Take this into your next conversation
The lesson in three points
- Start with the promise, people, trust boundaries and uncertainty.
- Use the seven-step method before selecting components.
- Practice an answer for an interview, then test the same reasoning in a design review.
Free member resources · Sign-in required for downloads
- Clarify the promise
- Map the journey
- Define boundaries
- Design & stress-test
Try it before you read
Teaching scenario: design an AI-enabled checkout. Before drawing components, describe what “order confirmed” promises and how you handle an uncertain payment result.
You can recognize every box in an architecture diagram and still struggle when someone asks: “Why this design—and what happens when the assumptions change?”
That is the problem I wrote System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems to help you work through. The goal is to connect the customer’s problem, the system’s boundaries, and the decisions you need to explain.
If you are preparing for an interview or an architecture review, start here. This overview explains the book’s structure, shows how to practice its method, and connects the reading to an interactive refund challenge.
Two free, fillable companions support that practice: an Interview Practice Guide and a System Design & Architecture Review Prep Pack. Downloads require a free AI Architect Circle account.
Before you read: what does “confirmed” promise?
Imagine you are designing an AI-enabled checkout.
Before choosing a model, database, or message broker, answer two questions:
* What must be true before the customer sees “order confirmed”?
* What should the customer see if the payment request times out and its outcome is unknown?
Keep your answers nearby. We will return to these questions throughout the guide.
What is System Design with AI Interview Guide about?
The book brings system design reasoning and AI architecture into the same conversation. It starts with requirements, people, and business capabilities, develops the technical foundations, and applies them to familiar systems.
The recurring question is practical: can you explain why a decision fits its constraints, where it can fail, and what evidence would make you change it?
Published by Apress in 2026, the book has 24 chapters across three parts. The chapter map below helps you find the material you need, whether you are rehearsing an interview, examining an AI agent’s authority, or preparing a design review.
See the publisher’s book details and complete chapter list
Who is the book for?
Interview candidates can use the method to structure an answer and practice follow-up questions.
Software engineers and architects can use it to connect implementation choices to business behavior.
Technical leaders can use the same questions to make review conversations more precise.
You can begin with the methodology before diving into AI-specific material. If you already work with distributed systems, use the foundations and case studies to challenge your assumptions about consistency, failure, latency, and operating cost.
Choose a reading path around a problem you can explain in your own words.
The three-part learning path connects the method, technical foundations, and applied system designs.
Part I: a method for thinking before drawing
Chapters 1–5 establish the seven-step method, the business domain, the customer journey, business capabilities, and architecture structure through ARCHVIEW.
The method gives your discussion an order:
1. Clarify requirements and constraints. Agree on the problem, success criteria, scale, and assumptions.
2. Understand the domain and users. Establish the actors, rules, and language of the business.
3. Empathize with the customer journey. Follow the experience through success, delay, and failure.
4. Define business capabilities and boundaries. Identify responsibilities before selecting products.
5. Design high-level architecture with ARCHVIEW. Make structure, behavior, decisions, and responsibilities visible.
6. Deep-dive critical components. Examine the decisions where failure would be most expensive.
7. Validate, scale, and stress-test. Challenge the design with changed constraints and failure cases.
The order matters because each step reduces uncertainty for the next. Choosing components before understanding the domain can produce a technically coherent solution to the wrong problem. Skipping the customer journey can leave you optimizing a path that does not address the customer’s need.
For example, “add a queue” becomes a stronger answer when you explain which capability owns the work, what pending means to the customer, and what happens when execution is delayed. A technology choice gains meaning from the behavior it supports.
Part II: the foundations, including what AI changes
Chapters 6–13 connect AI architecture with system design fundamentals. Three ideas help organize the AI discussion.
AI architecture primitives are recurring design concerns: authority, commitment, time, degradation, evidence and replay, and ownership.
They prompt concrete questions:
* Who may act?
* When does a proposal become a consequence the business owns?
* Which clock or expiry governs the decision?
* How should behavior change when certainty drops?
* What evidence lets us explain or reconstruct the decision?
* Who can stop the system or reduce what it is allowed to do?
LLM-SHIELD examines Scope, Harden, Isolate, Empower, Live-Defend, and Demonstrate. It gives you a way to discuss the boundaries and lifecycle of AI capabilities. The useful output is a specific control, responsibility, or test you can explain.
ROCS—Reliability, Observability, Cost, and Sustainability—connects design decisions to operation. A retry policy, for instance, affects availability, evidence, resource use, and cost together.
