fix(website): correct grammar in chat.mdx harness section

[Copilot]

Two grammatical errors in the LLM function calling harness description paragraph.

• "The answer is not" → "The answer is no"
• "if correct it" → "if you correct it"

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[Copilot]

Pull request overview

Fixes minor grammar in the LLM function-calling harness documentation section to improve readability and clarity in the website docs.

Changes:

• Replace “The answer is not” with “The answer is no”.
• Replace “if correct it” with “if you correct it”.

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[Copilot]

This sentence still has a couple of grammatical issues: “vendors … take a lot of type level mistakes” reads incorrectly (typically “make many type-level mistakes”), and “type-level” should be hyphenated.

Suggested change

	The answer is no, and LLM (Large Language Model) vendors like OpenAI take a lot of type level mistakes when composing the arguments of the target function to call. Even though an LLM function calling schema has defined an `Array<string>` type, LLM often fills it just by a `string` typed value. This is where the **function calling harness** comes in — a deterministic correction loop of schema generation, lenient parsing, type coercion, and validation feedback that turns unreliable LLM output into 100% correct structured data.
	Therefore, when developing an LLM function calling agent, the validation feedback process is essentially required. If LLM takes a type level mistake on arguments composition, the agent must feedback the most detailed validation errors, and let the LLM to retry the function calling referencing the validation errors.
	The answer is no, and LLM (Large Language Model) vendors like OpenAI make many type-level mistakes when composing the arguments of the target function to call. Even though an LLM function calling schema has defined an `Array<string>` type, LLM often fills it just by a `string` typed value. This is where the **function calling harness** comes in — a deterministic correction loop of schema generation, lenient parsing, type coercion, and validation feedback that turns unreliable LLM output into 100% correct structured data.
	Therefore, when developing an LLM function calling agent, the validation feedback process is essentially required. If LLM makes a type-level mistake on arguments composition, the agent must feedback the most detailed validation errors, and let the LLM to retry the function calling referencing the validation errors.

[Copilot]

Minor grammar: “success rate soars to 99%” is missing an article (e.g., “the success rate soars …”).

Suggested change

Such validation feedback strategy and combination with `typia` runtime validator, `@agentica/core` has achieved the most ideal LLM function calling through the **function calling harness** pattern. In my experience, when using OpenAI's `gpt-4o-mini` model, it tends to construct invalid function calling arguments at the first trial about 50% of the time. By the way, if you correct it through validation feedback with `typia`, success rate soars to 99%. And I've never had a failure when trying validation feedback twice.

Such validation feedback strategy and combination with `typia` runtime validator, `@agentica/core` has achieved the most ideal LLM function calling through the **function calling harness** pattern. In my experience, when using OpenAI's `gpt-4o-mini` model, it tends to construct invalid function calling arguments at the first trial about 50% of the time. By the way, if you correct it through validation feedback with `typia`, the success rate soars to 99%. And I've never had a failure when trying validation feedback twice.