Thinking & Perspectives
AI Strategy Insights
Practical perspectives on AI strategy, agentic systems architecture, and governance — from the advisory practice at Imagine Works.
Retrieval-Augmented Generation (RAG): What Enterprise Leaders Need to Know
RAG — grounding a language model in your own retrieved documents rather than relying on what it memorised in training — has become the dominant enterprise AI architecture. Menlo Ventures put RAG adoption at 51% of enterprise implementations in 2024, up from 31% a year earlier. Here is what RAG actually is, why it won, where it breaks, and what leaders need to govern.
Model Context Protocol: What Enterprise Leaders Need to Know Before Buying Into Agentic AI
Eighteen months after Anthropic released the Model Context Protocol, it has become the de facto standard for how AI agents connect to enterprise data and tools — adopted by OpenAI, Google DeepMind, Microsoft, and Cloudflare. For enterprise leaders evaluating agentic AI investment in 2026, MCP is no longer a technical curiosity; it is an architecture and procurement decision.
Orchestration Patterns in Agentic AI: Choosing the Right Architecture
Choosing an orchestration pattern is one of the most consequential architectural decisions in agentic system design. It determines how information flows through the system, how errors propagate, how human oversight integrates, and how the system scales. Here is a practical guide to the three core patterns and when to use each.
Multi-Agent Systems: When One Agent Is Not Enough
Single-agent AI architectures have well-defined limits. As enterprise AI ambitions grow to include research synthesis, complex workflow automation, and multi-step operational processes, multi-agent architectures become necessary. Understanding when and how to use them is one of the most consequential architectural decisions in agentic AI today.
Designing Human-in-the-Loop Systems: A Practical Architecture Guide
HITL is one of the most frequently cited and least frequently implemented requirements in agentic AI. Teams describe it as a safety feature. Regulators treat it as a legal requirement. Architects know it as a structural challenge that must be resolved before the system is built. Here is how to design it correctly.
AI Agents vs. Automation: Knowing Which One to Use
AI agents and traditional automation are often treated as competing options — or worse, conflated as the same thing. They are neither. Understanding the difference, and knowing when each is the right tool, is one of the most practical decisions an enterprise technology leader can make right now.