The Agent Bible, First Act: Context Engineering

The Agent Bible, Part 1

Nothing in AI is hotter today than OpenClaw-esque agents, these AIs that seem to run forever, know everything about you, and evolve with you.

I ignored them for a while, until I couldn’t. We talked about them here in healthy detail. But over the last week or so, I’ve been dabbling in agents more seriously, stretching them to their limits, trying to find what is real and what is not.

And I now have the answer to give you. And I have to say it’s actually pretty cool. Thus, today, I’m going to explain how I use AIs, including answers to the following key elements in agentic AI:

How to optimize model selection (hint, it’s not Opus 4.6),

How to understand context engineering, key context architecture and management ideas, and overall best practices for anyone wanting to get serious with agents. I will even explain how I structure prompts, with some examples you can use for yourself.

Why I fear (or rather, know) we are soon going to see price hikes, and why you should be preparing for them,

Tips, tricks, and recommendations I use to avoid the ugly side of these things (including a prompting guide for your guidance), including both my takes and, perhaps more interestingly, leaked lessons from a top closed AI Lab.

If context engineering is the most important skill you can learn in AI currently, by the end of this post, you’re going to be an expert yourself.

Let’s dive in.

Agents in First Principles

I’ve talked about agents more than enough, so I’ll cut to the chase here. Luckily for you, if you haven’t read my previous explanations, understanding what agents are isn't that hard.

Agents are AI models that execute actions. And, crucially, the quality of the action is a function of two things:

The quality of the context we provide the model with. If the agent doesn’t have the correct context, it can’t execute well.

The quality of the model’s knowledge and “intelligence”. If the agent doesn’t know what it doesn’t know, or isn’t smart enough to suggest good actions, the outcome won’t be good. As I always say, you can’t teach a dog to read, no matter how hard you or they try.

Handling the latter depends on choosing the right model for the task. You can, of course, choose to always use the smartest model possible, but as we’re going to see, AIs are not precisely cheap, so you’re going to bankrupt yourself eventually if that’s your approach. The harsh reality is that you need to learn to adjust model choice to the task complexity. We’ll tackle this in more detail below.

The former is much more under your control. As I always tell my clients, the only question you need to ask yourself whenever you interact with an agent is: Am I providing the model with the right context?

Besides having the right context at the right time, the other thing you must consider…

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