AI Architecture Patterns: Routers, Planner-Worker Loops, Memory Layers, and Evaluation Guardrails
Scenario context
A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation.
🎯 System Scenario & Requirements
System Goal
A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation.
Actors & Components
- Application or API tier
- Core service under design
- Operator or SRE observer
Functional Requirements
- A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation.
- Those layers determine safety, latency, cost, and traceability far more than model choice alone.
- How it solves the problem: Rather than writing custom orchestration code for routing, planning, memory, and evaluation, LangGraph encodes each layer as a typed graph node.
Constraints & Assumptions
- The source does not isolate a separate failure mode.
- Limits that matter for the scenario are called out in the stage decisions above.
⚡ Execution Stages & Decision Trace
Stage 1 — End-to-end outcome
After working through the stages, you should be able to explain: - - Route fewer paths well instead of many paths poorly.
- - Planner value comes from bounded execution, not autonomous sprawl.
Deep Dive
Scenario context
A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation. Those layers determine safety, latency, cost, and traceability far more than model choice alone.
How it solves the problem: Rather than writing custom orchestration code for routing, planning, memory, and evaluation, LangGraph encodes each layer as a typed graph node. Mem…
This walkthrough focuses on Scenario context.
Before making a design choice, clarify:
- A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation.
- Those layers determine safety, latency, cost, and traceability far more than model choice alone.
- How it solves the problem: Rather than writing custom orchestration code for routing, planning, memory, and evaluation, LangGraph encodes each layer as a typed graph node.
Concepts in play: llm-agents-and-langgraph, event-driven-architecture, graph-traversal-algorithms
Stage 1 — End-to-end outcome
- After working through the stages, you should be able to explain: - - Route fewer paths well instead of many paths poorly.
- Planner value comes from bounded execution, not autonomous sprawl.
Observable outcome: - - Planner value comes from bounded execution, not autonomous sprawl.
End-to-end outcome
A single agent loop is enough for a demo, but production AI systems need explicit layers for routing, execution, memory, and evaluation.
Alternatives considered
Alternatives considered
The source focuses on one primary design.
Failure and degraded modes
Failure and degraded modes
- Detection: The source does not isolate a separate failure mode.
- User or operator impact: The source does not isolate a separate failure mode.
Key learnings
- After working through the stages, you should be able to explain: - - Route fewer paths well instead of many paths poorly.