You Don’t Need an LLM Agent

🌈 Abstract

The article discusses the concept of Agentic Architecture, which involves using a swarm of LLM (Large Language Model) agents to tackle complex tasks. The author, Louis Chan, who is the Tech Lead in KPMG's Global Lighthouse and co-founder of KPMG's Enterprise GenAI-as-a-Service Platform, argues that businesses do not necessarily need an Agentic Architecture and may be better off not using one.

🙋 Q&A

[01] The Problem with Agentic Architecture

1. What are the key issues with using an Agentic Architecture?

• The author suggests that an Agentic Architecture, which involves using a swarm of LLM agents to tackle complex tasks, may not be necessary for most businesses.
• The author argues that businesses often adopt new technologies like Agentic Architecture without fully understanding their needs or the implications of the technology.
• The author suggests that businesses should focus on solving their own daily pain points rather than implementing complex technologies for the sake of using them.

2. What are the limitations of using LLM agents in an Agentic Architecture?

• LLMs are trained on publicly available data, which may not include information on how competitors or clients actually run their businesses.
• Automating business processes using LLM agents may result in creative or statistically unpredictable outcomes, which may not align with established protocols or procedures.
• Integrating LLM agents into existing systems and processes can be challenging and may lead to technical debt and silos.

[02] Alternatives to Agentic Architecture

1. What alternative approach does the author suggest for integrating LLMs into business processes?

• The author suggests converting business processes into well-documented and well-tested LLM pipelines, where each step has a defined input and output data structure.
• This approach allows for better transparency, robustness, and reliability, as well as the ability to debug issues in the LLM pipeline.
• The author suggests that this approach may be more suitable for businesses looking to integrate LLMs into their operations, rather than adopting a complex Agentic Architecture.

2. What are the benefits of the LLM pipeline approach?

• It allows businesses to think about how tasks have been conducted before the introduction of LLMs, and to integrate the technology in a more controlled and auditable manner.
• It reduces the "black box" nature of LLM agents and provides more visibility into the execution of tasks.
• It aligns with the principles of data engineering, DevOps, and MLOps, which emphasize transparency, robustness, and reliability.