The remaining foundations cover scale, consistency, latency, failure, and economics. AI introduces additional uncertainty, while these chapters keep the underlying engineering questions in view.
Quick check: what authorizes a refund?
An AI model is 99% confident that a customer should receive a $5,000 refund. What authorizes the action?
* A: The model’s confidence score.
* B: A second model agreeing.
* C: Verified eligibility, permissions, limits, current state, and any required approval.
The answer is C. The execution service must enforce those conditions. Any required approval should cover the exact action, amount, recipient, and expiry.
A model can propose a refund. Trusted services authorize and execute it. If the relevant state or approval changes, the system must check again before acting.
The execution service verifies authority before applying the proposed refund.
Part III: apply the reasoning to recognizable systems
Chapters 14–23 work through ride-sharing, social networking, e-commerce, messaging, auctions, fraud detection, video streaming, payments, file storage, and search. Chapter 24 closes with designing for uncertainty.
Before reading a case study’s design, sketch your own. Then compare assumptions, boundaries, trade-offs, and recovery paths. The most useful difference may be a question you forgot to ask.
Across these systems, the vocabulary changes, but several questions return: where does the system commit, which evidence is authoritative, how are retries bounded, and who owns recovery?
A small example: where does a proposal become a change?
Consider a fictional order assistant that helps a customer change a delivery address before picking begins. Assume the customer can modify only their own eligible order. These are exercise assumptions to validate in a real system.
The assistant can collect details and propose a request. The order service owns the authoritative state and the commitment decision. It must enforce identity, ownership, and current eligibility when the change is applied.
The order service enforces eligibility at commitment and reconciles uncertain outcomes.
Now change one condition: picking begins after the assistant gathered the information. An earlier check is insufficient. The design needs concurrency controls, such as a transaction or conditional version update, that preserve the rule at commitment.
Next, assume the call times out. A missing response does not tell you whether the change happened. Preserve the operation identity and reconcile the service result before another attempt, following the actual API’s retry contract.
This example connects the customer journey, capability ownership, AI authority, consistency, failure, and evidence. That connection is the reasoning to practice.
Practice with “The Refund That Disappeared”
In the November 7 live session, we explored a fictional refund assistant that sends a $45 refund, loses the provider’s response, and retries. An unsafe recovery policy pays the customer $90.
The challenge makes the difference between a missing response and an actual business outcome visible.
A timeout leaves the caller uncertain. Reconcile the original operation before creating another effect.
Work through four activities:
1. Break it. Run the unsafe policy and inspect what the application observed and what the provider committed.
2. Repair it. Preserve the original operation identity, look up the outcome, and apply the provider’s retry and idempotency rules.
3. Test it. Change the parameters, expire the key, or make status lookup unavailable. Check whether the repair still protects the customer and the business.
4. Explain it. Describe the evidence behind the final state and name the person or team responsible for unresolved cases.
Keep uncertain outcomes pending. Report success only when authoritative evidence supports it. Bound the recovery attempts and provide an escalation path.
Idempotency also has a contract: its scope, retention period, and parameter rules matter. If a key has expired and the original outcome remains unknown, simply issuing a new key can create another financial effect. Resolve the original operation or escalate it before proceeding.
The challenge is free and requires sign-in. Newsletter subscription is optional. It uses a fictional provider, and no money moves.
What does “eval” mean here?
An evaluation tests behavior against explicit criteria using defined scenarios and evidence.
For the refund challenge, useful criteria include:
* An authorized refund can complete.
* A lost response does not cause a duplicate refund.
* Changed request parameters do not silently reuse an earlier approval.
* An unresolved outcome reaches a named recovery owner.
Preventing every action would block legitimate customer work. A useful evaluation checks both the actions the system must prevent and the work it must allow.
Practice includes both preventing unsafe actions and completing legitimate customer work.
Use the book with a practical reading loop
Read one section. Sketch one design. Defend one decision. Change one constraint. Revise your answer.
Write down what changed and why.
For interview preparation, begin with Chapters 1–5, attempt a relevant case study, then revisit the foundation your answer exposed as weak. Use the Interview Practice Guide for a timed rehearsal and a focused self-review.
For architecture review preparation, choose a current decision and identify its customer impact, invariant, owning service, options, and unresolved evidence. Use the separate Review Prep Pack to prepare the meeting and record its outcome.
