Claude AI Solution Architect — Designing Enterprise AI Applications | CCAR-F
(Master Claude AI, Model Context Protocol (MCP), Agentic AI, Enterprise Architecture, and Production-Ready AI Solutions. A scenario-driven, 9-module program built from the official Anthropic exam blueprint and aligned to the Claude Certified Architect — Foundations (CCAR-F) exam domains.)
This comprehensive program provides hands-on training in Claude AI, Generative AI, Large Language Models (LLMs), prompt engineering, enterprise AI architecture, Retrieval-Augmented Generation (RAG), AI agents, and real-world AI application development.
Learners will gain practical expertise in designing and building enterprise-grade AI solutions using Claude, including AI application architecture, LLM integration, prompt design, RAG pipelines, knowledge bases, vector databases, AI agents, API integration, security, scalability, and production-ready AI workflows.
The program includes real-world implementation of Claude-powered AI applications, helping learners develop practical experience with enterprise AI use cases, conversational AI, document intelligence, RAG-based applications, AI agents, workflow automation, tool integration, API-based solutions, and intelligent AI assistants.
By the end of this training, learners will have strong expertise in Claude AI Solution Architecture and Enterprise AI Application Development, complete real-world projects for professional portfolios, and the confidence to design and implement modern AI solutions for roles such as AI Solution Architect, Generative AI Engineer, AI Engineer, LLM Engineer, AI Application Developer, and Enterprise AI Consultant.
Live Sessions Price:
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR13000 INR 9900 Rupees
OR
Free Demo On:
Indian Timings: 26th October @8:00 PM – 9:00 PM (IST)/
U.S Timings: 26th October @ 10:30 AM – 11:30 AM (EST)/
U.K Timings: 26th October @ 3:30 PM – 4:30 PM (BST)
Class Schedule:
For Participants in India: Monday – Friday 8:00 PM – 9:00 PM
For Participants in the US: Monday – Friday 10:30 AM – 11:30 (EST)
For Participants in the UK: Monday – Friday 3:30 PM – 4:30 PM (BST)
What students have to say about Chandra Kumar:
|
⭐ Chandra Kumar has deep knowledge of Claude AI, Generative AI, LLMs, and Enterprise AI Architecture. His explanations of complex topics like RAG, Prompt Engineering, AI Agents, and Claude-based applications were clear and easy to understand. The hands-on projects were extremely valuable. — Amit ⭐ Very supportive trainer with excellent AI and industry knowledge. Chandra Kumar explained every concept step-by-step and provided practical guidance throughout the program. The sessions on Claude AI, RAG, AI Agents, and Enterprise AI Applications were especially useful and informative. — Neha ⭐ One of the best learning experiences I have had in Generative AI and Enterprise AI. Chandra Kumar explained Claude, LLMs, Prompt Engineering, RAG, and AI Agents with real-world use cases. The practical projects helped me gain confidence in designing AI solutions. — Karthik ⭐ Chandra Kumar is an excellent trainer with strong knowledge of modern AI technologies and solution architecture. The sessions were interactive, practical, and easy to follow. His explanations of Claude AI, LLMs, RAG, AI Agents, and enterprise AI workflows were very helpful. — Meera ⭐ Chandra Kumar is an excellent trainer with strong expertise in Claude AI, Generative AI, and Enterprise AI Architecture. The concepts were explained clearly with practical examples and real-time projects. The hands-on sessions were very useful and helped me build confidence in developing production-ready AI applications. — Suresh |
What will I learn by the end of this course?
• Understand Claude AI and Enterprise AI Architecture fundamentals
• Design enterprise-grade AI applications using Claude
• Work with the Claude Agent SDK and Model Context Protocol (MCP)
• Build and configure Claude AI agents, tools, and workflows
• Apply API fundamentals and effective tool design for AI applications
• Implement RAG, context management, and knowledge-grounded AI solutions
• Design multi-agent workflows, task decomposition, and escalation strategies
• Build reliable AI applications with error handling and validation
• Manage context, provenance, built-in tools, and enterprise data effectively
• Create practical Claude-based AI agents and automation workflows
• Apply security, scalability, and enterprise architecture best practices
• Prepare for the CCAR-F certification with exam-focused practice and simulations
Salient Features:
- 60+ Hours of Live Training along with recorded videos
- 1 Year Access to the recorded videos
- Course Completion Certificate
Who can enroll for this course?
🎓 AI Professionals & Solution Architects looking to specialize in Claude AI and Enterprise AI Architecture.
💻 Software Developers who want to build enterprise-grade AI applications using Claude, APIs, and AI agents.
🤖 AI/ML Engineers interested in Generative AI, LLMs, RAG, Agents, and enterprise AI solutions.
🧠 Generative AI Professionals looking to advance their skills in Claude AI, Agent SDK, and MCP.
☁️ Cloud & DevOps Engineers who want to integrate AI agents and intelligent automation into modern cloud environments.
🧪 Software Testers & QA Professionals interested in AI-powered testing, automation, and intelligent workflows.
📊 Data Engineers & Data Scientists looking to work with LLM-powered applications and enterprise AI systems.
🏗️ Enterprise Architects & Technical Leads who want to design scalable and secure AI solutions.
🚀 Working Professionals planning to transition into Generative AI, AI Architecture, and LLM-based application development.
🔍 Anyone with basic AI/technical knowledge who wants to build practical Claude-based enterprise AI applications and prepare for CCAR-F certification.
