Claude AI Solution Architect — Designing Enterprise AI Applications
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.
Instructor: Ananth · 4+ Years Teaching Experience · 250+ Students Trained
- 45 Hours Live Training
- 1 Year Recorded Video Access
- Course Completion Certificate
- Hands-on Labs Every Module
- Community & Mentoring Support
- 4 Capstone Exercises + Practice Exam
Claude AI Solution Architect Course — CCAR-F Exam Preparation Summary
Isha Training Solutions offers a live, instructor-led Claude AI Solution Architect Course covering Claude API development, the Claude Agent SDK, Model Context Protocol (MCP) server design, agentic architecture and multi-agent orchestration, Claude Code configuration and CI/CD workflows, advanced prompt engineering and structured output, batch API processing, context management, error handling and escalation design, and provenance tracking. This 9-module, 45-hour weekday (Monday–Friday) program is built directly from the official Anthropic exam blueprint and is fully aligned to the Claude Certified Architect — Foundations (CCAR-F) certification, covering all 5 weighted exam domains (Agentic Architecture & Orchestration, Tool Design & MCP Integration, Claude Code Configuration & Workflows, Prompt Engineering & Structured Output, and Context Management & Reliability) and all 8 real-world certification scenarios. The course is taught by Ananth, an experienced technology professional and trainer with 4 years of teaching experience who has trained 250+ students, and is priced at ₹9,900 (approximately $119 USD) with a free live demo session on 9th September 2026. Ideal for solution architects, AI/backend engineers, and technical leads based in India, the USA, the UK, and worldwide who want to design, build, and evaluate production-grade Claude applications and pass the CCAR-F certification exam.
Built Around the 5 Official CCAR-F Exam Domains
Agentic Architecture & Orchestration (27%)
Agentic loops, the Claude Agent SDK, hub-and-spoke multi-agent orchestration, and context passing.
Tool Design & MCP Integration (18%)
Reliable tool interfaces, JSON Schema structured output, and production MCP servers with structured error handling.
Claude Code Configuration & Workflows (20%)
CLAUDE.md hierarchies, path-scoped rules, custom Skills, planning mode, and CI/CD integration.
Prompt Engineering & Structured Output (20%)
Few-shot prompting, prompt chaining, validation-retry loops, and Batch API strategy for cost-efficient output.
Context Management & Reliability (15%)
Escalation design, error-propagation, provenance tracking, and long-running context management.
CCAR-F Exam Readiness
Full coverage of all 8 certification scenarios, a 76-question timed practice exam, and domain-scored review drills.
What This Course Sets Out to Do
By the end of this program, you will have the knowledge and hands-on practice to:
Understand Claude's Architecture
Build a working mental model of the Claude API, Claude Agent SDK, and how agentic loops reason, plan, and call tools.
Design Multi-Agent Systems
Architect coordinator/subagent and hub-and-spoke orchestration patterns for real enterprise workflows.
Build Reliable Tools & MCP Servers
Define tool schemas and MCP server integrations that route model behavior predictably, with structured error handling.
Configure Claude Code for Teams
Set up CLAUDE.md hierarchies, path-scoped rules, custom Skills, and CI/CD pipelines for production engineering teams.
Engineer for Structured Output
Apply prompt chaining, JSON Schema validation-retry loops, and Batch API strategies for dependable, cost-efficient results.
Architect for Production Reliability
Design context-management, escalation, and error-propagation strategies that hold up under real-world load.
Evaluate & Govern AI Solutions
Apply provenance tracking and governance practices to responsibly evaluate and monitor deployed Claude applications.
Pass the CCAR-F Certification Exam
Walk into the Claude Certified Architect — Foundations exam fully prepared across all 5 weighted domains and 8 scenario types.
Attend a Free Introductory Session — 9th September 2026
Experience the quality of live training before enrolling. Choose your timezone below.
