Technical Reference | AI Engineering Playbooks

A practical list of AI use cases for insurance operations, underwriting support, claims, and service workflows. Insurance teams do not need moonshot AI programmes to create value. They need targeted workflows with clear controls. 10 practical use cases Submission triage and document completeness checks. Broker email summarisation with action extraction. Underwriting note drafting from structured risk inputs. Claims intake classification and routing support. Policy wording comparison assistance. Renewal packet preparation and variance summaries. Internal knowledge retrieval for servicing teams. Meeting preparation briefs for account and placement teams. Escalation risk early-warning summaries. Portfolio-level trend summaries for leadership reviews. Delivery pattern that works Start with one workflow and one business metric. Add human-in-the-loop review for all externally visible outputs. Capture failure cases weekly and retrain prompt/process contracts. Exp...

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...

This site is now focused on practical AI engineering: agents, MCP, frameworks, security, ethics, and product delivery. If you are building with AI and need practical, implementation-first guidance, this site is for you. Technical Reference is now focused on modern AI engineering with one rule: Useful in production beats impressive in demos. What you will find here AI agent architecture and MCP integration guides. Framework comparisons that include trade-offs, not marketing claims. Security and ethics checklists for real delivery contexts. Vibe coding workflows with reliability guardrails. AI + insurance applied patterns. Idea-to-product execution playbooks. How to use this site Start with implementation guides. Use comparison posts to choose a stack. Apply checklists before release. Revisit trend posts as the ecosystem changes. Publishing rhythm Tuesday: technical implementation guide. Friday: strategic comparison, trend, or case-based post. What to re...