From: aidotengineer
AI systems, particularly those designed for cloud architecture, necessitate advanced reasoning capabilities beyond mere automation to manage increasing complexity [00:00:16]. Cloud systems are growing in complexity due to diverse users, developers, tools, constraints, and ever-rising expectations [00:00:24]. Synergistic tools struggle to scale across the wide variety of decisions required for cloud architecture [00:00:37]. This demand calls for systems that can understand, debate, justify, and plan, which is indicative of reasoning, not just automation [00:00:44].
Architectural design is not solely technical; it is also highly cognitive, involving constant negotiation of trade-offs based on requirement definition, available time, and resources [00:00:56]. Architects rely on scattered and implicit context to make these decisions, making it crucial for AI to comprehend their thought processes [00:01:21].
Challenges at the Intersection of AI and Architecture
Key challenges arise when AI meets architecture design:
- Requirement Understanding How to process requirements from various formats, identifying crucial, global, or specific elements [00:01:46].
- Architecture Identification Understanding the functions of diverse components within an architecture to grasp its overall operation [00:02:05].
- Architecture Recommendation Combining requirements and current architecture state to provide recommendations that either meet needs or improve adherence to best practices [00:02:27].
These high-level problems translate into specific AI challenges involving a blend of semantic and graphic context [00:02:51]. Requirements are primarily textual, representing semantic context, while architecture is inherently graph data [00:02:59]. The challenge lies in integrating these disparate data sources to enable higher-level reasoning and handle complex, vague, and broad queries that require breakdown and planning [00:03:10]. Evaluating and providing feedback to such large, multi-component AI systems is also crucial [00:03:42].
Grounding AI Agents in Specific Context
Effective reasoning by large language models (LLMs) requires proper context about architecture [00:04:14]. Translating natural language into meaningful architecture retrieval tasks, especially quickly, is challenging [00:04:20].
Strategies employed include:
- Semantic Enrichment of Architecture Data Collecting relevant semantic information for each component to make 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]. This approach allows not only finding nodes but also connecting them and adding context for proper reasoning [00:07:00].
- Early Score Enrichment of Requirement Documents Scoring documents based on important concepts to facilitate faster retrieval, particularly when dealing with a large corpus of text [00:05:22]. Initial iterations used requirement templates to structure extracted information for downstream tasks [00:08:50].
Learnings on Grounding Agents
- Semantic grounding improves reasoning but has limitations, especially when dealing with complex graph data, where it may not scale or can lead to overly detailed responses [00:06:11].
- The exact structure and focus for retrieval are critical for agents [00:06:31].
- Graph memory supports continuity in understanding relationships between different nodes [00:06:47].
Initial architecture retrieval designs involved breaking down JSON architecture data into natural language, enriching it with connection data, embedding, and storing it in a vector database for semantic search [00:07:16]. While yielding some good results, semantic search proved limited for graph data, leading to a shift towards more graph-based searches and the development of a knowledge graph approach [00:08:00]. Similarly, while requirement templates aided fast retrieval, context can be lost in larger searches, suggesting a potential role for graph analysis here as well [00:09:37].
Complex Reasoning Scenarios with Multi-Agent Orchestration
Architectural design inherently involves conflicting goals, trade-offs, and debates [00:10:07]. AI agents need to collaborate, argue, and converge on justified recommendations [00:10:19].
To achieve this, the following were implemented:
- Role-Specific Multi-Agent Orchestration A system where multiple agents with distinct roles work together, supporting dynamic resolution of trade-offs rather than just executing static plans [00:10:30].
- Structured Message Format Transitioning from XML to structured messages improved workflow and inter-agent collaboration, enabling longer agent chains [00:10:53].
- Context Management Isolating conversations between agents to conserve tokens and prevent increased hallucination observed with larger shared memories [00:11:25].
- Cloning Agents for Parallel Processing Duplicating agents for specific tasks, managing memory at the cloning point, to speed up processes [00:12:05].
Learnings on Multi-Agent Systems
- Structured outputs are critical for clarity and control, despite potential concerns about reducing the model’s reasoning abilities [00:13:34].
- Dynamic orchestration encourages higher creativity in planning and reaching results [00:13:35].
- Successful multi-agent orchestration requires robust control flow, not just hopeful interaction [00:13:50].
The production system generates recommendations using a multi-agent setup [00:14:14]. This includes:
- A Chief Architect overseeing and coordinating high-level tasks [00:15:17].
- Ten Staff Architects, each specializing in a domain (e.g., infrastructure, API, IAM) [00:15:23].
- A Requirement Retriever accessing requirement data [00:15:36].
- An Architecture Retriever understanding the current architecture state and components [00:15:45].
The workflow involves three main sequential tasks:
- List Generation Staff architects request information from retrievers (in parallel) to generate a list of possible recommendations [00:16:11].
- Conflict Resolution The Chief Architect prunes the generated list for conflicts or redundancies [00:16:20].
- Design Proposal Cloned staff architects (each with access to past history but generating separate current histories) write full design proposals for each recommendation topic, detailing gap analysis and proposed improvements [00:16:49].
Evaluation and Feedback
Determining the quality of AI-generated recommendations, especially within complex multi-agent systems with rounds of conversations, is a significant challenge [00:19:03].
Key findings regarding evaluation:
- Human evaluation is the most effective approach, especially in early development stages, as LLM evaluations do not provide the necessary depth for improvement [00:19:32].
- An internal human evaluation tool, “Eagle Eye,” allows detailed analysis of specific cases, architectures, extracted requirements, agent conversations, and generated recommendations to assess relevance, visibility, and clarity [00:19:55].
- Confidence in AI output does not equate to correctness [00:20:38].
- Evaluation must be integrated into the system design from the outset, not added as an afterthought [00:20:56]. This includes considering human evaluation tools, monitoring dashboards, or LLM-based feedback loops [00:21:28].
- Monitoring conversations can help identify issues like hallucinations, where agents might deviate or propose irrelevant actions [00:22:02].
Conclusion: Reasoning Systems, Not Just Assistance
Building an AI co-pilot for cloud architecture is about designing a system that can reason, rather than merely generating answers [00:23:06]. This requires handling vast amounts of data, including thousands or millions of architecture components and numerous documents, to answer diverse questions from various stakeholders [00:23:16].
Key elements for building such a reasoning system include:
- Defining clear roles and workflows for agents [00:24:04].
- Implementing effective memory management strategies [00:24:06].
- Establishing clear structures for data and communication [00:24:06].
Experimentation is crucial to discover patterns that work best with existing data [00:24:16]. Graphs are increasingly important in these designs, influencing agent interactions and the level of autonomy granted to each agent [00:24:31]. Frameworks like LangGraph are being utilized for building agent workflows, often with a manager layer for higher-level control [00:25:00]. Capturing as much memory as possible in graphs ensures AI always has the right context [00:25:34]. This ongoing development is seen as the future of AI-driven software design [00:25:53].