Summary of https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
Practical guide explains that agents are advanced systems utilizing large language models (LLMs) to independently perform multi-step workflows by leveraging tools. It identifies suitable applications for agents in scenarios involving complex decisions, unstructured data, or unwieldy rule-based systems, emphasizing that simpler LLM applications are not considered agents.
The document outlines the fundamental components of an agent as an LLM model, external tools for interaction, and explicit instructions. It also explores orchestration patterns, from single-agent systems to more complex multi-agent architectures, and stresses the importance of robust guardrails and planning for human intervention to ensure safe and reliable agent operation.
- Agents are LLM-powered systems capable of independently accomplishing complex, multi-step tasks by managing workflow execution and leveraging tools to interact with external systems.
- Agents are particularly well-suited for workflows involving complex decision-making, difficult-to-maintain rules, or heavy reliance on unstructured data, where traditional automation methods encounter friction.
- The foundational components of an agent include the Model (the LLM for reasoning), Tools (external functions/APIs to take action), and Instructions (explicit guidelines for behavior).
- Agent orchestration can follow Single-agent systems (using tools within a loop) or Multi-agent systems(coordinating specialized agents via a manager or peer-to-peer handoffs), often starting with a single agent and scaling up as complexity requires.
- Implementing Guardrails (such as relevance/safety classifiers and tool safeguards) and planning for Human Intervention (for failures or high-risk actions) are critical to ensure agents operate safely, predictably, and reliably.
