Progressive Agent Architecture

Grow from a bare model API to a full agentic system in ordered steps — context, guardrails, routing/gateway, caching, then write-capable loops.

Start with the simplest architecture that can face users, then add components as challenges appear, keeping the control flow in your own code (start simple and own the control flow). A common production progression:

  1. Enhance context — RAG and read tools.
  2. Add input and output guardrails.
  3. Add a model router and model gateway.
  4. Cut latency/cost with response caching.
  5. Add complex control flow and write actions — full LLM agent patterns.

Each step adds capability or safety and also failure surface and debugging difficulty. Adapt the order to the product; do not jump to write-capable agents before context and blast-radius controls exist. Cross-cutting AI pipeline orchestration wires the pieces; runtime metrics and tracing for the running system belong to observability practice, not to this design sequence itself.

Within orchestration, prefer a basic fixed task graph (LLM inside tasks, deterministic routing between them) before LLM-driven workflow routing, role crews, or stateful task agents. Fixed graphs keep a finite, a-priori-known task set and communication pattern, so failures isolate per task and per-task optimization stays tractable. Avoid LLMs where traditional software or classical ML is enough; when LLMs are required, confine them to tasks inside a dependable graph until goals truly need open-ended agency. That progression is the practical face of the generality–strength tradeoff: conversational agents stay general-but-weak; workflows buy strength for one goal.

Sources

  • AI Engineering (Chip Huyen), ch. 10
  • Prompt Engineering for LLMs (Berryman), ch. 9