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 structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. A structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. Security posts with threat models and actionable mitigations rank highly and are widely shared by security and engineering audiences. Includes controls, pitfalls, and a phased implementation path. A structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. 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 ...

A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. This is a topic most engineering blogs avoid because it's hard. Includes controls, pitfalls, and a phased implementation path. A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. 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...

A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. Engineers are picking frameworks based on hype, not fit. Includes controls, pitfalls, and a phased implementation path. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. 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 ...

Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. 2026 production momentum shows 57%+ have agents live; addresses common failure modes with pragmatic, measurable controls. Includes controls, pitfalls, and a phased implementation path. Practical guide to layering guardrails (accuracy-first then risk-based) and using evals to cut hallucinations and ensure reliability in live agent deployments. 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 mod...