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AI changes where a system’s behavior and risk can originate; it does not change the architect’s job of making the whole system coherent. For a first project, keep the model’s authority narrow, map every stage from input to human action, and make uncertain outputs observable, testable, and reviewable.
What problem are we solving?
Start with a specific user task and a defined outcome, not with a model or a prompt. A useful first project is an incident-review assistant that reads incident notes and trusted runbooks, then drafts a summary and possible next checks for an engineer. The assistant supports investigation; it does not take over incident response.
Decide what the system must produce and what counts as useful before choosing an implementation. If the required result is a score, rank, flag, or class, conventional machine learning (ML) may fit. If the system needs to produce new text, code, or other content, generative AI is the relevant category. Use both only when the task genuinely needs both kinds of output.
Which quality needs matter most?
Write down the qualities that would make the project safe and useful in its actual setting. For the incident assistant, that could mean drafts are grounded in approved materials, engineers can review them before acting, sensitive details are handled deliberately, and a useful fallback remains available if generation fails.
#1 Best Overall
- Accuracy and evidence: Can a reviewer identify which approved sources support the draft?
- Privacy: What incident data may be sent to the model or retained in logs?
- Latency and cost: Is a response needed synchronously, and can the expected request volume be supported?
- Availability and recovery: What can the engineer do when search or the model is unavailable?
- Changeability: Can the team update a prompt, retrieval method, or model without silently changing the system’s behavior?
Prioritize these needs explicitly. They guide choices about retrieval, data handling, model access, fallbacks, and whether a response can be synchronous.
What changes when AI enters the architecture?
In a conventional application, teams often focus on application code and configuration as the main sources of behavior. An AI-enabled system has more behavior-shaping inputs. The model and its version, the data used to train or ground it, prompts, retrieved documents, tool permissions, settings, and output checks can all affect what users experience—even if the surrounding application code has not changed.
That means a change to a prompt or runbook index can matter as much as a code change. Treat these inputs as versioned, reviewable parts of the system. Record which versions and sources informed a result so the team can investigate behavior and reproduce relevant tests.
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| Architecture concern | ML for scores or classifications | Generative AI for new content |
|---|---|---|
| Typical output | A score, rank, flag, or class | Text, code, or other generated content |
| Behavior to account for | The trained model and its data | The model plus prompts, retrieved context, tools, and settings |
| What to test | Whether predictions meet the task’s acceptance criteria | Whether content is useful, supported, safe, and handled correctly when it is not |
| What to monitor | Operational performance and changes in prediction outcomes | Operational performance, sources, fallbacks, rejected drafts, and review outcomes |
This distinction is about the result the task needs, not a rule that every project must use one approach. An incident assistant that drafts explanations is a generative use case; adding a separately needed priority score would be a reason to assess ML as well.
Where are the system boundaries?
Draw the request as a pipeline rather than treating “call the model” as the whole design. In the incident assistant, the path can include input cleanup, sensitive-data handling, retrieval, request construction, the model call, output and source validation, human review, and outcome logging. Give every stage an identifiable failure path.
- Input boundary: Accept the incident notes and remove secrets or other data that should not be passed downstream.
- Retrieval: Search approved runbooks and team notes, and keep the retrieved sources identifiable.
- Request construction: Assemble the task instructions and allowed context using a versioned prompt.
- Model adapter: Put the model API behind a replaceable interface so the application is not coupled to one provider’s specific call pattern.
- Output checks: Validate the response structure, referenced source identifiers, prohibited content, and conditions that require rejection or fallback.
- Human review: Let the engineer accept, edit, or reject the draft before taking action.
- Outcome logging: Record safe operational metadata, including model and prompt versions, sources used, and the review outcome.
Checks can validate format, source identifiers, prohibited content, and fallback conditions; they cannot prove every claim is true. The engineer remains responsible for the final action.
Rank #3
Which team owns each part?
Assign ownership across the complete pipeline, not only to the team that integrates the model. The application team may own input handling and the model adapter; service or operations owners may curate runbooks; security and privacy reviewers may set data-handling requirements; and the engineering team using the assistant owns review of its draft before action. The exact split depends on the organization, but each stage needs an accountable owner.
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What happens when a dependency fails?
Decide in advance what the user sees when any stage cannot do its job. If the model call fails, the assistant can show search results from approved runbooks rather than presenting an empty screen or an unverified draft. If retrieval finds no trusted context, the system should not imply that a generated answer is grounded; it can explain that no usable source was found and provide the relevant fallback.
- When input cleanup or sensitive-data checks fail, stop the request rather than sending uncertain data onward.
- When search fails or returns no trusted material, show an explicit no-context state or available approved search results.
- When the model is unavailable, use the defined search fallback.
- When output validation rejects a draft, explain the rejection in a way the engineer can act on and preserve the fallback path.
Keep the fallback visible in the design and test it as part of the user experience. A fallback that exists only in architecture notes but leaves users stranded is not an effective recovery path.
How should the first project be bounded?
Limit both the task and the assistant’s authority. In this example, it drafts an incident summary and possible next checks for an engineer. It cannot restart a service, alter the system, or message a customer. A person reviews the draft before any action.
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Before building, settle the design choices most likely to change the architecture:
- What task is in scope, and what outputs are explicitly out of scope?
- Which incident data is sensitive, and what may leave the system?
- Which runbooks and notes count as trusted sources?
- Does the task require retrieval, and how will source references be represented?
- What authority, if any, can the model exercise through tools?
- How much latency and request cost are acceptable for this workflow?
- What is the user’s next step if retrieval, generation, or validation fails?
- Who must review the result before it can influence an operational action?
These are design constraints, not details to postpone until after choosing a model. For example, sensitive data may call for stricter filtering or a private API; slow responses may call for an asynchronous workflow; and poor retrieval may require better source tags or smaller chunks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How will we test usefulness and operational behavior?
Build a small, representative test set before relying on the assistant. Include approved sources, required facts, prohibited facts, valid next checks, and cases that should trigger a fallback. Evaluate both the usefulness of the draft and what the system does when it cannot produce a trustworthy result.
Re-run the tests after changes to the model, prompt, search, or validation logic. Track operational signals that help explain both system performance and user outcomes:
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- Latency, errors, and token use or cost
- Fallback and rejection rates
- How often engineers edit drafts
- Missing sources and failed searches
- Model and prompt versions, sources used, and review outcomes
Decide what prompt and output data is safe to retain, who may see it, and when it is removed. Do not log raw prompts and answers by default without a deliberate privacy and access decision. Operational metadata can help diagnose problems without making unrestricted retention of conversation content the default.
How will we monitor and change the system?
Monitoring should make it possible to distinguish a model issue from a search, data, prompt, validation, or dependency issue. Use version and source metadata alongside latency, errors, fallbacks, rejections, edits, and search failures. Review those signals with the people who own the corresponding pipeline stages.
Change control should cover prompts, retrieved documents, model versions, settings, and output checks as well as application code. Review meaningful changes, run the test set again, and preserve a way to identify which configuration produced a draft. For learning about AI architecture, the cited articles point to iSAQB and modules including SWARC4AI and AGENTA; check the providers for current availability and course details.
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