BackStage

Framework for Engineering Build-vs-Buy Decisions

This article presents a practical framework for engineering leaders to decide whether to build or buy software capabilities. It reframes the binary "build vs buy" question into a multi-option decision space (build, buy SaaS, buy+customise, open-source+host, partner/outsource) and proposes a four-question test: (1) Is this core to your product? (2) Does a mature market solution exist? (3) What is the true total cost of ownership? (4) What is the blast radius of getting it wrong? The author recommends a default-to-buy policy if the four questions don't resolve a decision within two weeks, and offers a simple 3-year cost model (multiply vendor quote by 1.4; multiply build estimate by 2.5). The piece includes a decision matrix, domain-specific guidance (CI/CD, observability, AI/ML, security), real anonymized case studies, and an annual review template for revisiting decisions.

Agentic AI Workflows for Platform Engineering

This is Part 1 of a technical series explaining how to build agentic AI workflows for platform engineering teams. The author argues that improving developer velocity requires encoding team standards into the workspace (not just better prompts): steering files, skills, and agent definitions that provide persistent, role-specific context for AI agents. The post outlines a layered workspace model (e.g., a .kiro/ directory) that injects non-negotiable rules into every AI interaction, describes specialised agents for tasks like infrastructure authoring and security review, and details tool integrations (ticket trackers, CI/CD, AWS). The assumed stack includes AWS (multi-account), Terraform, GitLab CI, and AWS Secrets Manager. The article provides immediate starter steps (create a steering file and AGENTS.md) and previews later parts covering detailed steering files and GitOps/Kubernetes tooling.

Naftiko Framework Alpha 1 Released for Spec‑Driven AI Integration

Naftiko published Alpha 1 of the Naftiko Framework: an Apache‑2.0, Java‑based, declarative runtime that treats existing APIs and data as governed, discoverable "capabilities" for agentic AI. Capabilities are YAML specs that declare consumed upstream APIs and exposed surfaces (MCP, Skill, REST); the Naftiko Engine executes these specs at runtime as live MCP servers without code generation. Alpha 1 focuses on right‑sized AI context (typed outputs, Skills, Resources, Prompts), API reusability (shared HTTP adapters, auth, composed capabilities), and developer experience (Maven/Docker artifacts, Javadocs, Spectral ruleset, GitHub Actions, VS Code extension, Backstage templates). A free Naftiko Fleet Community Edition ships alongside the framework. Roadmap targets Alpha 2 in May 2026, Beta in June 2026, and GA v1.0 in September 2026.