of the internet from a year ago.