Inside Catio’s Multi-Agent Recommendation Engine for Enterprise Architecture

While AI has permeated nearly every domain from sales to customer service to engineering the role of architects and technical decision makers has remained conspicuously underserved. But at Catio, we believe architecture deserves AI-native support too. It’s not that we are jealous as architects, it’s just that why can’t we also have nice things? 😊

Modern enterprise architecture is a dynamic, interdependent system. And just like enterprise teams, effective AI systems need more than isolated intelligence. They require collaboration, coordination, and contextual awareness.

That’s where multi-agent systems (MAS) shine, as agents that think and work together.

Unlike single-task bots, multi-agent systems model the way real teams operate: through communication, cooperation, and negotiation. Each agent brings expertise, shares knowledge, and adapts based on others’ inputs.

Catio’s Recommendation Engine embodies this approach. Built on a powerful AI orchestration framework, it enables real-time, data-driven architectural recommendations tailored to your tech stack, goals, and constraints.

It’s a core component of Catio’s personalized architecture copilot that’s built to reduce your Mean Time to Informed Decision and unlock intelligent design at enterprise scale.

Let’s walk through how it works and the technologies that are powering this engine to bring AI-native decision-making to enterprise architecture.

How the Recommendation Engine Works

The Catio platform continuously analyzes your architecture and its evolution. It surfaces contextual, high-impact recommendations based on:

• Your current tech stack and deployment patterns
• Business goals and product requirements
• Real-world operating conditions and constraints

These aren’t generic “best practices.” They’re precision insights for your actual environment that are targeted, trustworthy, and actionable. The current Recommendations Engine supports architectural domains like:

• Messaging & APIs: Design patterns, eventing strategies, REST vs GraphQL decisions
• Data & Infrastructure: Storage optimization, scaling plans, performance tuning
• ML/AI Architectures: Component orchestration, GPU utilization, model serving
• Security (currently AWS-focused): Live posture evaluation using configs and logs
• IAM & Access Controls: Principle-of-least-privilege, zero trust models
• Performance Optimization: Throughput scaling, latency reduction

With every insight, the system considers technical architecture, operational signals, and organizational context. It’s design guidance grounded in your stack, not someone else’s blog.

Meet Your Copilot Architecture Team

The power of multi-agent systems cannot be undersold. It represents a powerful paradigm in artificial intelligence, making AI relevant in complex and dynamic systems. Let’s click into what this looks like in action.

At the heart of our recommendation engine lies a team of intelligent, specialized agents with each focused on a key area of architecture. Think of it as your virtual staff of AI architects handling domain-specific analysis and design in areas (e.g. Messaging, AI, IAM, Infrastructure, and Network Security, as mentioned above), led by a digital Chief Architect agent.

The Team

• Chief Architect Agent: Orchestrates the end-to-end recommendation pipeline
• 10 Staff Architect Agents: Focused on the specific domains
• Requirements Retriever: Gathers relevant and appropriate business and product information as required by the questions facing the Architect Agents
• Architecture Retriever: Maps current deployments and topology and retrieves relevant and appropriate subsets of the deployments as required by the questions facing the Architect Agents
• 2 Knowledge Bases: Built with vector search and memory, for contextual recall
• Chat History Engine: Logs all interactions and enables agent cloning for fast, parallelized work

Each staff agent consults the Requirements Retriever for business needs and an Architecture Retriever for current system data

Each recommendation cycle results in either a targeted design suggestion or a complete design proposal including:

• Gap analyses
• Target state architecture
• Migration plans
• Risks and KPIs

All of this happens in parallel thanks to agent cloning, powered by a chat history engine that ensures contextual consistency. It’s repeatable, and grounded in your real systems.

These agents don’t just wait for prompts, they reason, ask questions, retrieve data, and make judgments. Just like your best architects with deep reasoning at scale.

Inside the AI Orchestration Stack

What powers this multi-agent symphony? A robust and flexible architecture rooted in the best of open source and cutting-edge LLM infrastructure.

Key Components

• Agent-Based Architecture: Built on LangChain and LangGraph for stateful, dynamic orchestration. Enables a complex, stateful AI workflows with dynamic decision-making, context preservation, and inter-agent communication for scalable application logic.
• Flyte Integration: Workflow orchestration for reliable, stepwise execution across AI workflows.
• LLM Infrastructure: Claude 3 from Anthropic, deployed via AWS Bedrock, supporting architectural analysis to  natural language reasoning and design generation
• Natural Language to SQL: Schema-aware agents convert human questions into valid, executable SQL using from sources including Steampipe.
• Code Gen Agents: Transform DSL mappings into data/service API calls—bridging recommendation and implementation
• EagleEye System (aka the Catio Flux Capacitor): Our own meta-framework that observes and analyzes inter-agent conversation threads for traceability and insight. The system retrieves conversations from our "conversation oriented database," which we have specifically designed to maintain traceability between different agents' interactions.

These layers work together to deliver context-rich, traceable, and verifiable architectural advice at scale.

Already in Production. Already Helping Architects.

Catio’s Recommendations Engine isn’t just a prototype, it’s live, in production, and supporting  customers today.

Need to align tech decisions with product goals? Optimize performance in your cloud infrastructure? Model design tradeoffs before you commit?

Whether you're navigating a security review, planning for scale, or rethinking your messaging layer, your Catio team of architects is ready to support you.

And this is just the beginning. We’re expanding the system with more domains, deeper integrations, and new agent capabilities every month.

👉 Ready to see how AI-native architecture planning works?

Book a demo or get early access to our Recommendations Engine today.

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