Course Details
Course Code: – ; Duration: 2 Days; Instructor-led
The Claude Certified Architect – Foundations certification demonstrates that practitioners can make informed decisions about trade-offs when implementing real-world solutions with Claude.
Audience
The ideal candidate for this certification is a solution architect who designs and implements production applications with Claude. This candidate has hands-on experience with:
- Building agentic applications using the Claude Agent SDK, including multi-agent orchestration, subagent delegation, tool integration, and lifecycle hooks
- Configuring and customizing Claude Code for team workflows using CLAUDE.md files, Agent Skills, MCP server integrations, and plan mode
- Designing Model Context Protocol (MCP) tool and resource interfaces for backend system integration
- Engineering prompts that produce reliable, structured output, leveraging JSON schemas, few-shot examples, and extraction patterns
- Managing context windows effectively across long documents, multi-turn conversations, and multi-agent handoffs
- Integrating Claude into CI/CD pipelines for automated code review, test generation, and pull request feedback
- Making sound escalation and reliability decisions, including error handling, human-in-the-loop workflows, and self-evaluation patterns
Prerequisites
None
Methodology
This program will be conducted with interactive lectures, PowerPoint presentations, and discussions
Course Objectives
The primary purpose of the Claude Certified Architect – Foundation certification is to provide an independent assessment of the knowledge, skills, and abilities required to architect Claude-based solutions competently in production environments. Earning the credential signals to employers, clients, and teams that the holder can own or significantly contribute to the full lifecycle of a Claude-powered system.
Outlines
Module 1: Agentic Architecture & Orchestration
This domain introduces the architectural principles behind modern agentic AI systems and how intelligent agents collaborate to solve complex tasks. Participants explore orchestration patterns, workflow decomposition, multi-agent coordination, and decision-making strategies that enable scalable, reliable, and production-ready AI solutions. The focus is on designing AI systems that effectively balance autonomy, control, and human oversight while delivering measurable business value.
Module 2: Tool Design & MCP Integration
This domain focuses on extending Claude’s capabilities through external tools and the Model Context Protocol (MCP). Participants learn how to design secure, reusable, and well-governed tools, expose enterprise capabilities through MCP servers, and integrate business systems into AI workflows. The domain emphasizes interoperability, structured communication, and scalable integration patterns for enterprise AI applications.
Module 3: Claude Code Configuration & Workflows
This domain explores how Claude Code supports modern software engineering through configurable development workflows, reusable project context, automation, and collaborative AI-assisted development. Participants learn how to configure Claude Code for consistent engineering practices, manage project context effectively, and establish repeatable workflows that improve productivity, code quality, and team collaboration.
Module 4: Prompt Engineering & Structured Output
This domain develops the skills required to design effective prompts that produce reliable, consistent, and predictable AI behavior. Participants learn how to structure prompts, define system instructions, guide reasoning, generate structured outputs, and apply prompt-engineering techniques to improve accuracy, maintainability, and integration with downstream applications and enterprise workflows.
Module 5: Context Management & Reliability
This domain examines how context, memory, and reliability influence the performance of production AI systems. Participants learn techniques for managing context efficiently, optimizing long-running conversations, improving response consistency, handling failures gracefully, and implementing validation strategies that enhance the reliability, observability, and trustworthiness of AI-powered applications.
