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 Archive is valuable but outdated for current engineering and AI workflows.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 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...

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...

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...

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...

How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. Agent frameworks like OpenAI’s Evals, CrewAI, and LangGraph are changing the baseline for production AI — engineers need clarity on trade‑offs. Includes controls, pitfalls, and a phased implementation path. How current agent architectures (tool use, multi-step reasoning, memory) are evolving into deployable systems rather than demos. 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 criteri...

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...