Artificial Intelligence
AI for Architects
Overview
AI for Architects is a three-day intensive program designed for architects and technical leaders responsible for building and operating enterprise-grade AI systems. The course focuses on the architectural patterns, platforms, and operational practices required to move from experimentation to production.
Participants will explore modern AI system design, including retrieval-augmented generation (RAG), agentic AI, and multi-agent orchestration. The course examines emerging standards such as the Model Context Protocol (MCP), as well as practical approaches to tool integration, context management, and AI system coordination.
Beyond design, the course emphasizes production readiness—covering CI/CD for AI, evaluation frameworks, observability, cost control, and governance. Attendees will gain a clear understanding of how to build AI systems that are scalable, reliable, and aligned with enterprise requirements.
Duration
3 Days
Who Should Take This Course
Audience
Software, System, and Solution Architects, Tech Leadership, and Senior AI staff.
Prerequisites
- Experience with software or systems architecture
- Familiarity with development and operational environments such as Linux, Git, containers, and CI/CD pipelines
- Basic awareness of AI/ML concepts is helpful but not required
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Understand the end-to-end AI system lifecycle, including data pipelines, model development, evaluation, deployment, and continuous improvement
- Evaluate architectural trade-offs across model selection, training inference, latency, scalability, and cost optimization
- Design and implement agentic AI systems, including tool use, memory, context management, and multi-agent collaboration
- Apply modern platform engineering practices (DevOps, CI/CD, GitOps) to AI and agent-based workloads
- Architect AI systems that integrate with enterprise platforms, APIs, and data ecosystems
- Implement observability, evaluation, and feedback loops to monitor model and agent behavior in production
- Design for security, privacy, and governance, including responsible AI and regulatory considerations
- Plan for Day 2 operations, including lifecycle management, versioning, retraining, and system evolution
Course Outline
AI for Architects
Day 1: AI Systems & Agentic Architectures
- AI Architecture Foundations
- End-to-end AI system reference architecture (data → model → application)
- Core components: models, tools, memory, orchestration layers
- Common patterns: batch, real-time, RAG, agent-based systems
- Mapping AI systems to enterprise architectures and constraints
- LAB: Decompose a real-world AI use case into a reference architecture diagram and identify key components
- Data, Models & Inference Decisions
- Data pipeline basics: ingestion, preparation, and quality considerations
- Model landscape: APIs vs. open models vs. fine-tuned models
- Training vs. inference: when you actually need training (and when you don’t)
- Latency, cost, and accuracy trade-offs in production systems
- LAB: Evaluate 3 architecture options (API-only, RAG, fine-tuned) for a business scenario and justify the choice
- Retrieval-Augmented Systems (RAG)
- When to use RAG vs. fine-tuning vs. agents
- Embeddings, chunking, and vector database fundamentals
- Retrieval strategies: semantic search, hybrid search, filtering
- Context construction and prompt assembly patterns
- LAB: Design a RAG pipeline (data → embeddings → retrieval → prompt) and identify failure points
- Agentic & Multi-Agent Architectures
- What makes a system an “agent” (tools, memory, planning, autonomy)
- Agent execution patterns: single-agent vs. multi-agent systems
- Orchestration models and control loops
- Interoperability and emerging standards (e.g., MCP)
- LAB: Design a multi-agent workflow (planner + executor + tool agent) for a real business process
Day 2: Platforms, Tooling, and Delivery
- AI Platforms & Build vs. Buy Decisions
- Model providers, hosting options, and deployment patterns
- Agent frameworks and orchestration platforms
- Tool integration and API-first system design
- Build vs. buy vs. compose: decision frameworks for architects
- LAB: Select an architecture stack (models, tools, orchestration) for a given enterprise scenario and defend trade-offs
- CI/CD for AI and Agent Systems
- Why CI/CD is different for AI (non-determinism, evaluation challenges)
- Pipelines for prompt, model, and agent updates
- Testing strategies: prompt testing, scenario testing, regression detection
- Release strategies, rollback, and risk mitigation
- LAB: Design a CI/CD pipeline for an agent-based system including testing and rollback checkpoints
- GitOps & Infrastructure for AI
- Declarative infrastructure and environment management
- Versioning models, prompts, data, and configurations
- Runtime environments: containers, Kubernetes, and serverless
- Reproducibility and environment consistency challenges
- LAB: Define a GitOps structure for managing an AI system (repos, configs, environments, versioning strategy)
- Observability & Performance Engineering
- What to measure: latency, cost, accuracy, and user outcomes
- Logging, tracing, and monitoring agent workflows
- Evaluating outputs and detecting drift or degradation
- Cost and performance optimization strategies
- LAB: Create an observability plan (metrics, traces, alerts) for an AI application with defined SLAs
Day 3: Operations, Security, and Governance
- AI Security & Threat Modeling
- Unique AI threat models: prompt injection, data leakage, tool abuse
- Securing model interactions and external tool usage
- Data protection strategies across pipelines and inference
- Access control and system hardening approaches
- LAB: Perform a threat model exercise on an agent-based system and identify mitigations
- Privacy & Responsible AI
- Data privacy fundamentals (PII, sensitive data, regulatory context)
- Bias, fairness, and ethical risks in AI systems
- Governance of training and inference data usage
- Transparency, explainability, and auditability
- LAB: Assess an AI use case for privacy and ethical risks and propose mitigation strategies
- Lifecycle & Continuous Improvement
- Model and agent versioning and release strategies
- Retraining vs. prompt iteration vs. system redesign
- Feedback loops, evaluation datasets, and human-in-the-loop
- Managing evolving behavior in agent systems
- LAB: Design a lifecycle strategy for an AI system including evaluation, updates, and feedback integration
- Governance & Operating at Scale
- Organizational patterns for AI adoption (platform teams, enablement)
- Cost governance and resource management
- Policy, controls, and compliance integration
- Long-term sustainability and architectural evolution
- LAB: Create a governance and operating model for scaling AI across an enterprise