India (IST)
9th September 2026
8:30 PM – 9:30 PM IST
USA (EST)
9th September 2026
11:00 AM – 12:00 PM EST
UK (BST)
9th September 2026
4:00 PM – 5:00 PM BST
Weekday Batch — Live Sessions
Every Monday to Friday, 1 hour/day — 45 contact hours over 9 weeks.
| Region | Days | Timing | Timezone |
|---|---|---|---|
| 🇮🇳 India | Monday – Friday | – | IST |
| 🇺🇸 USA | Monday – Friday | – | EST |
| 🇬🇧 UK | Monday – Friday | – | BST |
9-Module CCAR-F Exam Preparation Curriculum
Course Code: CCAR-F-101 · 9 Weeks · 45 Contact Hours (1 hour/day, 5 days/week) · Instructor-led or Self-paced. Built directly from the official Anthropic exam blueprint, mapped to all 5 weighted exam domains and the same 8 real-world scenarios used in the certification exam.
API Fundamentals & Tool Design Domain 2, 4 · 5h
▼Learning objectives: Understand the Claude API request/response cycle and design reliable, unambiguous tool interfaces.
- API request structure & message roles
stop_reasonas the agentic control signal- System prompt priority & context window risks
- Tool description quality & disambiguation
tool_choice& JSON Schema structured output
🔬 Practical Task: Construct a full request body with system prompt, tool_use & tool_result placement; design a schema with an enum + "other", a nullable field, and a justified required field.
Claude Agent SDK & Model Context Protocol Domain 1, 2 · 5h
▼Learning objectives: Build multi-agent systems with correct context isolation, and integrate MCP servers with structured error handling.
- The agentic loop lifecycle
- AgentDefinition & hub-and-spoke orchestration
- Task tool, context passing & parallel spawning
- Agent SDK hooks — PostToolUse / PreToolUse
- MCP servers, config scope & structured errors
🔬 Practical Task: Design a 3-subagent system with explicit context, plus a working .mcp.json entry with env-var auth and a structured error response.
Claude Code Configuration & Workflows Domain 3 · 5h
▼Learning objectives: Configure Claude Code for team-scale development and CI/CD pipelines.
- CLAUDE.md hierarchy & @path imports
- .claude/rules/ path-scoped conventions
- Slash commands & Skills
- Planning mode vs. direct execution
- CI/CD integration & session management
🔬 Practical Task: Diagnose a CLAUDE.md hierarchy misconfiguration and sketch a CI script using -p --output-format json --json-schema.
Prompt Engineering & Batch Processing Domain 4 · 5h
▼Learning objectives: Engineer prompts for consistent, structured, and cost-efficient output.
- Few-shot prompting (5 example categories)
- Explicit criteria vs. vague instructions
- Prompt chaining, the interview pattern & retry loops
- Self-correction & Batch API mechanics
- Batch SLA planning + Module 1–4 consolidation
🔬 Practical Task: Design a validate-retry loop with its failure case; classify 4 workloads as sync vs. batch.
Task Decomposition, Escalation & Error Handling Domain 1, 5 · 5h
▼Learning objectives: Design reliable workflows for open-ended tasks, human escalation, and multi-agent error recovery.
- Fixed pipelines vs. dynamic decomposition
- Multi-pass code review
- Escalation triggers & handoff protocols
- Confidence calibration & human oversight
- Error categories & multi-agent error handling
🔬 Practical Task: Write a 5-step adaptive decomposition plan and design a structured subagent error object.
Context Management, Provenance & Built-in Tools Domain 5, 2 · 5h
▼Learning objectives: Manage long-running context reliably and preserve data provenance across multi-agent synthesis.
- Persistent facts blocks & tool-result trimming
- Position-aware input & scratchpad files
- Subagent delegation & state persistence
- Preserving provenance across multi-agent synthesis
- Built-in tools + Module 1–6 consolidation
🔬 Practical Task: Write a "case facts" block with a trimming hook and a structured conflicting-data object with attribution.
Domain Consolidation — Exam-Weighted Review All 5 Domains · 5h
▼Learning objectives: Re-integrate all prior material through the lens of the 5 official exam domains.
- 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%) + full scenario read-through
📋 Re-integrates all prior material through the lens of the 5 official exam domains and their weighting.
Applied Practice — 4 Capstone Exercises All 5 Domains · 5h
▼Learning objectives: Apply all prior modules in integrated, multi-domain builds.
