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Mery-Sanz 2025-05-14 12:14:33 +02:00
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docs/agents.md
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@ -78,7 +78,7 @@ agent = Agent(
```
!!! note
<span style="color: red;"> When you pass an `output_type`, that tells the model to use [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) instead of regular plain text responses.</span>
When you pass an `output_type`, that tells the model to use structured outputs instead of regular plain text responses.
## Handoffs

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docs/tools.md
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@ -8,8 +8,8 @@ Tools let agents take actions: things like fetching data, running code, calling
## Hosted tools
CAI offers a few built-in tools when using the [`OpenAIResponsesModel`][cai.sdk.agents.models.openai_responses.OpenAIResponsesModel]. They are in [tools](src/cai/tools) and grouped in 6 major categories inspired by the <span style="color: red;">security kill chain[^2]<span>:
s
CAI offers a few built-in tools when using the [`OpenAIResponsesModel`][cai.sdk.agents.models.openai_responses.OpenAIResponsesModel]. They are in [tools](src/cai/tools) and grouped in 6 major categories inspired by the security kill chain[2]:
1. Reconnaissance and weaponization - *reconnaissance* (crypto, listing, etc)
2. Exploitation - *exploitation*
@ -300,3 +300,10 @@ When you create a function tool via `@function_tool`, you can pass a `failure_er
- If you explicitly pass `None`, then any tool call errors will be re-raised for you to handle. This could be a `ModelBehaviorError` if the model produced invalid JSON, or a `UserError` if your code crashed, etc.
If you are manually creating a `FunctionTool` object, then you must handle errors inside the `on_invoke_tool` function.
---
[1] Arguably, the Chain-of-Thought agentic pattern is a special case of the Hierarchical agentic pattern.
[2] Kamhoua, C. A., Leslie, N. O., & Weisman, M. J. (2018). Game theoretic modeling of advanced persistent threat in internet of things. Journal of Cyber Security and Information Systems.
[3] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023, January). React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR).