Architecture
Last updated
Last updated
Building AI engineering architecture doesn't happen overnight. The most successful approach is to start simple and gradually add complexity as your needs grow.
Begin with the most basic setup: user query → AI model → response. No frills, no complexity—just a working system.
Add mechanisms to provide your model with relevant information for each query. This includes:
Retrieval systems for text, images, or tabular data
Tool integration for web search, news, or company APIs
External database connections
Protect your system and users with safety measures:
Input guardrails: Prevent private information leaks and prompt attacks
Output guardrails: Catch failures, irrelevant content, and harmful outputs
Policy enforcement: Define how to handle guardrail violations
Manage complexity and costs with smarter routing:
Router: Direct queries to the optimal model based on intent or type
Gateway: Provide unified, secure access to multiple models
Centralised security: Improve maintainability and logging
Reduce latency and costs using proven caching strategies:
Exact caching: Reuse identical query responses
Semantic caching: Reuse similar query responses based on meaning
System-level optimisation: Balance speed, cost, and accuracy
Add sophisticated workflows that go beyond simple processing:
Planning capabilities: Allow models to create multi-step strategies
Tool integration: Enable interaction with external systems
Write actions: Allow direct environment interaction (emails, transactions)
Complex flows: Support loops, parallel execution, and conditional logic
Trade-offs: Each step introduces trade-offs between reliability, latency, cost, and complexity. Consider your specific needs when deciding which components to implement.
Security: Steps 3 and 6 are particularly important for security. Guardrails and careful agent implementation are essential for production systems.
Gradual Implementation: Don't try to implement everything at once. This step-by-step approach allows you to validate each component before adding the next layer of complexity.