- Capstone 1A — Multi-tool agent with description disambiguation
- Capstone 1B — Structured errors & escalation hook
- Capstone 2 — Claude Code team configuration
- Capstone 3 — Structured extraction pipeline
- Capstone 4 — Multi-agent research pipeline with provenance
🔬 Apply every prior module in integrated, multi-domain builds spanning the full CCAR-F blueprint.
Exam Simulation & Final Review All 5 Domains · 5h
▼Learning objectives: Convert knowledge into exam performance under timed, scenario-based conditions.
- Guided review — sample questions 1–30 (answer + distractor analysis)
- Guided review — sample questions 31–76
- Full 76-question timed practice exam
- Domain-scored review & targeted re-drill
- Final review, out-of-scope topics check & exam logistics briefing
Assessment Structure
How your progress and exam-readiness are measured across the program.
| Component | Weight | Description |
|---|---|---|
| Module hands-on tasks (1–6) | 30% | Completion and quality of each module's practical task |
| Capstone exercises (Module 8) | 30% | 4 integrated builds spanning multiple domains |
| Practice exam score (Module 9) | 30% | 76-question timed practice test, scored by domain |
| Participation / peer review | 10% | Engagement in review sessions (instructor-led delivery only) |
A participant is considered exam-ready when they score consistently above 80% on the domain-scored practice exam (Module 9.3–9.4), with no single domain below 70%.
Materials & Resources
Official Exam Guide (Anthropic Partner Academy / Skilljar) — blueprint, 8 scenarios, domain task statements
Study Guide (13 chapters) — theory, domain notes, 76 sample questions, 4 practical exercises
Official documentation: Claude API, Claude Agent SDK, Model Context Protocol, Claude Code
Official practice exam (Anthropic Partner Academy access-request flow)
Supplementary practice test (community-maintained, 76 questions)
Certification Logistics
Covered in full during Module 9.5 — everything you need to know about sitting the official CCAR-F exam.
| Parameter | Value |
|---|---|
| Exam format | 60 questions, 4 of 8 scenarios drawn at random |
| Scoring | 100–1000 scale, passing score 720 |
| Guessing penalty | None |
| Fee | $125 USD |
| Validity | 12 months (free non-proctored renewal if done on time) |
| Retake policy | 14-day wait after 1st fail, 30 after 2nd, 90 after 3rd; max 4 attempts per rolling 12 months |
| Registration | Anthropic Partner Academy → Pearson VUE scheduling |
Delivery Notes for Instructors & Self-Paced Learners
Pacing: Domains 1, 3, and 4 make up 67% of the exam weighting — if the course must be shortened, Module 7's domain-mapping days and the Module 1–6 "consolidation" sessions are compressed before touching Modules 8 or 9.
Assessment emphasis: The exam tests judgment (root cause over symptom, deterministic over probabilistic, scoped fix over overengineering), not recall. Practical task quality is weighted over reading completion in any qualitative review.
Cohort format: Modules 7–9 work well delivered as instructor-led synchronous sessions even if Modules 1–6 are self-paced, since they synthesize rather than introduce new material.
Self-paced format: Participants studying independently should still timebox each session to ~1 hour and attempt the practical task before reviewing the reference material, per the course's task-first design.
4 Capstone Exercises to Build Your Portfolio
Module 8 applies every prior module in integrated, multi-domain builds spanning the full CCAR-F blueprint.
Multi-Tool Agent
Build a multi-tool agent with clean tool description disambiguation to reliably route model behavior.
Structured Errors & Escalation Hook
Design structured error objects and an escalation hook for reliable human handoff protocols.
Claude Code Team Configuration
Configure CLAUDE.md hierarchies, path-scoped rules, and CI/CD integration for a team environment.
Structured Extraction Pipeline
Engineer a validation-retry pipeline for reliable, schema-based structured output at scale.
Multi-Agent Research Pipeline with Provenance
Orchestrate a multi-agent research pipeline with hub-and-spoke coordination and full data provenance tracking — ready to showcase.
Skills You'll Master
This Course Is Designed For
Solution Architects
Architects who design or ship applications built on Claude and need to reason about production trade-offs.
