Architecture Playbooks
Practical guides and reference architectures for designing complex systems. These playbooks provide step-by-step approaches, design decisions, and trade-offs to help architects and leaders build resilient, scalable, and maintainable solutions.
Most playbooks below are available now; a few, marked Planned, are still in progress, covering the full stack from data readiness through agent platforms, infrastructure, governance, and AI-native software delivery.
The data and retrieval substrate underneath every AI system.
Enterprise RAG Playbook
Maturity model, readiness checklists, and governance for running a RAG initiative end to end.
Data & Knowledge Platform Readiness Playbook
The enterprise data substrate under every AI initiative — data mesh, quality, and knowledge graphs.
How autonomous agents are built, coordinate, and remember.
AI Agent Platform Playbook
The runtime and infrastructure layer for building and operating agents — tool calling, sandboxing, deployment.
Multi-Agent Architecture Playbook
Orchestration patterns for agents working together — supervisor-worker, swarm, and blackboard topologies.
AI Memory Architecture Playbook
How agents track evolving state across a session and beyond — distinct from RAG's static knowledge retrieval.
Enterprise MCP Playbook
When to standardize on Model Context Protocol versus building custom tool integrations.
Agent-to-Agent (A2A) Playbook
Interoperability decisions for agents that need to communicate across vendors and platforms.
Human-in-the-loop Playbook
Approval workflows, escalation paths, and audit trails for AI systems that need a human checkpoint.
The infrastructure that runs, monitors, and evaluates AI in production.
AI Gateway Playbook
A single control point for routing, rate limiting, and cost control across model providers.
LLMOps Playbook
The operational lifecycle for LLM systems — deployment, versioning, CI/CD, and model registry.
AI Observability Playbook
Production monitoring, tracing, and alerting for AI systems in the wild.
AI Evaluation Framework Playbook
A cross-cutting framework for measuring AI quality — benchmarks, golden datasets, and human eval.
The controls that make AI systems safe to run at enterprise scale.
AI Security Playbook
The attack surface and infrastructure controls unique to AI — injection, data leakage, access control.
AI Guardrails Playbook
The runtime policy-enforcement layer — content filtering, jailbreak defense, output validation.
AI Governance Playbook
Organizational policy, audit, and compliance — the org-level counterpart to Security and Guardrails.
The leadership-level decisions: cost, vendor strategy, and organizational design.
AI Cost & FinOps Playbook
Unit economics, inference cost optimization, and chargeback models for AI at scale.
AI Vendor & Model Selection Strategy Playbook
Foundation model and platform selection — and how to avoid orchestration-layer lock-in.
Enterprise AI Platform Reference Architecture Playbook
A synthesis reference architecture tying gateway, agent platform, RAG, and LLMOps into one platform strategy.
AI Talent & Operating Model Playbook
Team topology and org design for AI initiatives — platform team vs. embedded, and the roles you need.
How the SDLC itself changes when AI writes, reviews, and ships code alongside your engineers.
AI-Assisted Development Playbook
Where AI coding assistants and agents actually pay off — task selection, guardrails, and how to measure productivity gains that survive contact with production.
Agentic Software Engineering Governance Playbook
Guardrails for autonomous coding agents — scoped autonomy, mandatory checkpoints, and audit trails for AI-authored code.
AI Code Review & Quality Gates Playbook
Where AI strengthens code review, and where it becomes a rubber stamp — quality gates for an AI-augmented review pipeline.
AI-Native CI/CD & Testing Playbook
Test generation, flaky test triage, and pipeline optimization — the CI/CD lifecycle redesigned around AI agents.
Developer Platform Strategy for the AI Era
Context feeding, agent orchestration, and IDE integration — the internal platform decisions behind AI-native engineering at scale.