Technical Reference | AI Engineering Playbooks

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...