Autogen vs Strands: Why I Stopped Forcing Agents Everywhere
The article discusses the differences between two AI frameworks, Autogen and Strands, and when to use each one based on the problem at hand.
Why it matters
This article highlights the importance of choosing the right AI framework based on the problem at hand, rather than trying to force a one-size-fits-all solution.
Key Points
- 1Autogen is built around LLM agents that can communicate, ask questions, and revise answers, making it suitable for open-ended, subjective problems.
- 2Strands is built around semantic workflows with defined steps, inputs, and outputs, making it suitable for repeatable, consistent processes.
- 3Trying to use Autogen for structured, predictable problems led to inconsistency, as the author realized the wrong abstraction was being forced onto the problem.
Details
The article explains that Autogen and Strands are not alternatives, but rather tools for solving different types of problems. Autogen is designed for open-ended, reasoning-heavy tasks where the path to the solution is unknown, and quality is subjective. It allows agents to communicate, ask questions, and revise answers. In contrast, Strands is designed for structured, repeatable processes with known steps and consistent outputs. It uses a linear or DAG-based workflow with defined nodes and inputs/outputs. The author realized that trying to use Autogen for predictable, structured problems led to inconsistency, as the wrong abstraction was being forced onto the problem. The article provides examples of when to use each framework, such as using Autogen for code reviews, design critiques, and multi-step decision making, and using Strands for document ingestion, summarization pipelines, and structured data extraction.
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