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erik

@etash

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main takeaways from openai's newly published agent guide: teams can start adding agents to their workflows right now keeping design and safety top of mind. for product and engineering teams, agents are highly useful in areas where: - decision making is complex - traditional algorithms fall short - rules are difficult to maintain - unstructured data is a challenge every effective agent requires three components: - the model, which serves as the brain - the tools, or APIs, to take action - the instructions, which provide guidelines and constraints one lesson i've learned is that you don't always need the newest or most advanced model. it's easy to assume the latest release is the best fit, but if your use case is tagging emails or processing documents, a simpler model is often more cost-effective. e.g., Sonnet 4 and Opus 4 are strong for programming but older models still outperform in reasoning or writing tasks. choose the right tool for the job. in terms of architecture, you can use either a single-agent or multi-agent approach. anthropic recently showed that multi-agent systems can outperform single-agent setups on certain tasks. these can be organized with a central manager or as a decentralized peer system. guardrails are essential for managing privacy, security, and reputation risks. multiple layers of protection are needed, such as relevance filters and output validation. it's also important to have a human backup for situations where the system flags an issue.
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