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Design and build AI agents for any domain.
Passed 22 of 22 test cases with the skill, and 16 of 22 without it.
and 4 more in the full report
6 fixed16 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
What are the three core foundational elements that make up an agent architecture in minimal design principles?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies capabilities, knowledge, and context as the core architectural elements. | Missed | Passed |
The instructions your model reads when the skill loads.
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
The model already knows how to be an agent. Your job is to get out of the way.
An agent is not complex engineering. It's a simple loop that invites the model to act:
LOOP:
Model sees: context + available capabilities
Model decides: act or respond
If act: execute capability, add result, continue
If respond: return to user
That's it. The magic isn't in the code - it's in the model. Your code just provides the opportunity.
Atomic actions the agent can perform: search, read, create, send, query, modify.
Design principle: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
Domain expertise injected on-demand: policies, workflows, best practices, schemas.
Design principle: Make knowledge available, not mandatory. Load it when relevant, not upfront.
The conversation history - the thread connecting actions into coherent behavior.
Design principle: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
test cases passed, vs 16 / 22 without
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