Skip to main content

Technical Reference Relaunch Plan (2026): AI, Agents, MCP, and Applied Engineering

A practical relaunch plan to modernise Technical Reference into an AI-first engineering publication.

Technical Reference relaunch cover

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  • Archive is valuable but outdated for current engineering and AI workflows.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

  1. Engineers building AI-enabled products.
  2. Technical leads evaluating agentic architecture choices.
  3. Teams in regulated environments (including insurance).
  4. Makers shipping rapid prototypes and turning ideas into products.

Pillars (content architecture)

  1. AI Agents and MCP in production.
  2. Framework and stack comparisons.
  3. AI security and AI ethics for engineering teams.
  4. Vibe coding with guardrails.
  5. AI in insurance and regulated sectors.
  6. Productisation: from idea to deployable app.
  7. Trend intelligence and feature interpretation.

Label taxonomy to enforce

  • ai-agents
  • mcp
  • frameworks
  • ai-security
  • ai-ethics
  • vibe-coding
  • model-comparison
  • ai-trends
  • ai-insurance
  • idea-to-product

Publishing cadence (first 12 weeks)

  • 2 posts per week.
  • Tuesday: deep technical/how-to post.
  • Friday: strategic comparison, trend, or applied case post.

4-phase execution plan

Phase 1 (Week 1-2): Foundation

  1. Publish Start Here and Editorial Standards posts.
  2. Update theme and navigation.
  3. Define labels and archive strategy.
  4. Publish first 4 cornerstone posts.

Phase 2 (Week 3-6): Authority build

  1. Publish practical agent and MCP implementation guides.
  2. Add comparison frameworks and evaluation templates.
  3. Introduce security/ethics checklists.
  4. Add internal linking between every new post.

Phase 3 (Week 7-10): Differentiation

  1. Launch AI + Insurance series.
  2. Launch Idea -> Product series.
  3. Publish real implementation retrospectives.
  4. Add downloadable checklists.

Phase 4 (Week 11-12): Optimisation

  1. Review top posts by traffic and retention.
  2. Refresh winners with diagrams and templates.
  3. Expand into monthly trend brief format.
  4. Publish quarterly synthesis post.

Success metrics (first 90 days)

  1. 24 new posts published.
  2. 3 cornerstone series launched.
  3. 10 legacy posts refreshed or archived.
  4. 30%+ increase in organic sessions.
  5. Consistent returning readership on weekly cadence.

Current topic shortlist to start writing now

  1. AI Agents and MCP: architecture, protocol, and failure modes.
  2. Frameworks: LangGraph vs Semantic Kernel vs AutoGen style patterns.
  3. AI ethics and security in delivery pipelines.
  4. Vibe coding workflow with quality gates.
  5. Model comparison playbooks for real use cases.
  6. AI in insurance operations and underwriting support.
  7. From idea to production: minimal reliable AI app path.

If this was useful, consider bookmarking this post or sharing it with someone who'd benefit.

Labels:

Comments

Post a Comment

Popular posts from this blog

AI Evaluation Harness: From Prompt Tests to Production Release Gates

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...
Post a Comment
Read more

AI Agents and MCP in Production: A Practical Architecture Pattern

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...
Post a Comment
Read more

AI Agent Failure Modes: Detection, Triage, and Recovery Runbook

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...
Post a Comment
Read more