---
title: AI Agents
slug: ai-agents
createdAt: 2026-02-24T04:09:11.456Z
---

# 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