Production AI Architecture
Scalable AI systems combining retrieval, reasoning, memory, evaluation, and tool-using agents built for real production workloads.
AI requests pass through retrieval systems, LLM reasoning layers, tool execution, memory stores, and structured response generation.
Building Production AI Systems
Practical patterns for designing scalable AI systems: RAG pipelines, agents, memory systems, evaluation frameworks, and real-world inference architecture.
Core Principles
- Separate retrieval, reasoning, and generation into distinct layers
- Design for observability and evaluation before optimizing prompts
- Treat LLM outputs as intermediate signals, not final truth
- Prefer composable pipeline stages over monolithic prompt chains
Common Failure Modes
- Hallucinations caused by weak retrieval grounding or missing context
- Agent loops without proper termination or cycle-detection logic
- Context window overflow and memory drift across long sessions
- Unstable tool execution ordering in parallel agent steps
System Architecture Areas
Retrieval
Vector search, hybrid retrieval, embedding models, and context window assembly.
Agents
Tool calling, planning loops, multi-step task execution, and multi-agent workflows.
Memory
Long-term context, episodic memory, session summarization, and knowledge persistence.
Evaluation
Retrieval metrics, hallucination detection, latency tracing, and quality scoring.
System Architecture Diagram
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Implementation Stack
Frequently Asked Questions
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