Your AI Agent Probably Should Be a Workflow

🔗 https://shre.ink/TobiasZ-AIAgents-vs-Workflow

In this blog post, Tobias Zwingmann discusses the current trend of AI agents and emphasizes that many of these implementations could be simplified into workflows. He highlights the complexities and potential pitfalls of adopting agentic systems prematurely, suggesting that simpler solutions are often more effective. Zwingmann outlines six workflow patterns that can address most AI use cases:

1. Prompt Chaining: Decomposing complex tasks into sequential, manageable steps.

2. Multi-Expert Analysis: Utilizing multiple LLMs concurrently, each serving as a specialized expert to analyze the same input from different perspectives.

3. Ensemble Voting: Executing the same task multiple times to enhance confidence in the output, particularly useful in high-stakes classification scenarios.

4. Parallel Processing: Dividing large tasks into smaller segments that can be processed simultaneously to achieve faster results or to handle content length limitations.

5. Iterative Refinement: Different LLMs working as a team - one creates while the others reviews and provides feedback.

6. Dynamic Planning: The first LLM plans the steps dynamically, the rest LLMs execute following a structured workflow.

He cautions against the hidden development costs, integration challenges, and the risk of framework lock-in associated with complex agent frameworks. Zwingmann advises opting for the simplest solution possible, often achievable without extensive frameworks, to effectively implement AI solutions.