Course syllabus:
Module 01: API Fundamentals & Tool Design
◆ Foundation
○ API request structure & message roles
○ Understanding the Claude API request/response cycle
○ System prompt priority
○ Context window risks
◆ Tool Design
○ Tool description quality
○ Tool disambiguation techniques
○ Tool choice configuration
○ JSON Schema structured output
○ Designing reliable and unambiguous tool interfaces
○ Understanding Stop reason as an agentic control signal
Module 02: Claude Agent SDK & Model Context Protocol
◆ Agentic Architecture
○ The agentic loop lifecycle
○ AgentDefinition
○ Hub-and-spoke orchestration
○ Multi-agent system architecture
◆ Task & Context Management
○ Task tool
○ Context passing
○ Parallel agent spawning
○ Context isolation
◆ Hooks & MCP
○ Agent SDK hooks
○ PreToolUse and PostToolUse
○ MCP servers
○ MCP configuration scope
○ Structured error handling
Module 03: Claude Code Configuration & Workflows
◆ Claude Code Configuration
○ CLAUDE.md hierarchy
○ path imports
○ claude rules
○ Path-scoped conventions
◆ Development Workflows
○ Slash commands
○ Skills
○ Planning mode
○ Direct execution
◆ CI/CD & Sessions
○ CI/CD integration
○ Session management
○ Team-scale Claude Code development
○ Workflow configuration and optimization
Module 04: Prompt Engineering & Batch Processing
◆ Prompt Engineering
○ Few-shot prompting
○ Five example categories
○ Explicit criteria vs. vague instructions
○ Consistent and structured output
◆ Advanced Prompting
○ Prompt chaining
○ Interview pattern
○ Retry loops
○ Self-correction techniques
◆ Batch Processing
○ Batch API mechanics
○ Cost-efficient processing
○ Batch SLA planning
○ Module 1–4 consolidation
Module 05: Task Decomposition, Escalation & Error Handling
◆ Task Decomposition
○ Fixed pipelines vs. dynamic decomposition
○ Breaking complex tasks into manageable steps
○ Multi-pass code review
○ Reliable workflow design
◆ Escalation & Human Oversight
○ Escalation triggers
○ Handoff protocols
○ Confidence calibration
○ Human oversight
◆ Error Handling
○ Error categories
○ Multi-agent error handling
○ Agent recovery strategies
○ Reliable failure management
Module 06: Context Management, Provenance & Built-in Tools
◆ Context Management
○ Persistent facts blocks
○ Tool-result trimming
○ Position-aware input
○ Scratchpad files
◆ Multi-Agent State Management
○ Subagent delegation
○ State persistence
○ Long-running context management
○ Context preservation across workflows
◆ Provenance & Built-in Tools
○ Preserving provenance across multi-agent synthesis
○ Built-in tools
○ Data traceability
○ Module 1–6 consolidation
Module 07: Domain Consolidation & Exam-Weighted Review
◆ Exam Domain Review
○ Domain 1 — Agentic Architecture & Orchestration — 27%
○ Domain 2 — Tool Design & MCP Integration — 18%
○ Domain 3 — Claude Code Configuration & Workflows — 20%
○ Domain 4 — Prompt Engineering & Structured Output — 20%
○ Domain 5 — Context Management & Reliability — 15%
◆ Scenario-Based Review
○ Full scenario read-through
○ Cross-domain concept integration
○ Exam-focused problem solving
○ Key concept revision across all domains
Module 08: Applied Practice & Capstone Exercises
◆ Capstone 1 — Multi-Tool Agent
○ Multi-tool agent design
○ Tool description disambiguation
○ Tool selection strategies
◆ Capstone 2 — Error Handling
○ Structured errors
○ Escalation hooks
○ Error recovery workflows
◆ Capstone 3 — Claude Code
○ Claude Code team configuration
○ Team-scale development workflow
○ Configuration best practices
◆ Capstone 4 — Multi-Agent Research
○ Structured extraction pipeline
○ Multi-agent research pipeline
○ Provenance preservation
○ Integrated multi-domain implementation
Module 09: Exam Simulation & Final Review
◆ Guided Exam Review
○ Sample questions 1–30
○ Answer analysis
○ Distractor analysis
○ Scenario-based question review
◆ Full Practice Exam
○ Sample questions 31–76
○ Full 76-question timed practice exam
○ Domain-scored assessment
◆ Final Preparation
○ Targeted re-drill
○ Final review
○ Out-of-scope topics check
○ Exam logistics briefing
○ Final exam preparation and strategy
FAQ’s – Claude AI Solution Architect Live Training Course
1. What is the Claude AI Solution Architect course?
This course focuses on designing and building enterprise-grade AI applications using Claude, with practical concepts covering AI architecture, solution design, LLM integration, security, scalability, and real-world enterprise use cases.
2. Who can enroll in this course?
Software developers, solution architects, AI/ML professionals, cloud engineers, technical leads, data professionals, and working professionals interested in building enterprise AI applications can enroll.
3. Do I need prior AI experience?
Basic knowledge of software development and cloud or AI concepts is helpful, but the course is designed to guide learners through the key concepts required to design enterprise AI solutions.
7. Will class recordings be available?
Yes. Session recordings and course materials will be shared so you can revise the topics at your own pace if you miss a live session.
5. What is the fees for this course?
The course fee is ₹9,900 (or 119 USD for international learners) for the full 45+ hour live program.
How can I enroll for this course?
OR
For any other details, Call me or Whatsapp me on +91-9133190573
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR13000 INR 9900 Rupees