AI agents have gained attention as the concept of agency becomes increasingly significant. Unlike traditional large language models that are reactive, AI agents are autonomous and goal-seeking systems. They can plan, decide, and act on complex problems, emphasizing the criticality of giving them authority and responsibility. Prompt engineering plays a key role in developing these agents, as it guides their thought processes. Contemporary techniques such as 'chain-of-thought' prompting and the ReAct framework enhance the effectiveness of AI agents in complex task management.
The essence of an AI agent comes down to one word: agency. AI agents operate with autonomy, being goal-seeking systems that plan, decide, act, and iterate.
When we give an AI agent a goal, we provide it authority and responsibility to decide how to achieve that goal, demonstrating agency in action.
Prompt engineering has evolved beyond clever phrasings. It now focuses on structuring prompts to guide how an agent should think, reason, and act.
Techniques such as 'chain-of-thought' prompting and the ReAct framework assist agents in navigating complex multi-step tasks, making them more effective.
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