AI Agents

overview (agents are “goal oriented workflows”) kubegrade ai agents are goal oriented operational workflows designed to help teams complete specific kubernetes tasks with strong context and human control built in agents assistant agent purpose serve as an intelligent high level cluster assistant for users provide explanations of cluster metrics, components, and operational states answer technical questions related to configurations, upgrades, and troubleshooting processes assist users by contextualizing issues, linking relevant documentation, and suggesting next actions support other agents’ workflows by offering guidance and clarification during multi agent operations troubleshooting agent purpose investigate symptoms across multiple signal layers identify probable causes suggest and optionally prepare remediations upgrade agent purpose analyze target version readiness run preflight checks propose upgrade sequencing and remediation steps create executable and specific upgrade plans generate data necessary to generate prs for required config changes pr generation agent purpose convert approved remediation plans into repo specific pull requests preserve format/structure conventions attach rationale and provenance metadata custom agents custom agents let teams define workflows that combine kubegrade context with external tools via mcp servers typical custom agent inputs cluster metadata and object state dependency graph context iac source context (terraform/helm/kustomize) git/gitops state external tools via connectors/mcps (e g , terraform/argocd/etc ) example custom use cases policy enforcement workflows scheduled drift checks + pr creation environment readiness checks before releases cost/risk posture reviews across fleet prompting patterns + guardrails recommended prompting patterns goal + scope + constraints (cluster/env/namespace) desired action mode (analyze only / propose / pr ready) risk tolerance and change restrictions output format requirements (summary, checklist, pr body) guardrails read only by default for new workflows policy checks before proposing or executing explicit approval for pr generation/execution scope limits (namespace/workspace/project) logging/audit of agent actions and outputs approval + execution controls kubegrade keeps ai assisted workflows under operator control controls to document approval requirements by severity/environment who can run agents vs who can approve results suggest only mode vs pr generation mode execution via gitops only (recommended default for prod) time window restrictions feedback loop kubegrade can improve agent usefulness over time through operator feedback feedback signals (recommended) accepted vs rejected suggestions edits made before pr generation rollback frequency time to resolution after applying recommendation “incorrect root cause” or “missing context” tags why it matters better remediation quality less noisy suggestions more consistent outputs across teams