# LangChain Advanced Patterns: Building Production-Grade AI Systems

> Source: <https://dev.to/mzunain/langchain-advanced-patterns-building-production-grade-ai-systems-4d65>
> Published: 2026-07-21 06:00:00+00:00

You've built a simple agent. Now scale it.

Production LangChain systems require:

``` python
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
agent = initialize_agent(
    tools, llm, memory=memory, agent_type="conversational"
)
python
from langchain.memory import ConversationSummaryMemory

memory = ConversationSummaryMemory(
    llm=OpenAI(),
    buffer="Current conversation summarized"
)
python
from langchain.chains import SequentialChain

chain = SequentialChain(
    chains=[chain1, chain2, chain3],
    verbose=True
)
router_template = """Given the input, route to: 
analysis, coding, or research

Input: {input}
Route:"""

router = llm_chain.run(router_template)
if "coding" in router:
    result = coding_agent.run(input)
python
from tenacity import retry, stop_after_attempt

@retry(stop=stop_after_attempt(3))def safe_agent_run(query):
    return agent.run(query)

try:
    result = safe_agent_run(query)
except Exception as e:
    logger.error(f"Agent failed: {e}")
    result = fallback_response()
python
from langchain.cache import RedisCache
import redis

redis_client = redis.Redis.from_url("redis://localhost")
langchain.llm_cache = RedisCache(redis_client=redis_client)
python
results = [agent.run(q) for q in queries]
# Better: Use async
import asyncio
results = await asyncio.gather(*[async_agent(q) for q in queries])
python
import logging
from datetime import datetime

class AgentLogger:
    def log_run(self, query, response, duration):
        logging.info(f"Query: {query}")
        logging.info(f"Response: {response}")
        logging.info(f"Duration: {duration}s")

        # Track metrics
        self.track_metric("agent_latency", duration)
        self.track_metric("token_usage", count_tokens(response))
python
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vector_store = Pinecone.from_documents(docs, embeddings)

retriever = vector_store.as_retriever()
agent_with_retrieval = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever
)
python
# AWS Lambda
def lambda_handler(event, context):
    query = event['query']
    result = agent.run(query)
    return {'statusCode': 200, 'body': result}
python
def test_agent_accuracy():
    test_cases = [
        ("query1", "expected_output1"),
        ("query2", "expected_output2")
    ]

    for query, expected in test_cases:
        result = agent.run(query)
        assert verify_correctness(result, expected)
```

✅ Error handling for all tool calls

✅ Logging for debugging

✅ Monitoring & alerting

✅ Rate limiting

✅ Input validation

✅ Output sanitization

✅ Cost tracking

✅ Performance metrics

✅ Rollback procedures

✅ Security hardening

**Issue 1**: Token limits exceeded

→ Solution: Summarize long conversations

**Issue 2**: Tool calls fail silently

→ Solution: Add explicit error messages

**Issue 3**: Costs spiral out of control

→ Solution: Implement token budgets

**Issue 4**: Model drift over time

→ Solution: Regular monitoring & retraining

LangChain in enterprise = structured, monitored, optimized.

You now have the patterns to build production systems.

**What LangChain patterns are you using?**