AI / Backend Engineers
Engineers building agentic systems, tool integrations, and Claude-powered production applications.
Technical Leads
Leads who need to evaluate, govern, and make sound architectural decisions on Claude-based AI systems.
What You Need Before You Start
Working proficiency in at least one general-purpose programming language (Python or JavaScript/TypeScript recommended)
Basic familiarity with REST APIs and JSON
Recommended: 6 months of hands-on exposure to LLM-based application development (not mandatory, but strongly improves pacing)
Not required: Prior certification, machine learning background, or prior Claude-specific experience
What You'll Achieve by the End
On completing this course, participants will be able to:
Design and implement agentic loops and multi-agent (coordinator/subagent) architectures using the Claude Agent SDK
Write tool and MCP server definitions that reliably route model behavior, including structured error handling
Configure Claude Code for team environments — CLAUDE.md hierarchies, path-scoped rules, custom skills, and CI/CD integration
Engineer prompts and JSON schemas for reliable structured output, including validation-retry loops and batch processing strategy
Design escalation, error-propagation, and context-management strategies for production reliability
Sit the CCAR-F certification exam with full coverage of all 8 scenario types and all 5 weighted domains
About Your Instructor
Ananth is an experienced technology professional and trainer with a passion for helping software engineers and aspiring AI professionals accelerate their careers. With 15+ years of overall industry experience and 4 years of teaching experience, he has successfully trained 250+ students, helping them build practical technical skills and grow with confidence.
He brings diverse experience across technology domains including Deep Tech, SaaS, Enterprise, Startups, Telecom, Legal Compliance, AML & Fraud, and EdTech. His expertise includes problem-solving, product thinking, leadership, strategic communication, people management, and career development.
Currently, Ananth is teaching the Claude AI Solution Architect course, helping students understand modern AI concepts and develop practical skills for real-world AI applications. His industry knowledge and engaging teaching approach make learning more practical, career-focused, and relevant to today's evolving technology landscape.
What Our Students Say About Ananth
"Ananth's teaching style makes even complex concepts feel intuitive. His real-world examples from enterprise AI environments are what set him apart from other trainers."
"The hands-on labs in every module made all the difference. I came in as a developer and left ready to design complete AI solutions. Highly recommend to anyone in tech."
"Excellent course structure and a truly knowledgeable instructor. Ananth covers not just the how, but the why — and that makes you a much better architect."
"The recorded videos with 1 year access were a lifesaver. I could revisit complex topics at my own pace. This is the most practical AI course I've taken."
"After completing this course I landed a senior AI engineer role. The capstone project was directly transferable as a portfolio piece. Worth every rupee."
"Outstanding content. The MCP and Agentic AI modules were game-changers for my understanding of enterprise AI architecture. Thank you Ananth sir!"
Earn Your Certificate of Completion
Every participant who successfully completes the Claude AI Solution Architect — Designing Enterprise AI Applications Training receives an official certificate from Isha Training Solutions — recognised by industry professionals.
Sample certificate — your name will be printed upon completion
Official & Verifiable
Issued by Isha Training Solutions (ISO 9001:2015 Certified, Est. 2016) with a unique registration number — shareable and verifiable by employers.
Dual Signature Authority
Signed by both Kumar Gupta (CEO & Founder) and Bagya Gattu (Director) — giving it full institutional recognition.
Resume & LinkedIn Ready
Showcase your Claude AI architecture expertise — MCP, Agentic AI, RAG, and Enterprise AI Governance — on LinkedIn and your resume.
Questions? Reach Us
Call or WhatsApp: +91-9133190573 / +91-8977922427
Batch Policy — Please Read Before Enrolling
To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.
Moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.
We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.
Payment Options
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Once your payment is completed, please send your payment screenshot, full name, and WhatsApp number to +91 73962 83617 via WhatsApp to confirm your enrollment.
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| Account Number | 567405000109 |
| Bank Name | ICICI Bank |
| Branch | RR Dadi Arcade, ECIL, Kapra, Secunderabad – 500062, Telangana, India |
| Account Type | Current |
| IFSC Code | ICIC0005674 |
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