When drawing, choose the view that answers the question. The C4 model offers context, container, component, and code views. A review of authority or commitment also needs those responsibilities made explicit on the relevant diagram.
Explore the C4 model and its diagram levels
The official companion repository includes chapter materials and practice resources, including a seven-step method, interview rubric, diagram guide, and architecture decision record template.
Open the official Apress companion repository
Download the two fillable prep guides
Interview Practice Guide — 4 pages
A 30-minute drill, worked answer, follow-up questions, explanation rubric, and six fillable worksheet fields. Use it to rehearse how you communicate a design.
System Design & Architecture Review Prep Pack — 5 pages
A chapter-to-review map, meeting agenda, worked decision, option comparison, boundary-test matrix, evaluation criteria, and six fillable decision-record fields. Use it to prepare a team review.
Download a copy before filling it in, and use a PDF reader that supports form fields. You can also print either guide. The examples are teaching scenarios; adapt the criteria to your context.
Watch the walkthroughs
Book walkthrough and live Q&A
Explore the book’s organization, method, foundations, and applied chapters, with audience questions about getting started, prompts, diagrams, and exercises.
Interactive introduction and refund simulation — November 7, 2026
Follow the seven-step method, examine what authorizes an AI action, and work through a refund whose outcome becomes uncertain after a timeout. See how recovery choices affect the result and how to test the repair.
Recording coming soon to the AI Architect Circle YouTube channel.
Get the book and continue the conversation
I go deeper into the method, foundations, and applied designs in System Design with AI Interview Guide. Choose a chapter, bring your own design to it, and practice explaining why your decisions fit the requirements.
* Get the book on Amazon
* Read the book on O’Reilly
* Join AI Architect Circle for sessions and architecture discussions
* Subscribe to AI Architect Circle on YouTube
Discussion: Which decision is hardest for you to defend today: where authority lives, how the system behaves under failure, or which trade-off to accept? Share a non-confidential example in the comments and the chapter or challenge you would use to examine it.
Continue with the LLM-SHIELD series
* Harden: turn prompts and policies into enforced boundaries
* Empower: delegate capability within explicit permissions
* Demonstrate: inspect what your evaluation actually proved
You can recognize every box in an architecture diagram and still struggle when someone asks: “Why this design—and what happens when the assumptions change?”
That is the problem I wrote System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems to help you work through. The goal is to connect the customer’s problem, the system’s boundaries, and the decisions you need to explain.
If you are preparing for an interview or an architecture review, start here. This overview explains the book’s structure, shows how to practice its method, and connects the reading to an interactive refund challenge.
Two free, fillable companions support that practice: an Interview Practice Guide and a System Design & Architecture Review Prep Pack. Downloads require a free AI Architect Circle account.
Before you read: what does “confirmed” promise?
Imagine you are designing an AI-enabled checkout.
Before choosing a model, database, or message broker, answer two questions:
* What must be true before the customer sees “order confirmed”?
* What should the customer see if the payment request times out and its outcome is unknown?
Keep your answers nearby. We will return to these questions throughout the guide.
What is System Design with AI Interview Guide about?
The book brings system design reasoning and AI architecture into the same conversation. It starts with requirements, people, and business capabilities, develops the technical foundations, and applies them to familiar systems.
The recurring question is practical: can you explain why a decision fits its constraints, where it can fail, and what evidence would make you change it?
Published by Apress in 2026, the book has 24 chapters across three parts. The chapter map below helps you find the material you need, whether you are rehearsing an interview, examining an AI agent’s authority, or preparing a design review.
See the publisher’s book details and complete chapter list
Who is the book for?
Interview candidates can use the method to structure an answer and practice follow-up questions.
Software engineers and architects can use it to connect implementation choices to business behavior.
Technical leaders can use the same questions to make review conversations more precise.
You can begin with the methodology before diving into AI-specific material. If you already work with distributed systems, use the foundations and case studies to challenge your assumptions about consistency, failure, latency, and operating cost.
Choose a reading path around a problem you can explain in your own words.
The three-part learning path connects the method, technical foundations, and applied system designs.
Part I: a method for thinking before drawing
Chapters 1–5 establish the seven-step method, the business domain, the customer journey, business capabilities, and architecture structure through ARCHVIEW.
