Technical Reference | AI Engineering Playbooks

How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. Most content on multi-agent systems focuses on possibilities. Includes controls, pitfalls, and a phased implementation path. How to design multi-agent pipelines with clear orchestration, fallback logic, and accountability — without ending up with a distributed system that no one can debug. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality an...

A practical workflow for using AI-first coding speed while preserving quality, security, and maintainability. Vibe coding is useful for speed, but speed without controls creates technical debt quickly. This workflow keeps velocity while protecting reliability. The 5-step workflow Intent definition : write a one-paragraph spec before prompting. AI generation : generate initial implementation in small modules. Human review : validate architecture, naming, and boundary decisions. Automated checks : lint, tests, type checks, and security scan. Operational check : logging, error paths, and rollback readiness. Non-negotiable guardrails Never merge AI-generated code without human review. Always require tests for changed behaviour. Always check secrets and auth flows manually. Always capture design rationale for non-obvious choices. Where vibe coding works best Prototypes and internal tools. Boilerplate and repetitive integration code. Test scaffolding and docs g...