Agentic AI systems are everywhere in demos. Making them reliable in production is a different story.
After building several multi-agent systems with LangGraph and LangChain, here are the patterns that actually hold up.
The Core Problem
Most agent demos are optimistic: perfect inputs, single tasks, no failures. Real-world agents face ambiguous queries, tool timeouts, and cascading failures across a graph of reasoning steps.
Memory Is Not Optional
Agents without persistent memory repeat themselves, lose context between turns, and fail on multi-step tasks. The fix:
from langgraph.checkpoint.sqlite import SqliteSaver
memory = SqliteSaver.from_conn_string(":memory:")
graph = workflow.compile(checkpointer=memory)Use thread_id to isolate sessions. Use namespace to scope shared knowledge.
Design for Failure at Every Node
Every tool call is a potential failure point. Wrap them:
def safe_tool_call(tool_fn, *args, retries=2, **kwargs):
for attempt in range(retries + 1):
try:
return tool_fn(*args, **kwargs)
except Exception as e:
if attempt == retries:
return {"error": str(e)}Don't let one failed tool call crash an entire reasoning chain.
Parallel Subgraphs for Speed
LangGraph supports Send for fan-out — run independent subtasks in parallel before merging results:
from langgraph.constants import Send
def route_tasks(state):
return [Send("worker_node", {"task": t}) for t in state["tasks"]]This cut latency by ~60% on our document analysis pipeline.
Observability First
Log every node entry/exit with the full state. Use LangSmith or build your own trace store. You can't debug what you can't see.
The gap between "agent that works in a notebook" and "agent running 10k requests/day" is mostly about failure handling, observability, and memory. Get those right first.