Technical Reference | AI Engineering Playbooks

A practical operating guide for teams adopting AI quickly without compromising quality, security, or trust. AI adoption succeeds when teams are explicit about boundaries, not just enthusiastic about tools. Do Define approved use cases and forbidden use cases. Keep a human reviewer for high-impact outputs. Use versioned prompts and templates for repeatable workflows. Capture and review model failures weekly. Validate outputs against source systems before action. Treat AI tooling access as privileged access. Don't Do not let AI-generated output bypass review in regulated workflows. Do not mix sensitive data into prompts without policy controls. Do not assume model confidence equals correctness. Do not ship agentic workflows without observability. Do not optimise for speed at the expense of rollback readiness. Team operating model Product sets problem and success metric. Engineering owns architecture and controls. Security signs off on tool boundaries. ...

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...

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 ...

Layered defences for agents in production, with insurance-relevant examples. Layered defences for agents in production, with insurance-relevant examples. Persistent 2026 concern. Includes controls, pitfalls, and a phased implementation path. Layered defences for agents in production, with insurance-relevant examples. 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. Implem...

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...

A practical pre-release checklist for AI features covering security, misuse risk, transparency, and governance. Shipping AI features without security and ethics checks creates hidden operational risk. Use this checklist before each release. 1) Data and privacy Confirm data minimisation in prompts and context. Remove secrets and personal data from logs. Enforce retention windows for model inputs and outputs. Validate third-party processor boundaries. 2) Security controls Restrict tool permissions by role and environment. Validate all tool outputs against strict schemas. Add prompt-injection defences for external content. Require approval gates for high-impact actions. 3) Safety and misuse Define clear disallowed use cases. Add risk prompts for potentially harmful requests. Add user-visible warnings for uncertain outputs. Add abuse monitoring and escalation paths. 4) Transparency and trust Disclose where AI assistance is used. Explain known limitations...

A practical architecture for building AI agents with MCP, including boundaries, observability, and failure handling. AI agents are moving from demos to production systems, and MCP is quickly becoming a common protocol for tool and context integration. This guide covers a practical baseline architecture. Why this matters now As of 2025-2026, MCP support and agent workflows have expanded across major ecosystems, and teams need interoperable patterns rather than provider lock-in. Baseline architecture Orchestrator layer : plans tasks, manages tool calls, and handles retries. Model layer : reasoning/generation model with explicit prompt contracts. MCP tool layer : context servers for docs, repos, tickets, and internal systems. Policy layer : security rules, redaction, and allowed-tool boundaries. Observability layer : traces, token costs, tool latency, failure telemetry. Key design rules Treat MCP servers as untrusted inputs unless explicitly verified. Whitelist to...