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Claude Enterprise Architecture and Governance
Duration
3 Days
Course
CL-400
Available Formats
Classroom Training, Online Training
Course Description
Overview
This Claude Enterprise Architecture and Governance course is designed for architects who own Claude programs rather than individual Claude systems. It is built against the published Claude Certified Architect – Professional (CCAR-P) blueprint, which spans seven domains: solution design, models and context engineering, integration, evaluation and optimization, governance and risk, stakeholder communication and lifecycle management, and developer productivity. The heaviest of these is Integration at 19%, and it reaches into territory the Foundations pathway does not cover — retrieval architecture, protocol selection, authorization boundaries, and observability across many teams. The course is design-led and every design is proved: participants build the retrieval pipeline they specify, instrument the system they claim is observable, and defend the architecture they document to an executive panel.Objectives
Upon completion of the Claude Enterprise Architecture and Governance course, students will be able to:
- Translate a business problem into an end-to-end Claude program architecture aligned to stated value pillars and performance SLAs
- Select the integration mechanism — MCP, API or CLI, agent-to-agent — and justify the choice against authorization, latency, and observability requirements
- Design and build a RAG pipeline with chunking, indexing, and retrieval strategies matched to data shape and query pattern
- Design evaluation, observability, and A/B testing practice for Claude systems operating across many teams and many agents
- Implement guardrails, human-in-the-loop thresholds, and compliance controls that satisfy GDPR, HIPAA, or FedRAMP obligations
- Conduct structured discovery and present architectural trade-offs, cost, and SLA commitments to executive stakeholders
- Manage the program lifecycle from pilot through production to deprecation, including model migration playbooks and version pinning
- Configure Claude Code for a large developer organization using shared skills, shared MCP servers, and path-scoped rules
- Produce an architecture decision record and a governance package that a client's risk function can review
Audience
AI and machine learning engineers, technical leads, and senior software engineers who build, integrate, and ship Claude-powered applications, agents, and workflows, and who are preparing for the Claude Certified Developer – Foundations (CCDV-F) exam.
Prerequisites
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Anthropic recommends roughly three years in systems architecture or platform engineering and six or more months of hands-on experience running Claude or comparable LLM systems in production; the CCAR-P exam itself carries no mandatory prerequisite. Participants should arrive able to implement an agentic loop, a tool interface, and an MCP server without instruction.
The recommended learning track for the CCAR-P exam is Claude Foundations or Claude Foundations Accelerated, then Claude Practitioner for Solutions Architects, then Claude Agentic Systems, or equivalent production experience.
Topics
- Translating a business problem into a Claude program aligned to value pillars: efficiency, transformation, productivity, cost, and performance SLAs
- Architectural pattern selection across a portfolio: workflow, agentic, and augmented LLM — and the cases where the answer is not Claude
- End-to-end architecture: input, processing, output, and the feedback loop that keeps the system honest
- Decomposition at two levels: a program into systems, a system into agents (single-system agent design is covered in CL-300)
- Lab: take an enterprise brief to a reference architecture and write the architecture decision record that defends it
- Protocol selection: MCP, API and CLI, and agent-to-agent, and the criteria that actually decide between them
- Shared MCP servers as enterprise infrastructure: ownership, versioning, deprecation, and access boundaries (server construction is covered in CL-300)
- Capability bloat and least privilege: auditing an agent's tool surface against the role it genuinely serves
- Authentication and authorization: identity propagation, delegated credentials, and the gaps that surface only at scale
- Progressive discovery versus monolithic context as an integration decision, not a prompting one
- Accuracy and latency trade-offs, and how to justify a configuration decision to a reviewer who will push back
- Hands-on lab: audit an over-tooled production agent, cut its capability surface, and defend what remains
- RAG pipeline design as an architectural decision: chunking, indexing, and embedding choices and what each one costs
- Matching retrieval strategy to data shape and query pattern rather than to habit
- Data residency across Amazon Bedrock and Google Vertex AI, and the constraints residency places on the pipeline
- Failure modes of retrieval: stale indexes, mismatched embeddings, and confident wrong answers after a document refresh
- Hands-on lab: build a RAG pipeline over an enterprise corpus, then measure retrieval quality before and after a chunking change
- Evaluation metrics that survive a steering committee: accuracy, latency, cost, safety, and security
- Designing evaluation datasets and mixed-method test frameworks, including golden sets and adversarial sets
- A/B testing in production and reading the result honestly
- Observability across many teams and many agents: logging, tracing, and the monitoring strategy that scales
- Diagnosing a live system: prompt failure, retrieval failure, hallucination, and model mismatch
- Cost and token optimization across a portfolio: prompt caching, model routing, and the Batch API trade-off
- Hands-on lab: instrument a system, run an A/B test on a retrieval or prompt change, and report the result to a business owner
- Guardrail architecture: where a control belongs — in the prompt, the tool, the application, or a human
- Risk registers for LLM systems: failure modes, likelihood, and blast radius
- Human-in-the-loop validation strategies and the thresholds that should trigger them
- Compliance as a design constraint: GDPR, HIPAA, and FedRAMP obligations expressed as architecture
- Red-team programs and DPIA-style assessment templates as recurring practice rather than one-time paperwork
- Bias, fairness, and transparency: what to test for, and what to disclose to whom
- Lab: complete a risk and impact assessment for a proposed deployment and rule on whether it ships
- Structured discovery and requirements gathering with business owners who have heard only the marketing
- Presenting an architecture to a CIO: trade-offs, cost, SLA commitments, and an explicit statement of what you are not promising
- The Discover, Develop, Embed, and Scale framework applied to a Claude program
- Lifecycle phases: pilot, production, monitoring, iteration, and deprecation
- Model migration playbooks and version pinning: absorbing model change without breaking the estate
- Incident post-mortems and the feedback loop back into design
- Architecture documentation and implementation guidance a delivery team can act on without a meeting
- Lab: deliver a ten-minute architecture briefing to an executive panel and defend the cost, SLA, and risk decisions under challenge
- Scaling Claude Code across teams: shared skills, shared MCP servers in .mcp.json, and path-scoped rules
- CLAUDE.md hierarchy as an organizational standard rather than a project file
- Rollout, adoption measurement, and the support model for debugging and operational issue resolution
- Lab: design the Claude Code configuration standard for a five-hundred-developer organization and pilot it on a sample repository
- Take an enterprise brief in a regulated industry from discovery to a program architecture spanning multiple business units
- Specify the integration and retrieval architecture, then build the component that carries the most risk and measure it
- Produce the governance package: risk assessment, guardrails, human-review thresholds, and compliance mapping
- Write the lifecycle plan: pilot criteria, production gates, monitoring, model migration, and deprecation
- Present to an executive panel and defend the cost, SLA, and risk decisions against challenge
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