Technical Reference | AI Engineering Playbooks

A practical control checklist for securing MCP servers across identity, tool boundaries, data handling, and auditability. Treat MCP servers as privileged integration surfaces, not simple helper services. Enforce identity, scoped permissions, input validation, and full audit trails. Use a release gate that blocks deployment until critical controls are verified. MCP can accelerate agent integration, but it also expands your attack surface. If your server can read internal documents, call business APIs, or trigger workflows, it is effectively a privileged control plane. This checklist is designed for engineering teams that need to move quickly without creating avoidable security debt. Prerequisites A clear inventory of MCP tools and connected systems. A named owner for security decisions. Basic logging and metrics in place. Environment separation for development, test, and production. 12 production controls 1) Explicit trust boundary Document what the MCP server m...

Define autonomy levels with stakeholder alignment and audit trails. Define autonomy levels with stakeholder alignment and audit trails. 2026 ethics trends on thresholds. Includes controls, pitfalls, and a phased implementation path. Define autonomy levels with stakeholder alignment and audit trails. 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. Implementation checklist...

Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. Emerging 2026 trend in agent foundations. Includes controls, pitfalls, and a phased implementation path. Build unambiguous data models as foundations for reliable agentic AI, with examples from regulated domains. 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 contro...

A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. Your stakeholder alignment and regulated-environment experience makes this a natural and credible topic. Includes controls, pitfalls, and a phased implementation path. A decision framework for when AI agents should act autonomously, when they should seek confirmation, and how to design escalation paths that work under operational pressure. 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. Basel...

Roadmap to scale agents for autonomous tasks while maintaining compliance. Roadmap to scale agents for autonomous tasks while maintaining compliance. Deloitte/Dataiku 2026 outlooks. Includes controls, pitfalls, and a phased implementation path. Roadmap to scale agents for autonomous tasks while maintaining compliance. 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. Imple...