docs: clarify blocking vs parallel guardrail behavior

Author: RVV-karma

docs/guardrails.md

@@ -1,6 +1,6 @@
 # Guardrails
 
-Guardrails enable you to do checks and validations of user input and agent output. For example, imagine you have an agent that uses a very smart (and hence slow/expensive) model to help with customer requests. You wouldn't want malicious users to ask the model to help them with their math homework. So, you can run a guardrail with a fast/cheap model. If the guardrail detects malicious usage, it can immediately raise an error and prevent the expensive model from running, saving you time and money.
+Guardrails enable you to do checks and validations of user input and agent output. For example, imagine you have an agent that uses a very smart (and hence slow/expensive) model to help with customer requests. You wouldn't want malicious users to ask the model to help them with their math homework. So, you can run a guardrail with a fast/cheap model. If the guardrail detects malicious usage, it can immediately raise an error and prevent the expensive model from running, saving you time and money (**when using blocking guardrails; for parallel guardrails, the expensive model may have already started running before the guardrail completes—see "Execution modes" below for details**).
 
 There are two kinds of guardrails:
 

src/agents/guardrail.py

@@ -70,7 +70,7 @@ class OutputGuardrailResult:
 
 @dataclass
 class InputGuardrail(Generic[TContext]):
-    """Input guardrails are checks that run in parallel to the agent's execution.
+    """Input guardrails are checks that run either in parallel with the agent or before it starts.
     They can be used to do things like:
     - Check if input messages are off-topic
     - Take over control of the agent's execution if an unexpected input is detected