Technical Reference | AI Engineering Playbooks

A practical relaunch plan to modernise Technical Reference into an AI-first engineering publication. Current site review Site is currently on a legacy Blogger layout ( Awesome Inc. theme), with old structure and low scannability. Most recent post is from December 2013. Archive is valuable but outdated for current engineering and AI workflows. Topic fit today should shift from ad hoc tips to systematic, production-grade AI engineering guidance. New positioning Technical Reference becomes: "A practical AI engineering reference for builders: agents, MCP, frameworks, security, ethics, and production operations." Target audience Engineers building AI-enabled products. Technical leads evaluating agentic architecture choices. Teams in regulated environments (including insurance). Makers shipping rapid prototypes and turning ideas into products. Pillars (content architecture) AI Agents and MCP in production. Framework and stack comparisons. AI security...

A practical 14-day blueprint for turning one validated AI use case into a secure, testable micro-product with measurable outcomes. Start with one painful workflow and a measurable business outcome. Keep scope tight: one persona, one trigger, one successful output. Ship with controls for quality, security, and operability from day one. Many AI projects fail because they start broad, not because the technology is weak. A micro-product approach keeps delivery disciplined and outcome-focused. This blueprint is designed for small teams that need to prove value quickly and safely. Prerequisites One clearly owned business problem. Access to subject matter experts. Basic delivery stack (repo, CI, monitoring). A named product and engineering owner. 14-day plan Days 1-2: Define outcome and scope Choose one workflow with repeated manual effort. Define baseline time, error rate, or cycle time. Write acceptance criteria for success. Days 3-4: Design the minimal archite...

A practical framework for building an AI evaluation harness that links test quality to release decisions and operational confidence. Evaluation harnesses turn subjective model quality into measurable release criteria. Combine functional, safety, latency, and cost checks into one pipeline. Block releases when critical thresholds are missed, even under delivery pressure. If your AI release decision is based on a demo, you are not releasing engineering software; you are releasing a hope strategy. A proper evaluation harness creates repeatable evidence for quality, safety, and cost trade-offs. Prerequisites Versioned prompts and model configuration. Representative test dataset by use case. CI/CD pipeline with artefact retention. Clear service-level objectives for latency and reliability. Evaluation layers 1) Functional correctness Golden set response checks. Tool invocation correctness. Schema compliance for structured outputs. 2) Safety and policy Prompt in...

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 incident runbook for AI agent systems, covering common failure modes and response actions that reduce production impact. Most agent incidents are predictable: tool misuse, context drift, and weak guardrails. Build a failure taxonomy and link each class to detection and recovery playbooks. Track MTTR and recurrence to continuously harden your agent platform. Agent systems do not fail in one way. They fail across planning, context, tool invocation, and execution boundaries. Without a clear runbook, teams lose time arguing about symptoms instead of restoring service. This guide provides an operating model you can implement immediately. Prerequisites Incident severity model (SEV1, SEV2, SEV3). On-call owner for agent platform. Baseline observability for prompts, tool calls, and outcomes. Rollback path for model and policy configuration. Failure taxonomy 1) Intent misclassification The agent chooses the wrong plan for a valid request. Signals: - Wrong w...

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...

Tools and patterns for tracing multi-agent decisions, detecting drift, and maintaining reliability in live environments. Tools and patterns for tracing multi-agent decisions, detecting drift, and maintaining reliability in live environments. Essential for scaling agents (LangChain state of agents survey). Includes controls, pitfalls, and a phased implementation path. Tools and patterns for tracing multi-agent decisions, detecting drift, and maintaining reliability in live environments. 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 relea...

How agentic tools accelerate development while enforcing engineering controls, reviews, and security in regulated delivery pipelines. How agentic tools accelerate development while enforcing engineering controls, reviews, and security in regulated delivery pipelines. Anthropic's 2026 trends highlight agentic quality control as standard; pragmatic for delivery leaders. Includes controls, pitfalls, and a phased implementation path. How agentic tools accelerate development while enforcing engineering controls, reviews, and security in regulated delivery pipelines. 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 approac...

