AI Systems Architecture Starter Kit
The 5-component framework for designing production AI systems — with build-vs-buy guidance, key decisions, and common mistakes for each layer.
Why This Matters
A production AI system is not a model with a UI. It is a system of five interdependent components that each need deliberate design. Teams that design all five components before building ship to production faster and spend significantly less time firefighting. Teams that discover the components they missed in production spend months retrofitting reliability into systems that were not designed for it.
Component 1: Data Layer
The infrastructure that collects, cleans, transforms, and serves data to the AI components. Every other component depends on the data layer working reliably. A good data layer means the AI gets clean, current, complete inputs. A bad data layer means every AI problem is actually a data problem in disguise.
Build vs Buy
Buy or use open-source: ingestion connectors (Fivetran, Airbyte, custom webhooks), data quality tools (Great Expectations, dbt tests), vector databases (pgvector, Qdrant, Pinecone). Build: your specific schema and transformation logic, your quality rules for your domain, your ingestion logic for proprietary internal systems.
Key Decisions
- Batch vs streaming: what freshness does the AI system require? Minutes → streaming. Hours → batch. Most systems need batch.
- Where does the data live at inference time? In a vector store (for RAG)? In a structured database (for decision support)? Both?
- Who owns data quality, and how is it monitored? Define the owner and the metrics before build.
- What happens when data is missing or corrupt? Design the error handling before you need it.
Common Mistakes
- Assuming data quality is adequate without measuring it — always audit a representative sample before starting
- Building ingestion in isolation from the consuming AI component — ingestion schema should be designed with the consumer's requirements in mind
- No data freshness monitoring — stale data fed to an AI system produces stale outputs that appear correct
- Single point of failure in the ingestion pipeline — design for the ingestion source being temporarily unavailable
Component 2: Orchestration Layer
The layer that sequences, manages, and coordinates the steps in your AI workflow. In a simple system, this might be a small service with three steps. In a complex system, it is a workflow engine that manages branching logic, parallel execution, retries, timeouts, and human-in-the-loop steps. Either way, it exists — the question is whether it is designed or emergent.
Build vs Buy
Evaluate carefully: LangChain/LlamaIndex for prototyping and standard RAG/agent patterns; Temporal or Prefect for durable, long-running workflows with retry/timeout requirements; Airflow for scheduled batch processing pipelines. Build: your specific workflow logic, branching conditions, and business rules. Do not build a general-purpose orchestration engine from scratch.
Key Decisions
- What is the failure behaviour at each step? Retry, skip, alert, or halt?
- Which steps require human approval, and what is the interface for that approval?
- How do you handle partial failures — where one step in a multi-step workflow fails?
- What are the maximum acceptable latency and cost for the full workflow execution?
Common Mistakes
- Linear "happy path" orchestration with no failure handling — production always encounters the unhappy path
- No timeout on any step — a single slow model call can stall the entire workflow indefinitely
- Synchronous orchestration for workflows that should be async — blocking the caller while the AI processes is a latency antipattern
- No visibility into orchestration state — when something goes wrong, you should be able to see exactly where and why without reading logs
Component 3: Model Serving
The layer that manages how your system calls AI models: which model, with what parameters, with what cost controls, with what fallback when the primary model is unavailable. This is not just the API call — it is the management layer around the API call that makes it production-grade.
Build vs Buy
Buy: a model gateway (LiteLLM, Portkey, or a custom proxy) that handles key management, routing, logging, and rate limiting centrally. Build: your model selection logic, your prompt templates, your output schema specifications. Never build: the model itself, the inference infrastructure, or the API management for commercially hosted models.
Key Decisions
- Which model(s) for which tasks? Define the routing logic that sends requests to the appropriate model based on task complexity and cost requirements.
- What is the fallback when the primary model is unavailable? Define and test this before launch.
- What are the timeout and retry settings? These must reflect real model latency, not default HTTP settings.
- How are API keys managed and rotated? Central key management prevents credential sprawl.
Common Mistakes
- Calling model APIs directly from application code — eliminates central visibility, rate limiting, and key management
- No model fallback — a single model API outage takes down the entire AI feature
- No per-request cost logging — you cannot manage costs you cannot see
- Using a single model for all tasks regardless of complexity — model routing cuts costs 40-70% with minimal quality impact
Component 4: Output Validation Layer
The layer that validates AI outputs before they are used or shown to users. This is the firewall between model capability and business reliability. It catches errors the model makes, enforces business rules the model does not know, and routes low-confidence outputs to human review before they reach the end state.
Build vs Buy
Build: all of this. Validation is inherently business-specific — the rules that define a valid output for your use case are specific to your domain, your data, and your risk tolerance. Tools like Guardrails AI and LMQL can scaffold the validation framework, but the rules must be defined by you.
Key Decisions
- What are the validation rules for each output field? (format, range, completeness, business logic)
- What is the confidence threshold below which an output is routed for human review?
- What happens when an output fails validation? Retry? Route to review? Return an error?
- Who reviews flagged outputs, in what interface, and within what SLA?
Common Mistakes
- No output validation — trusting model outputs directly produces business logic errors at the rate of the model's error rate
- Binary pass/fail validation — field-level confidence enables partial automation (approve confident fields, review uncertain ones)
- No human review interface — if reviewers have to re-enter data from scratch, automation adds no value for flagged outputs
- Validation rules defined without testing against real failure cases — validation rules should be built from observed failure modes, not assumed ones
Component 5: Monitoring and Evaluation
The infrastructure that tells you, continuously, whether your AI system is performing as expected. This includes operational health monitoring (latency, error rates, cost), output quality monitoring (quality scores, override rates), data quality monitoring (freshness, completeness, distribution), and business outcome monitoring (the metric the AI is designed to move).
Build vs Buy
Buy: observability tooling (Langfuse, Helicone, Arize, or a general APM tool with AI extensions), alerting infrastructure (PagerDuty, OpsGenie, or your existing alerting system), dashboarding (Grafana, Datadog, or your existing BI tool). Build: your specific quality metrics, your evaluation set and scoring logic, your business outcome queries.
Key Decisions
- What quality metric is the primary health indicator for this system? Define and instrument it from day one.
- What are the alert thresholds and escalation paths? Who gets paged at 2am vs who gets an email?
- How often is the evaluation set run? After every change? Weekly? On a schedule?
- What does degradation look like, and what is the remediation playbook when it is detected?
Common Mistakes
- Monitoring infrastructure is the last thing built — by the time monitoring is added, the system has been in production without visibility
- Only monitoring infrastructure health (HTTP errors, latency) without monitoring output quality — the most important failure mode goes undetected
- No evaluation set — without ground truth testing, quality degradation is only detected when users complain
- Alerts without escalation paths — alerts that go nowhere are the same as no alerts
AI Systems Architect
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