Agentic AI · LangGraph · Orchestration
Agentic AI systems architecture with LangGraph
Design patterns for agentic AI with LangGraph: routing, tool use, memory, evaluation, and observability for production systems.
Puneet Singhal
· 1 min read
A field-tested blueprint for orchestrating multiple specialized agents using LangGraph, with reliable tool access, structured memory, and hardened guardrails. Build systems that can reason, collaborate, and recover gracefully in production.
Core building blocks
- Router agent for intent detection and delegation across the graph
- Domain specialists for pedagogy, practice, worldview, reflection, and orchestration
- Shared memory layer combining RAG and short-term conversation buffers
- Tool layer for profile/data access, search, transactions, and guardrails
Reliability & governance
- Deterministic tools with explicit input/output schemas and timeouts
- Evaluation harness with regression suites, telemetry-driven improvements, and red teaming
- Escalation paths and human-in-the-loop checkpoints for high-risk actions
Observability & rollout
- Trace each turn: prompt, tool calls, outputs, cost, and latency metrics via LangSmith/Langfuse
- Use progressive rollout with feature flags and canary cohorts
- Keep agents narrowly scoped; avoid monolithic “do everything” agents for clarity and safety
Related: Build the LLM integration layer and Engineer the agent harness.