Introduction to AI Agents
AI agents are more than just fancy chatbots — think of them as autonomous helpers that can “think” and act on their own, orchestrating multiple steps or tools to accomplish a goal. In practice, agents often combine LLM-powered reasoning with external tools (databases, APIs, etc.) to tackle complex tasks.
What are Agentic Systems?
Anthropic refers to all these setups as agentic systems, where a clear split exists: workflows run along predefined code paths, while true agents let the LLM decide its next moves dynamically. As more products rely on LLMs to do multi-step reasoning, it becomes essential to organize those steps using familiar design patterns. Much like software design patterns, these AI workflow patterns provide blueprints for breaking down complex jobs into manageable pieces, making systems easier to build, debug, and scale.
Key Patterns for Agentic Workflows
Below are five key patterns that keep agentic workflows on track. Each one represents a common way to choreograph LLM calls and tool use.
Pattern 1: Prompt Chaining
Prompt chaining is exactly what it sounds like: you chain a series of prompts together, where the output of one LLM call becomes the input to the next. In other words, you decompose a task into smaller sub-tasks, and the LLM generates the input for the next sub-task based on the output of the previous one.
Conclusion
In conclusion, AI agents are powerful tools that can automate complex tasks by combining LLM-powered reasoning with external tools. By using agentic systems and familiar design patterns, developers can build, debug, and scale these systems more efficiently. Understanding these key patterns, such as prompt chaining, is essential for creating effective agentic workflows.
FAQs
- What are AI agents?
AI agents are autonomous helpers that can “think” and act on their own, orchestrating multiple steps or tools to accomplish a goal. - What are agentic systems?
Agentic systems refer to setups where workflows run along predefined code paths, while true agents let the LLM decide its next moves dynamically. - What is prompt chaining?
Prompt chaining is a pattern where a series of prompts are chained together, where the output of one LLM call becomes the input to the next.
