A few weeks ago, I completed Anthropic Education's AI Frameworks and Fluency course.
I joined out of curiosity.
One part of the course stayed with me because it described something I had already been doing while building Artificial Intelligence agents.
The framework is simple.
At first glance, those four words look like another acronym to remember.
After thinking about them, I realized they describe most of the decisions that separate a useful Artificial Intelligence agent from one that constantly needs human intervention.
Not every task should be handed to a language model.
Some decisions belong in code.
Some belong in business rules.
Some are well suited for an Artificial Intelligence model.
A large part of building reliable agents is deciding where those boundaries should be.
The quality of an agent depends on the quality of the information it receives.
Good prompts are only part of the picture.
Context, examples, constraints, user preferences, available tools, and structured data all influence the final result.
An agent cannot produce information it was never given.
This is probably the part I spend the most time thinking about.
Just because an Artificial Intelligence model produces an answer does not mean the answer should be accepted.
Can it verify the information?
Does the response match the user's intent?
Should another tool validate the result before it reaches the user?
Those questions matter more than finding the perfect prompt.
Reliable systems assume that things will go wrong.
Retries.
Validation.
Fallbacks.
Human review.
Rate limiting.
Logging.
Those details are usually invisible to users, but they are often the reason an agent works consistently.
The course did not completely change how I build.
Instead, it gave me better language for explaining the decisions I already make while building Artificial Intelligence systems.
When I build agents for clients, my goal is not simply to connect a model to an automation platform.
My goal is to build systems that know when to act, when to ask for more information, and when to hand control back to a person.
Those decisions have a bigger impact on the final product than the choice of model alone.
I am curious.
If you build with Artificial Intelligence, what principles guide the way you design your systems?
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