Agent Design Fieldbook
An 8-layer architecture for production AI agents — from data layer to RAG. Real code, real failures, battle-tested patterns for engineers and technical founders.
The Foundation: Why Most AI Agents Fail in Production
Most AI agents are wrappers that crash in production; production agents are systems with 8 deterministic layers where the LLM is constrained, not trusted.
The Data Layer: Your Agent Is Only As Good As Your Data
LLMs will hallucinate field names and values unless you explicitly define what's real through schemas, field registries, and validation boundaries.
The Ingestion Layer: Getting Messy Data Into Your Clean Schema
Vendor data is chaos -- different units, formats, nulls, duplicates; ingestion is 80% of the work, and scripts beat pipelines for flexibility.
The Intent Layer: Classify Before You Act
The first LLM call should classify what the user wants (search, compare, select), not execute it -- then route to specialized handlers that do one job well.
The Filter Extraction Layer: From Natural Language to Query
LLMs are excellent at extracting filters from natural language, but terrible at enforcing boundaries -- inject your field registry, validate everything, and relax constraints when needed.
The Memory Layer: Conversations That Persist
Agents need memory -- not just chat history, but structured context (what was fetched, what was selected, what tokens were used) persisted to database for multi-turn conversations.
The Sort & Rank Layer: Ordering Results Intelligently
Sorting isn't just ORDER BY -- define what 'best' means for each field, handle JSONB with SQL expressions, and let the LLM infer user intent from keywords like 'cheapest' or 'best'.
The Product Deep-Dive Layer: When Users Want More Than Specs
When users want to go deep on a product -- reading reviews, understanding thermal performance, checking compatibility -- RAG lets you search unstructured knowledge that doesn't fit in structured columns.