2026-09-18
Four C’s for clarity on complex tasks
Here’s a simple framework I use for improving the performance of your fav agent on (semi-)complex tasks:
I think it’s all mostly obvious how the four C’s improve your prompt, which means I won’t explain each part of the framework. The only subtle part is the control loop. Designing the control loop to be more “independent” is supremely useful; in practice it means the control loop doesn’t know about the other three C’s. I usually use AI agents with a fresh context window for this. But the independent design principle can materialize in all sorts of things: static tests in programming, a screenshot of the website the agent is working on, or, if you want, a human in the loop.
Maybe, just maybe, you can let the control loop know about some of the constraints.
I never go overboard with my prompts, and I think the framework is simple enough to scale well with model intelligence.
You can also call it the “Four C” framework.
If you think you will forget about this anyway, put something like this in the system prompt:
# ask for these c's for medium to big tasks
Medium to big tasks are tasks that are not one off, aren't a simple question or something like filling out a form or parsing a pdf, it's building something new or synthesizing multiple things. Research doesn't fall under this.
## context
the why
## constraints
the how, and the how not
## control (aka, the loop)
the controlling, importantly, the control doesn't know about the context and constraints, usually subagents or more static control like red-green tests.
