These playbooks focus on the operational discipline that turns agentic AI into something the organisation can rely on. They show ops leaders and teams how to monitor, control, and guide agents so the work drives real productivity gains rather than sliding into technical debt.
Inside this bundle
This bundle is for operations leaders and anyone responsible for bringing AI into day-to-day workflows. It focuses on simple first implementations, the data needed to support them and the governance required to keep agents reliable.
Below is a summary of the playbooks in this series.
- The 6-Week Sprint to a Governed Production Agent: How to apply a rigorous framework to transition a fragile AI prototype into a trustworthy, production-ready agent. The playbook prioritizes governance and observability, ensuring leaders can trace every decision, enforce boundaries, and measure clear KPI improvements before scaling.
- Stop Over-Architecting and Build the ‘One-Job Agent’ First: The mantra to ‘start small’ in midmarket AI calls for an architectural strategy of extreme simplicity, prioritizing the creation of “One-Job Agents” that execute a single, clearly defined task. Leaders can ensure overhead is low, failure is minimized, and that agent behaviour is predictable and observable before any complexity is added.
- The 9-Point Checklist for AI That Survives Real-World Conditions: This playbook provides a 9-point mandatory reliability checklist for hardening AI agents against the unstable realities of production, focusing on minimizing unexpected failures. It guides leaders to proactively control critical factors, ensuring the agent’s behaviour remains predictable and trustworthy even when external systems shift or fail.
- RAG-Ready Data: The Knowledge Base Preparation Checklist: RAG is how a general AI advances to being a tool for your unique business. This playbook establishes the data governance and engineering steps required to build a knowledge base for AI agents using Retrieval-Augmented Generation (RAG).
- The Perpetual Feedback Loop to Guarantee AI Improvement: This playbook outlines the parameters for establishing a feedback loop to capture performance, cost, and user satisfaction data from production agents.
