Prompt Engineering Guide

Abstraction: Primer on LLM-powered agents: capabilities, design patterns, use cases

Key points:

• AI agents extend LLMs with three core capabilities: planning/reflection, tool access (APIs, databases), and memory (short and long-term)
• Distinguish from plain LLMs: agents handle multi-step, broad tasks requiring real-world information and reasoning chains
• Common use cases: recommendation systems, customer support, research, e-commerce, booking, financial analysis, reporting
• Example: marketing strategy agent must research competitors, analyze trends, access internal data — beyond a standalone LLM
• Design philosophy: break complex objectives into manageable subtasks assigned to specialized agent components
• Part of DAIR.AI's Prompt Engineering Guide (promptingguide.ai)

Connections: Promptingguide AI · AI Agents · Prompt Engineering · Large Language Models

Source: https://www.promptingguide.ai/agents/introduction