About Me
My name is Marc Brooker. I've been writing code, reading code, and living vicariously through computers for as long as I can remember. I like to build things that work. I also dabble in machining, welding, cooking and skiing.I'm currently an engineer at Amazon Web Services (AWS) in Seattle, where I work on databases, serverless, and serverless databases. Before that, I worked on EC2 and EBS.
All opinions are my own.
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My Publications and Videos@marcbrooker on Mastodon @MarcJBrooker on Twitter
Music To Build Agents By
Press play, then start reading:
Want to learn how to think about agent policy? Start with Goethe’s Der Zauberlehrling.
So come along, you old broomstick! Dress yourself in rotten rags! You’ve long been a servant; Obey my orders now!
When I talk to customers and teams around me about agents and agent policy, and the work we’re doing on AgentCore Policy (now GA), I hear a lot of folks worried about adversarial agents, about prompt injection, and about hallucinations. That’s not unreasonable, because all those things exist (and all are areas we’re actively working on). But the most common problem is a more basic one, more Fantasia than James Bond.
AI agents are persistent problem solvers. You ask them to solve a problem, and they’ll go to work solving the problem.
Look, he’s running down to the bank; In truth! He’s already reached the river, And back he comes as quick as lightning And swiftly pours it all out.
That’s exactly what makes agents powerful. If we knew how to solve the problem as a fixed workflow, we probably wouldn’t bother with an agent. Workflows are faster, cheaper, and simpler. We build agents because they’re persistent, because they handle edge cases, because they can adapt to changing circumstances and work around problems.
And this is also why they need policy (and should be in a box).
Alas! speedily he runs and fetches! If only you were a broom as before! He keeps rushing in With more and more water, Alas! a hundred rivers Pour down on my head!
Policy layers like AgentCore Policy allow us to define limits on the agent’s behavior. They allow us to make sure that agents stop when the basin is full, and to avoid pouring water all over the floor. That’s important even if your agent is insulated from adversaries, and if your model is free from hallucinations. In fact, it becomes more and more important as models become more powerful, and able to solve longer-running problems.
Now, jump ahead to here:
Ah, my master comes at last! Sir, I’m in desperate straits! The spirits I summoned - I can’t get rid of them.
Marc Brooker
The opinions on this site are my own. They do not necessarily represent those of my employer.
marcbrooker@gmail.com
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