Enabling the Agent with Context Engineering

Thinking through the domain requirements

In the rapidly evolving world of AI, context engineering is emerging as a critical discipline, surpassing traditional prompt engineering in its ability to drive precision and reliability in AI agents. At Strategic Machines, where we explore the cutting edge of AI agents and voice technologies, we see context engineering as the backbone of intelligent, enterprise-ready systems. Industry luminaries like Phil Schmid, Tobi Lutke, and Andrej Karpathy underscore its transformative potential.

"Context is the scaffolding that allows AI to reason effectively. Without it, even the best models are just guessing."
— Phil Schmid, AI thought leader

1. Context is King
Like content, context reigns supreme. For AI agents, a comprehensive view of relevant data—whether customer interactions, inventory levels, or operational metrics—ensures precise performance. Context engineering provides the full picture, enabling agents to deliver tailored, accurate outcomes, from answering queries to automating complex workflows.

"The difference between a good and great AI is how well it understands the world it operates in."
— Tobi Lutke, CEO of Shopify

2. Keeping Context Fresh
The engineering challenge lies in maintaining up-to-date context. A stale context leads to irrelevant results, but a fresh one empowers agents to shine. For example, an AI voice agent handling customer inquiries about recent sales or deliveries must access real-time data. Solving this challenge unlocks consistent, reliable performance.

3. Context Enables and Constrains
Context isn’t just about data—it includes the tools and functions available to an agent. By defining what an agent can and cannot do, context engineering ensures predictable outcomes. For instance, limiting an agent to specific functions for booking a room or a set of workflows for authentication reduces errors and enhances trust in the system.

"Context engineering is about giving the model just enough rope to be creative, but not so much it hangs itself."
— Andrej Karpathy, AI pioneer

4. Mapping to the Enterprise Domain
Context mirrors an enterprise’s domain model, aligning AI capabilities with business needs. By mapping context to organizational priorities—such as supply chain or customer service—leaders can strategically plan investments and ensure comprehensive coverage. This alignment drives scalability and long-term value.

Context engineering is more than a technical shift; it’s a strategic imperative. As Karpathy, Schmid, and Lutke highlight, it empowers AI agents to operate with precision, adapt to real-time demands, and align with enterprise goals.

At Strategic Machines, we believe mastering context engineering will define the next generation of AI agents, delivering unparalleled reliability and impact. Give us a call so we can discuss how context engineering will enable your agentic strategies and deployments.