How to Become a Claude Architect
Chapter 7 / 78 min read

Part 7: Build Exercises & Resources

Reading is not enough. You become a Claude Architect by building. This chapter gives you structured exercises that cover every domain, plus the resources to go deeper.

01 Capstone Project: Customer Support Resolution Agent

This single project touches all five domains. Build it end to end.

Requirements

ComponentDomain
Coordinator + 2 subagents (lookup + resolution)Domain 1: Orchestration
MCP tools for customer lookup, order lookup, refund processingDomain 2: Tool Design
CLAUDE.md with team standards, .claude/rules/ for test filesDomain 3: Configuration
Explicit escalation criteria, few-shot examples for edge casesDomain 4: Prompt Engineering
Persistent case facts block, structured error propagationDomain 5: Context Management

Architecture

Customer Message
       │
       ▼
┌─────────────────┐
│   Coordinator    │
│                  │
│  - Decompose     │
│  - Route         │
│  - Synthesise    │
└───────┬──────────┘
   ┌────┴────┐
   ▼         ▼
┌────────┐ ┌──────────┐
│ Lookup │ │Resolution│
│ Agent  │ │  Agent   │
└────────┘ └──────────┘

Implementation Checklist

  • Agentic loop with proper stop_reason handling
  • Subagents with isolated context (no shared memory)
  • Structured context passing with claim-source mappings
  • Programmatic prerequisite gate: block refund until account verified
  • PostToolUse hook: normalise date formats across tools
  • Tool call interception hook: block refunds above $500
  • Persistent case facts block (never summarised)
  • Structured handoff protocol for human escalation
  • Four error categories with proper isRetryable flags
  • Valid empty result vs access failure distinction

Test Scenarios

  1. Happy path: customer requests refund for defective product, under $500, account verified
  2. Prerequisite gate test: attempt refund without account verification — should be blocked
  3. Hook test: refund request above $500 — should redirect to human escalation
  4. Multi-concern request: customer has a billing issue AND a product question — both handled
  5. Ambiguous customer: search returns multiple matches — agent asks for clarification
  6. Explicit human request: customer says "I want a human" — immediate escalation, no investigation

02 Capstone Project: Multi-Agent Research System

Requirements

Build a coordinator with three subagents:

SubagentRoleTools (4-5 max)
Web SearchFind current informationsearch_web, fetch_page, extract_quotes
Document AnalysisAnalyse uploaded documentsread_document, extract_data, summarize_section
SynthesisCombine findings into reportverify_fact, format_output

Implementation Checklist

  • Coordinator decomposes broadly (test with "impact of AI on creative industries" — must cover music, writing, film, not just visual arts)
  • Parallel subagent spawning for web search and document analysis
  • Structured metadata passed between agents (source URL, document name, page number, publication date)
  • Synthesis agent preserves attribution through to final output
  • Conflicting sources annotated with both values, not arbitrarily resolved
  • Coverage annotations for areas with limited data
  • Iterative refinement: coordinator evaluates synthesis, re-delegates for gaps

03 Capstone Project: CI/CD Code Review Pipeline

Requirements

ComponentWhat to Build
Non-interactive review-p flag with --output-format json
Independent review instanceSeparate from code generation session
Multi-pass architecturePer-file analysis + cross-file integration
Incremental reviewsOnly report new or unaddressed issues

Implementation Checklist

  • CI script using claude -p with JSON schema output
  • CLAUDE.md with testing standards and available fixtures
  • Path-specific rules for test files (**/*.test.tsx) and API files (**/api/**)
  • Few-shot examples for severity calibration
  • Explicit criteria: what to flag vs what to skip
  • Independent review instance (not same session as code generation)
  • Incremental review context: prior findings included, only new issues reported

04 Capstone Project: Structured Data Extraction Pipeline

Requirements

ComponentWhat to Build
JSON schema with tool_useRequired, optional, nullable fields, enums with "other"
Validation-retry loopSend back errors, model self-corrects
Batch processingMessage Batches API for bulk documents
Confidence routingLow-confidence fields routed to human review

Implementation Checklist

  • Tool definition with comprehensive JSON schema
  • Optional/nullable fields for data that may be absent (prevents fabrication)
  • "unclear" enum value for ambiguous cases
  • Validation-retry loop: catches format mismatches and semantic errors
  • Distinguish retriable errors from genuinely absent information
  • Batch processing via Message Batches API with custom_id
  • Failure handling: resubmit only failed documents
  • Field-level confidence scores with calibrated thresholds
  • Few-shot examples for varied document formats (tables, narrative, citations)

05 Quick Reference: Domain Weights and Key Concepts

DomainWeightSingle Most Important Concept
1. Agentic Architecture27%Subagents have isolated context — pass everything explicitly
2. Tool Design & MCP18%Tool descriptions are the primary selection mechanism
3. Claude Code Config20%CLAUDE.md hierarchy — project-level for team standards
4. Prompt Engineering20%Be explicit — concrete criteria beat vague instructions
5. Context & Reliability15%Never summarise transactional data — use persistent case facts

06 Study Resources

Official Anthropic Documentation

ResourceWhat It Covers
Agent SDK OverviewAgentic loop mechanics, subagent patterns
Building Agents with the Claude Agent SDKHooks, orchestration, sessions
Agent SDK Python repo + examplesHands-on code: hooks, custom tools, fork_session
MCP Integration for Claude CodeServer scoping, env var expansion, project vs user config
MCP specification + community serversProtocol understanding, when to use community vs custom
Claude Agent SDK TypeScript repoTool definition patterns, structured errors
Claude Code official docsCLAUDE.md hierarchy, rules directory, slash commands
Anthropic Prompt Engineering docsFew-shot patterns, explicit criteria, structured output
Anthropic API Tool Use documentationtool_use, tool_choice config, JSON schema enforcement
  1. Building with the Claude API — core API mechanics
  2. Introduction to Model Context Protocol — MCP fundamentals
  3. Claude Code in Action — practical CLI usage
  4. Claude 101 — general Claude capabilities

Community Resources

ResourceWhat It Covers
Claude Code CLI CheatsheetCommands, skills, hooks, CI/CD flags
Creating the Perfect CLAUDE.mdReal team configuration patterns, MCP integration
Everything Claude Code repoContext management patterns, scratchpad files, compaction

07 The Exam Anti-Pattern Cheat Sheet

Keep these in mind across all domains:

Anti-PatternWhy It Is WrongCorrect Approach
Parsing natural language for loop terminationAmbiguous and unreliableCheck stop_reason field
Arbitrary iteration capsCuts off useful work or wastes iterationsLet model signal via stop_reason
Assuming subagents share memoryThey do notPass everything explicitly in prompt
Prompt-based enforcement for high-stakesNon-zero failure rateProgrammatic hooks and gates
Vague tool descriptionsCauses misroutingSpecific descriptions with boundaries
"Be conservative" as criteriaInconsistently appliedDefine explicit categories and examples
Sentiment-based escalationFrustration ≠ complexityUse the three valid triggers
Summarising transactional dataLoses critical numbers and datesPersistent case facts block
Single-pass review of many filesAttention dilutionMulti-pass: per-file + cross-file
Same session for generation and reviewReasoning biasIndependent review instance
Batch API for blocking workflowsNo latency SLASynchronous for blocking, batch for overnight

Now go build. You do not need a certificate to be a Claude Architect. You need the knowledge and the projects to prove it.