Yesterday, I was trying to explain context engineering to someone non-technical. The more I fumbled through explanations about token windows, system prompts, and retrieval mechanisms, the more I realized I was overcomplicating things. Then it hit me: context engineering is essentially just the art of delegation.
Think about it. When you delegate a task to a colleague, you don’t just say “do the thing.” You provide background, set expectations, share relevant documents, and explain how this fits into the bigger picture. The quality of their output directly correlates with the quality of the context you provide.
AI works exactly the same way.
The Delegation Parallel
Every good manager knows that effective delegation requires three things: clarity about the task, access to necessary resources, and appropriate autonomy. Context engineering is just this principle applied to AI systems.
When you’re prompting an AI model or building an agentic system, you’re essentially delegating work. And just like with human teammates, the better you set up that delegation, the better the results.
Here’s what breaks down when delegation fails—whether with humans or AI:
Insufficient context: You ask for a report but don’t mention it’s for the CFO who cares deeply about quarterly trends. You get a generic summary instead of focused financial analysis.
Too much noise: You dump every document you have into the conversation, hoping the AI will figure out what’s relevant. It drowns in information and loses the thread.
Unclear expectations: You say “make it better” without explaining what “better” means in this context. You get random changes that miss your actual intent.
What Good Delegation Looks Like
When I delegate to a team member on a complex task, I usually cover:
- The goal: What are we actually trying to achieve?
- The constraints: What’s the timeline, budget, or format?
- The background: What happened before that led us here?
- The resources: Where can they find what they need?
- The autonomy level: Should they run decisions by me or just execute?
Context engineering is structuring the same information for an AI. System prompts handle the role and constraints. Retrieved documents provide background and resources. The prompt itself clarifies the immediate goal. And your tool configuration determines how much autonomy the AI has.
The Trust Factor
Here’s where the analogy gets interesting. With human delegation, there’s a trust calibration that happens over time. You start with smaller tasks, see how they go, and gradually expand scope as trust builds.
We’re doing the same thing with AI right now. Early adopters started with simple prompts—basic questions, straightforward text generation. As we got better at context engineering, we started giving AI more complex, multi-step tasks. Now we’re building agentic systems that can operate with significant autonomy.
But just like with humans, giving too much autonomy too fast leads to problems. You wouldn’t hand a new hire the keys to the production database on day one. Similarly, building AI systems that can take irreversible actions without guardrails is asking for trouble.
Practical Implications
This framing helps demystify context engineering for people who’ve never touched a prompt. If you’ve ever been a manager, you already have transferable skills. Ask yourself:
- Would a smart intern understand what I’m asking with just this information?
- Have I given access to the right reference materials?
- Am I being specific about what “done” looks like?
- Have I explained where this fits in the bigger picture?
If you can answer yes to these, you’re probably doing context engineering right—even if you never use the term.
The Limits of the Analogy
To be fair, the comparison isn’t perfect. AI doesn’t retain information across sessions like humans do (yet). It doesn’t have the life experience to fill in gaps with reasonable assumptions. And it won’t push back when your request doesn’t make sense—it’ll just do something weird.
But these limitations actually reinforce why context engineering matters. Because AI lacks that human background knowledge, you need to be more explicit and thorough in your setup. Every piece of context you provide is doing work that a human colleague might do automatically.
Looking Forward
As AI systems get more capable, context engineering will only become more important. We’re moving from simple prompt-response interactions to complex agentic systems that can plan, execute, and iterate. That’s a big expansion of the delegation scope.
The organizations that will thrive aren’t necessarily the ones with the most sophisticated AI. They’re the ones that get good at delegation—at structuring context so AI can actually do useful work.
And honestly, that’s reassuring. It means the skills we’ve been developing for decades around management, communication, and collaboration aren’t obsolete. They’re just being applied to a new kind of teammate.
Context engineering isn’t some arcane technical discipline. It’s delegation. And if you’ve ever successfully gotten someone else to do something useful, you’re already on your way.