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Challenges in AIdriven architecture design

6 min read

From: aidotengineer

Designing cloud architectures is a complex process that demands more than just automation; it requires deep reasoning capabilities [00:00:20]. Cloud systems are growing in complexity due to increasing demands from both users and developers, and the constant evolution of tools and constraints [00:00:24]. Traditional synergistic tools struggle to scale across the diverse range of decisions required for cloud architecture [00:00:37]. Effective systems need to understand, debate, justify, and plan to solve these problems, moving beyond mere automation to true reasoning [00:00:44].

The architecture stack is not purely technical; it involves significant cognitive effort [00:00:56]. Architects constantly negotiate tradeoffs based on factors like requirement definition, available time, and resources [00:01:04]. They rely on scattered and implicit context to make decisions, and capturing this context for AI requires understanding how architects think [00:01:21].

Core Design Problems for AI in Architecture

At a high level, three main challenges arise when AI meets architecture design:

  • Requirement Understanding The complexity lies in identifying where requirements originate, their format, what key pieces are important, and their scope (global or specific) [00:01:46].
  • Architecture Identification Understanding how an architecture works requires knowing the different components and their specific functions, as components can have varied roles depending on their placement [00:02:05].
  • Architecture Recommendation Once requirements and the current architecture state are understood, the challenge is to provide relevant recommendations that either meet requirements or improve the architecture to align with best practices [00:02:27].

To address these core design problems, more specific AI-related challenges must be overcome:

  • Mixing Semantic and Graphic Context Requirements are often textual, while architecture data is inherently graphical [00:02:54]. The challenge is to integrate these disparate data sources to enable higher levels of reasoning and make the right connections [00:03:06].
  • Complex Reasoning Scenarios User questions can be vague, broad, or highly complex, requiring the AI system to break them down into manageable parts and plan effectively to deliver accurate answers [00:03:20].
  • Evaluation and Feedback for Large AI Systems Given that AI systems for architecture design can have many moving parts, a significant challenge is evaluating their performance and providing effective feedback for continuous improvement [00:03:42].

Solutions and Learnings

Grounding Agents in Specific Contexts

Large Language Models (LLMs) need proper context to reason effectively. Translating natural language into meaningful architecture retrieval tasks is not straightforward, especially when speed is a factor [00:04:12].

Techniques tried include:

  • Semantic Enrichment of Architecture Data Collecting relevant semantic information for each component makes it more searchable and findable in vector search [00:04:40].
  • Graph-Enhanced Component Search Utilizing graph algorithms to retrieve specific components or types of components within an architecture [00:04:57].
  • Early Score Enrichment of Requirement Documents For faster retrieval, important concepts within large text corpora are identified and scored, enabling quicker retrieval of relevant information [00:05:22]. Initial designs involved structuring extracted information from documents using requirement templates [00:08:47].

Learnings regarding grounding:

  • Semantic grounding improves reasoning but doesn’t always scale or provide sufficiently detailed responses [00:06:09].
  • Correct design is critical in soft grounding, guiding the agent on what to focus on and retrieve [00:06:29].
  • Graph memory supports continuity, allowing the system to connect different nodes within a graph and add context for proper reasoning [00:06:47]. Semantic search has limitations for graph data, leading to a shift towards graph-based searches and knowledge graphs [00:08:00].

Complex Reasoning Scenarios

Good design in architecture often involves conflicting goals, tradeoffs, and debates [00:10:07]. AI agents need to collaborate, argue, and converge on justified recommendations [00:10:19].

Approaches and learnings:

  • Multi-agent Orchestration with Role-Specific Agents Building a system where multiple agents work together, each with specific properties or roles, like a “Chief Architect” overseeing “Staff Architects” specialized in domains such as infrastructure or API management [00:10:30], [00:15:14].
  • Structured Message Format Using structured messages (e.g., JSON over XML) helps build better workflows and enables multiple agents to interact effectively in longer chains [00:10:53]. Structured outputs improve clarity and control [00:13:34].
  • Context Management Isolating conversations between agents prevents token waste and avoids increased hallucination seen with larger shared memories [00:12:00].
  • Cloning Agents for Parallel Processing Duplicating agents for certain tasks, with each clone having access to past history but generating its own separate current history, speeds up processes and allows for parallel generation of design proposals [00:12:05], [00:18:15].
  • Dynamic Tradeoff Resolution Allowing multi-agent systems to dynamically resolve tradeoffs rather than executing static plans leads to higher creativity and better planning [00:13:10].
  • Controlled Flow Successful multi-agent orchestration requires control flows; agents cannot simply operate without guidance hoping for the best outcome [00:13:50].

Evaluation and Feedback

Determining the quality of an AI-generated recommendation, especially in a complex multi-agent system with multiple rounds of conversations, is a key challenge [00:19:03].

Key findings for evaluation:

  • Human Evaluation is Essential At early stages, human evaluation is the most effective method, as LLM evaluations often do not provide the granular insights needed for improvement [00:19:32]. Internal tools like “Eagle Eye” help humans review architectures, requirements, agent conversations, and recommendations for relevance, visibility, and clarity [00:19:55].
  • Confidence is Not Correctness While AI confidence levels can be helpful, they cannot be fully trusted [00:20:38].
  • Early Integration of Evaluation Evaluation must be integrated into the system design from the outset, not added as an afterthought [00:20:59]. This includes planning for human evaluation tools, monitoring dashboards, or LLM-based feedback loops [00:21:28].
  • Handling Hallucinations Evaluation tools help identify and address issues like agents hallucinating conversations or tasks, such as attempting to schedule workshops [00:22:05].

The Future of AI in Software Design

Building an AI copilot for architecture design is about creating a system that can reason, not just generate answers [00:23:06]. This requires a system with a comprehensive view of vast amounts of data, including thousands or millions of components and numerous documents, to answer varied questions from diverse stakeholders [00:23:16]. Achieving this involves defining roles, workflows, memories, and structures [00:24:04].

Experimentation is key, as certain patterns work better based on existing data [00:24:16]. Graph structures are becoming increasingly important in these designs [00:24:31]. Frameworks like LangGraph are being used for building multi-agent workflows, often with a manager layer on top, and knowledge graphs are utilized to capture memory and maintain context for AI tasks [00:24:53]. This ongoing development is seen as the future of how AI will design software [00:25:50].

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