AI Workflows vs Agents
- Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
- Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
Do you need AI agents?
Agentic systems often trade latency and cost for better task performance, and you should consider when this tradeoff makes sense.
When more complexity is warranted, workflows offer predictability and consistency for well-defined tasks, whereas agents are the better option when flexibility and model-driven decision-making are needed at scale.
Ensure to read through AI Workflow patterns before starting with an agent
Agents
- They are typically just LLMs using tools based on environmental feedback in a loop.
- It is therefore crucial to design toolsets and their documentation clearly and thoughtfully.
- Ideal for open-ended problems where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path.
