AI Architecture
Deterministic Workflows vs Autonomous Agents: When to Use Code vs LLM Decisions
Avoid over-engineering: why deterministic Python scripts often beat autonomous agents for predictable, high-reliability business logic.
The 80/20 Engineering Rule
If the business process is 100% deterministic, write regular code. Delegate to LLM agents only when handling unstructured inputs or dynamic tool selection.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
Master Anthropic and Gemini Prompt Caching to slash API bills and reduce latency on long documents, system instructions, and multi-turn chats.