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 ...

Compare frontier models (e.g., GPT-5 variants, Claude Opus 4.6, Gemini) on price/performance/reasoning, focusing on agentic and enterprise use cases. Compare frontier models (e.g., GPT-5 variants, Claude Opus 4.6, Gemini) on price/performance/reasoning, focusing on agentic and enterprise use cases. February 2026 benchmarks show split into "God Mode" vs "Flash Mode"; helps engineers choose without wasting budget. Includes controls, pitfalls, and a phased implementation path. Compare frontier models (e.g., GPT-5 variants, Claude Opus 4.6, Gemini) on price/performance/reasoning, focusing on agentic and enterprise use cases. 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 beha...

How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory compliance. How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory compliance. Top 2026 insurance trend (hyper-personalisation, AI agents joining teams); relatable for your Verisk/specialty background. Includes controls, pitfalls, and a phased implementation path. How agentic AI enables hyper-personalised, real-time underwriting in specialty insurance, with trade-offs in data quality and regulatory 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 ...

Explores how AI-assisted coding can accelerate delivery in insurance and financial services — and the guardrails you must have before you ship anything. Explores how AI-assisted coding can accelerate delivery in insurance and financial services — and the guardrails you must have before you ship anything. Your insurance background makes this uniquely credible. Includes controls, pitfalls, and a phased implementation path. Explores how AI-assisted coding can accelerate delivery in insurance and financial services — and the guardrails you must have before you ship anything. 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 a...

Design patterns for agent-assisted claims that amplify human judgment while achieving 40% faster processing in regulated settings. Design patterns for agent-assisted claims that amplify human judgment while achieving 40% faster processing in regulated settings. 2026 insurance predictions stress hyper-automated claims with people-first AI. Includes controls, pitfalls, and a phased implementation path. Design patterns for agent-assisted claims that amplify human judgment while achieving 40% faster processing in regulated settings. 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...

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...

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...

Trade-offs in modularity, security, and scalability for building custom agents. Trade-offs in modularity, security, and scalability for building custom agents. Evolving 2026 building frameworks. Includes controls, pitfalls, and a phased implementation path. Trade-offs in modularity, security, and scalability for building custom agents. 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 rollb...

Architecture modernisation for agent integration, data foundations, and security. Architecture modernisation for agent integration, data foundations, and security. 2026 insurance outlook on foundations for scaling AI. Includes controls, pitfalls, and a phased implementation path. Architecture modernisation for agent integration, data foundations, and security. 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 expl...

Tools and processes to discover/secure unmanaged agents in regulated orgs. Tools and processes to discover/secure unmanaged agents in regulated orgs. 2026 governance priority. Includes controls, pitfalls, and a phased implementation path. Tools and processes to discover/secure unmanaged agents in regulated orgs. 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. Implementat...

Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. Trending heavily in 2026 reports (Google Cloud, IBM); offers clear benefits in scalable orchestration and vendor neutrality for regulated workloads. Includes controls, pitfalls, and a phased implementation path. Explore how MCP enables secure, interoperable multi-agent orchestration across tools and vendors, with real-world architecture patterns to move beyond single-agent prototypes. 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...

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...

Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance compliance. Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance compliance. 2026 focus on agentic guardrails in law and runtime ethics. Includes controls, pitfalls, and a phased implementation path. Implementing runtime controls, fairness checks, and accountability in agent decisions for insurance and finance 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 r...

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 ...

Compare emerging standards for agent collaboration, with architecture diagrams and production considerations for interoperability. Compare emerging standards for agent collaboration, with architecture diagrams and production considerations for interoperability. Google/IBM 2026 trends emphasise multi-agent dashboards and protocols. Includes controls, pitfalls, and a phased implementation path. Compare emerging standards for agent collaboration, with architecture diagrams and production considerations for interoperability. 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 capabil...

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...