🧠 Treat Your AI Agents Like Employees—Because They Kinda Are

Today’s experiment: What if your AI agent walked into your office like it was day one on the job? Would you be proud of the onboarding experience you’ve created… or quietly panicking as it stares blankly at the wall? Thank you LangChain for the article that got me thinking!

The Big Idea: Context Engineering = Good Management

Let’s get something straight: most AI failures aren’t model problems. They’re management problems.

That little agent you just spun up? It didn’t ghost your task because the LLM is broken. It struggled because you gave it a vague job title, no tools, and sent it into a digital desert with nothing but a smile and a malformed JSON blob.

So here’s the reframe: If you wouldn’t treat a new hire that way, why do it to your AI?

Let’s break this down like a Day 1 checklist.

Do They Know What They’re Supposed to Do? Clear roles. Clear goals. Clean instructions. If you’re still relying on clever prompt phrasing, you’re playing checkers in a chess match. Instructions should be intentional, structured, and easy to reference. Think job description + operating manual.

Do They Have Access to the Right Tools and Info? Agents can’t “just figure it out.” If they need a doc, a database, or a function call—they need access. That’s context. And if you want them to operate at the speed of trust, it better be reliable, retrievable, and in the right format.

Can They Flag When They’re Stuck? Humans escalate. Agents… hallucinate. Unless you build in feedback loops, checkpoints, or summaries, your agent will confidently give you the wrong answer. Design for confusion. Build support into the system, not just the surface.

Are Their Instructions Clear, Consistent, and Accessible? If your agent needs to scroll through nested YAML or interpret conflicting examples like it’s reading Ulysses, you’ve already failed. Make your system messages as crisp as your user training. Think less “prompt hacking,” more AI enablement.

The Future Has Employees You Don’t Interview That’s the shift. We’re not just deploying AI. We’re hiring it. At scale. Across departments. And just like with people, the difference between “meh” and “wow” isn’t the résumé—it’s the onboarding, enablement, and environment.

Context engineering isn’t a technical extra. It’s the new frontline of performance management. So next time your agent fumbles? Before you tweak the temperature or retrain the model, ask: Would I set a human up like this and expect them to succeed?If the answer’s no, don’t blame the bot.

Got your own AI onboarding playbook? Working on context systems inside your org? Let’s compare notes—hit me up.