The method gives your discussion an order:
1. Clarify requirements and constraints. Agree on the problem, success criteria, scale, and assumptions.
2. Understand the domain and users. Establish the actors, rules, and language of the business.
3. Empathize with the customer journey. Follow the experience through success, delay, and failure.
4. Define business capabilities and boundaries. Identify responsibilities before selecting products.
5. Design high-level architecture with ARCHVIEW. Make structure, behavior, decisions, and responsibilities visible.
6. Deep-dive critical components. Examine the decisions where failure would be most expensive.
7. Validate, scale, and stress-test. Challenge the design with changed constraints and failure cases.
The order matters because each step reduces uncertainty for the next. Choosing components before understanding the domain can produce a technically coherent solution to the wrong problem. Skipping the customer journey can leave you optimizing a path that does not address the customer’s need.
For example, “add a queue” becomes a stronger answer when you explain which capability owns the work, what pending means to the customer, and what happens when execution is delayed. A technology choice gains meaning from the behavior it supports.
Part II: the foundations, including what AI changes
Chapters 6–13 connect AI architecture with system design fundamentals. Three ideas help organize the AI discussion.
AI architecture primitives are recurring design concerns: authority, commitment, time, degradation, evidence and replay, and ownership.
They prompt concrete questions:
* Who may act?
* When does a proposal become a consequence the business owns?
* Which clock or expiry governs the decision?
* How should behavior change when certainty drops?
* What evidence lets us explain or reconstruct the decision?
* Who can stop the system or reduce what it is allowed to do?
LLM-SHIELD examines Scope, Harden, Isolate, Empower, Live-Defend, and Demonstrate. It gives you a way to discuss the boundaries and lifecycle of AI capabilities. The useful output is a specific control, responsibility, or test you can explain.
ROCS—Reliability, Observability, Cost, and Sustainability—connects design decisions to operation. A retry policy, for instance, affects availability, evidence, resource use, and cost together.
The remaining foundations cover scale, consistency, latency, failure, and economics. AI introduces additional uncertainty, while these chapters keep the underlying engineering questions in view.
Quick check: what authorizes a refund?
An AI model is 99% confident that a customer should receive a $5,000 refund. What authorizes the action?
* A: The model’s confidence score.
* B: A second model agreeing.
* C: Verified eligibility, permissions, limits, current state, and any required approval.
The answer is C. The execution service must enforce those conditions. Any required approval should cover the exact action, amount, recipient, and expiry.
A model can propose a refund. Trusted services authorize and execute it. If the relevant state or approval changes, the system must check again before acting.
The execution service verifies authority before applying the proposed refund.
Part III: apply the reasoning to recognizable systems
Chapters 14–23 work through ride-sharing, social networking, e-commerce, messaging, auctions, fraud detection, video streaming, payments, file storage, and search. Chapter 24 closes with designing for uncertainty.
Before reading a case study’s design, sketch your own. Then compare assumptions, boundaries, trade-offs, and recovery paths. The most useful difference may be a question you forgot to ask.
Across these systems, the vocabulary changes, but several questions return: where does the system commit, which evidence is authoritative, how are retries bounded, and who owns recovery?
A small example: where does a proposal become a change?
Consider a fictional order assistant that helps a customer change a delivery address before picking begins. Assume the customer can modify only their own eligible order. These are exercise assumptions to validate in a real system.
The assistant can collect details and propose a request. The order service owns the authoritative state and the commitment decision. It must enforce identity, ownership, and current eligibility when the change is applied.
The order service enforces eligibility at commitment and reconciles uncertain outcomes.
Now change one condition: picking begins after the assistant gathered the information. An earlier check is insufficient. The design needs concurrency controls, such as a transaction or conditional version update, that preserve the rule at commitment.
Next, assume the call times out. A missing response does not tell you whether the change happened. Preserve the operation identity and reconcile the service result before another attempt, following the actual API’s retry contract.
This example connects the customer journey, capability ownership, AI authority, consistency, failure, and evidence. That connection is the reasoning to practice.
Practice with “The Refund That Disappeared”
In the November 7 live session, we explored a fictional refund assistant that sends a $45 refund, loses the provider’s response, and retries. An unsafe recovery policy pays the customer $90.
The challenge makes the difference between a missing response and an actual business outcome visible.
A timeout leaves the caller uncertain. Reconcile the original operation before creating another effect.
Work through four activities:
1. Break it. Run the unsafe policy and inspect what the application observed and what the provider committed.
2. Repair it. Preserve the original operation identity, look up the outcome, and apply the provider’s retry and idempotency rules.
3. Test it. Change the parameters, expire the key, or make status lookup unavailable. Check whether the repair still protects the customer and the business.
