Virtual Think Tank: Optimizing Trade-offs in Software Architecture with Large Language Models

Software architecture has always been considered an art form, involving the weighing of options. Which database system is better suited for the long term? Should a monolithic approach be chosen, despite the greater complexity it entails, or microservices? Questions like these require technical expertise and the ability to consider and objectively weigh different perspectives.

While tools such as GitHub Copilot have long changed the way we program, the use of virtual think tanks opens up a new dimension by providing support for strategic architecture decisions.

Software developers are already using LLMs to generate clean, functional code. However, architectural decisions are not concerned with syntax or specific algorithms. Instead, the focus is on overarching quality characteristics, such as maintainability, security, performance, and scalability.

Because there are rarely clear-cut answers, but rather trade-offs, AI-supported thought processes can demonstrate their added value. A virtual think tank facilitates the transition from a reactive approach, such as generating code at the touch of a button, to a proactive approach that supports complex design decisions.

This approach is unique because it simulates different expert roles.

• For example, an architect might argue from the perspective of system complexity.
• Another architect advocates for an alternative approach.
• A security expert focuses on vulnerabilities and compliance.
• A DevOps specialist evaluates operating costs and scalability.
• A UX designer considers consistency for users.

The LLM brings these perspectives together, creating a structured discourse that presents decision-makers with a variety of viewpoints. This makes it easier to make informed decisions by putting subjective preferences into perspective.

To ensure that the debate proceeds systematically, the teams structure the process.

• Targeted prompts clearly define roles and criteria.
• Architectural alternatives are examined based on quality attributes such as security, maintainability, and scalability.
• Virtually generated stakeholders discuss typical conflicts and present counterarguments.
• The AI records the decision-making process in a comprehensible protocol.

This allows potential weaknesses in an architecture to be identified early, before real implementation costs are incurred.

The advantages are obvious.

• Expertise is democratized and made available to smaller teams.
• Discussions are less influenced by confirmation bias or hierarchies.
• There is a 24/7 advisory body available without the need for external budgets.
• Well-documented decision-making processes allow for traceability later on.

Nevertheless, this method must be used reflectively. LLMs lack practical experience with the latest technology and may make errors in detail. Therefore, human validation remains essential.

In the future, virtual think tanks may become more closely linked to development processes. For example, architectural decisions could be directly linked to architecture decision records. Automated checks for pull requests and continuous validation during development are also becoming feasible. Additionally, specialized language models could be developed to provide companies with individualized support on architectural topics.

In such a scenario, software architecture becomes a living process in which AI helps make options more transparent and well-founded, but does not make the decision.