AI Architecture for Technical Leaders

intermediate ~5 hours architecture ai-strategy agents mcp

A technically rigorous guide to AI system architecture for product managers and executives who can evaluate architecture diagrams. This path walks through the major architectural decisions in modern AI systems — from the LLM application stack to agent frameworks, communication protocols, and multi-model strategies.

By the end of this path, you’ll be able to: evaluate your engineering team’s architecture proposals with informed questions, understand the protocol landscape (MCP, AG-UI) for platform integration decisions, and assess whether your system architecture is ready for multi-model, multi-agent production workloads.

This path draws from Anthropic, OpenAI, CrewAI, MCP, AG-UI, and a16z enterprise research to give you a cross-provider architectural perspective rather than a single-vendor view.

Steps

  1. Emerging Architectures for LLM Applications ai-strategy article intermediate

    Reference architecture for the LLM application stack, covering data pipelines, embedding models, vector databases, orchestration frameworks, and operational tooling.

    Start with the most widely-cited architectural map of the LLM application stack. a16z's decomposition — data pipelines, embedding models, vector databases, orchestration, and operational tooling — gives you the vocabulary for evaluating your team's architecture proposals. Focus on which components are commoditizing vs. differentiating, and whether your current stack matches the emerging consensus.

  2. Overview anthropic-platform beginner

    Anthropic's agent architecture provides a concrete reference implementation for how a major provider thinks about agent design. For technical leaders, the key is understanding the three capability layers — tool use, memory, and multi-step planning — and how they map to your product's requirements. This is not about implementing Anthropic's agents specifically, but about understanding the architectural pattern that all providers are converging on.

  3. Enterprise anthropic-platform advanced

    Enterprise agent architecture adds constraints that don't exist in prototypes: access control, audit trails, human-in-the-loop workflows, and multi-tenant isolation. Review this through the lens of your compliance and security requirements. If your product serves regulated industries, these patterns aren't optional — they're table stakes.

  4. Production best practices openai advanced

    Explore best practices for transitioning your AI projects from prototype to production, including scaling, security, and cost management.

    OpenAI's production checklist complements the architecture view with operational concerns: rate limiting, error handling, monitoring, and deployment patterns. Compare this with Anthropic's approach — the overlap tells you what's essential regardless of provider, and the differences reveal where provider choice affects your operational architecture.

  5. Architecture overview mcp beginner

    MCP is becoming the standard protocol for connecting AI agents to external tools and data sources. Understanding MCP's architecture — the client-server model, capability negotiation, and transport layers — is essential for evaluating whether your platform should expose MCP endpoints, consume them, or both. This is an infrastructure bet that affects your integration strategy for the next 2-3 years.

  6. Core architecture ag-ui advanced

    Understand how AG-UI connects front-end applications to AI agents

    AG-UI addresses a gap that most AI architecture discussions ignore: how do agents communicate with user interfaces? As your product adds AI-powered features, the agent-to-UI protocol determines what real-time interactions are possible. Review this alongside MCP — MCP connects agents to tools, AG-UI connects agents to users. Together they define the communication architecture of an agentic system.

  7. Production Architecture crewai advanced

    Best practices for building production-ready AI applications with CrewAI

    CrewAI's production architecture shows how multi-agent systems work at scale — task delegation, memory sharing, agent coordination, and result aggregation. Even if you don't use CrewAI, the patterns here (hierarchical vs. sequential vs. consensual agent workflows) are the architectural vocabulary for evaluating any multi-agent framework proposal your team brings forward.

  8. How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025 ai-strategy article intermediate

    Updated enterprise AI survey showing 81% of enterprises now use 3+ model families, with data on procurement patterns, multi-model optimization, and Anthropic's growing enterprise penetration.

    Close the architecture loop with hard data: 81% of enterprises now use 3+ model families. This isn't future speculation — it's the current reality. Your architecture needs model-agnostic abstractions, routing logic, and per-model cost tracking. Use this to validate whether your current architecture is multi-model ready, or whether you're accumulating technical debt with a single-provider design.