4. Explain it. Describe the evidence behind the final state and name the person or team responsible for unresolved cases.
Keep uncertain outcomes pending. Report success only when authoritative evidence supports it. Bound the recovery attempts and provide an escalation path.
Idempotency also has a contract: its scope, retention period, and parameter rules matter. If a key has expired and the original outcome remains unknown, simply issuing a new key can create another financial effect. Resolve the original operation or escalate it before proceeding.
The challenge is free and requires sign-in. Newsletter subscription is optional. It uses a fictional provider, and no money moves.
What does “eval” mean here?
An evaluation tests behavior against explicit criteria using defined scenarios and evidence.
For the refund challenge, useful criteria include:
* An authorized refund can complete.
* A lost response does not cause a duplicate refund.
* Changed request parameters do not silently reuse an earlier approval.
* An unresolved outcome reaches a named recovery owner.
Preventing every action would block legitimate customer work. A useful evaluation checks both the actions the system must prevent and the work it must allow.
Practice includes both preventing unsafe actions and completing legitimate customer work.
Use the book with a practical reading loop
Read one section. Sketch one design. Defend one decision. Change one constraint. Revise your answer.
Write down what changed and why.
For interview preparation, begin with Chapters 1–5, attempt a relevant case study, then revisit the foundation your answer exposed as weak. Use the Interview Practice Guide for a timed rehearsal and a focused self-review.
For architecture review preparation, choose a current decision and identify its customer impact, invariant, owning service, options, and unresolved evidence. Use the separate Review Prep Pack to prepare the meeting and record its outcome.
When drawing, choose the view that answers the question. The C4 model offers context, container, component, and code views. A review of authority or commitment also needs those responsibilities made explicit on the relevant diagram.
Explore the C4 model and its diagram levels
The official companion repository includes chapter materials and practice resources, including a seven-step method, interview rubric, diagram guide, and architecture decision record template.
Open the official Apress companion repository
Download the two fillable prep guides
Interview Practice Guide — 4 pages
A 30-minute drill, worked answer, follow-up questions, explanation rubric, and six fillable worksheet fields. Use it to rehearse how you communicate a design.
System Design & Architecture Review Prep Pack — 5 pages
A chapter-to-review map, meeting agenda, worked decision, option comparison, boundary-test matrix, evaluation criteria, and six fillable decision-record fields. Use it to prepare a team review.
Download a copy before filling it in, and use a PDF reader that supports form fields. You can also print either guide. The examples are teaching scenarios; adapt the criteria to your context.
Watch the walkthroughs
Book walkthrough and live Q&A
Explore the book’s organization, method, foundations, and applied chapters, with audience questions about getting started, prompts, diagrams, and exercises.
Interactive introduction and refund simulation — November 7, 2026
Follow the seven-step method, examine what authorizes an AI action, and work through a refund whose outcome becomes uncertain after a timeout. See how recovery choices affect the result and how to test the repair.
Recording coming soon to the AI Architect Circle YouTube channel.
Get the book and continue the conversation
I go deeper into the method, foundations, and applied designs in System Design with AI Interview Guide. Choose a chapter, bring your own design to it, and practice explaining why your decisions fit the requirements.
* Get the book on Amazon
* Read the book on O’Reilly
* Join AI Architect Circle for sessions and architecture discussions
* Subscribe to AI Architect Circle on YouTube
Discussion: Which decision is hardest for you to defend today: where authority lives, how the system behaves under failure, or which trade-off to accept? Share a non-confidential example in the comments and the chapter or challenge you would use to examine it.
Continue with the LLM-SHIELD series
* Harden: turn prompts and policies into enforced boundaries
* Empower: delegate capability within explicit permissions
* Demonstrate: inspect what your evaluation actually proved
Resources for this article
Put the lesson to work.
Explore the chapter guide, then practice an interview answer or prepare an architecture review.
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For your next interview
Interview Practice Guide
Rehearse the seven-step method with a timed drill, worked answer and fillable worksheet.
4 pages · Free member PDF
Get the interview guide ↓For your next design review
System Design & Architecture Review Prep Pack
Prepare an architecture review with a chapter map, worked decision, tests and fillable decision record.
5 pages · Free member PDF
Get the review pack ↓Go deeper with the book.
Begin with Part One for the method; use Part Two to examine failure, trust and authority. System Design with AI Interview Guide develops these ideas through methods, fundamentals and applied systems.