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...

Side‑by‑side look at emerging agent orchestration frameworks. Side‑by‑side look at emerging agent orchestration frameworks. Offers clarity for technical decision‑making and integration plans. Includes controls, pitfalls, and a phased implementation path. Side‑by‑side look at emerging agent orchestration frameworks. 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 and security acceptance criteria. Practical approach Define the business decision this capability supports. Limit the first release scope to one workflow and one owner. Add measurable controls for quality, latency, and failure handling. Roll out with explicit monitoring and rollback paths. Implemen...

How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. Agent frameworks like OpenAI’s Evals, CrewAI, and LangGraph are changing the baseline for production AI — engineers need clarity on trade‑offs. Includes controls, pitfalls, and a phased implementation path. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. 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 and security acceptance criteri...