Building AI SRE in Public
A long-form series on building an AI SRE under an approval boundary — intent compiler, PlanResult, safety, multi-context, investigation, and why we refuse unsupervised auto-remediation. Episode index and honesty rules.
kprompt started as an AI Kubernetes CLI: natural language becomes a reviewable plan, then you approve before apply. That wedge still ships today. The longer bet is AI SRE — a system that can investigate, explain why, show blast radius, and verify outcomes without silently mutating production. This series is how we build that bet in public.
We are not writing weekly changelog fluff. Each episode is a durable essay: a design claim, what already exists in the CLI, what is still building or exploring, and what we explicitly refuse. Share it on Hacker News or in a CNCF channel if the idea is useful even if you never install the binary.
Rules of the series
- Shipped / building / exploring — never imply a demo is a product
- Approval boundary stays load-bearing — no silent apply across contexts
- Typed outputs over chat vibes — PlanResult, risk, hard denies
- Prefer non-production while you learn; experimental software
- CLI stays Apache-2.0; no “buy Team to make the series real”
Episodes
| # | Topic | Status |
|---|---|---|
| 1 | Why AI SRE | Published |
| 2 | Intent Compiler | Published |
| 3 | PlanResult | Published |
| 4 | Safety Engine | Published |
| 5 | Multi-context | Next |
| 6 | Investigation Graph | Building / vision |
| 7 | AI Timeline | Building / vision |
| 8 | Cluster Memory | Exploring |
| 9 | Knowledge Graph | Exploring |
| 10 | Autonomous SRE — and why not yet | Planned |
Read episodes in order: Why AI SRE → Intent Compiler → PlanResult → Safety Engine. Earlier positioning posts (PlanResult JSON field guide, vs kubectl-ai, plan-approve) remain companions; this series goes deeper and stays chronological.
Who this is for
- Platform / SRE engineers evaluating AI tools that touch clusters
- Builders designing agentic ops who need fail-closed patterns
- CNCF practitioners who care about GitOps, Prom, OTel — not only chat CLIs
Try the wedge while you read
Safe read on a non-prod context
brew install kprompt/tap/kprompt
# or: curl -fsSL https://kprompt.ai/install | bash
export KPROMPT_GEMINI_API_KEY="..."
kprompt "list deployments"
kprompt "optimize my cluster"Star the repo, open issues when an episode claims something the CLI cannot do yet, and follow the index as later episodes land.
Related posts
Building AI SRE in Public #4: Safety Engine
Policy is code, not LLM vibes. How kprompt’s safety engine hard-denies wipe-class intents, scores risk, forces approval, and why fail-closed is the load-bearing wall of AI SRE.
Read articleBuilding AI SRE in Public #3: PlanResult
PlanResult is the IR of AI SRE: one typed document for humans and CI. Why JSON, what applied means vs verify, how blastRadius attaches, what never gets stored, and how investigate/why must extend the same artifact.
Read articleBuilding AI SRE in Public #2: Intent Compiler
Kubernetes deserves a compiler, not a chatbot. How kprompt turns natural language into Intent → Actions → PlanResult, why Go owns planning and safety, and why the IR must stay reviewable for AI SRE.
Read article