Workflow · Production

Microsoft ISE: Earning Agentic Complexity — Guidance on Avoiding Unearned Complexity in LLM Production Solutions

The problem

Development teams building LLM solutions frequently adopt agentic frameworks and LangChain before understanding whether the added complexity is warranted, leading to brittle systems that are hard to debug and exhibit wide swings in accuracy and latency in production.

First attempt

LangChain, as a pre-v1 library, caused large breaking changes requiring significant rework on customer projects, carries a heavy software bill of materials with numerous sub-dependencies that expand the attack surface, and has known security issues that most first-party Microsoft teams choose to avoid.

Workflow diagram · grounded in source
1
User queries RAG chatbot
trigger
“Let's imagine a typical "talk with your data" scenario where we are building a Generative AI (GenAI) chatbot using a Retrieval Augmented Generation (RAG) architecture.”
2
Ada model creates embeddings
ai_action
“the Ada model for creating embeddings”
3
Azure Cognitive Search retrieval
integration
“Azure Cognitive Search will act as our vector database (VectorDB)”
4
LangChain agent generates response
ai_action
“The chatbot will be powered by a LangChain agent, with various LangChain tools integrated to enhance its capabilities. This setup will allow the chatbot to engage in natural conversations while effectively retrieving and presenting infor…”
5
Dependency and vulnerability audit
validation
“# Install pip-audit pip install pip-audit # Check for vulnerabilities pip-audit -r requirements.txt”
Reported outcome

Recent ISE customer projects have explicitly avoided agentic designs in favor of more explicit chained component designs that are easier to debug and optimize, and ISE recommends starting with a simple fixed-path baseline solution before adding complexity.

Reported metrics
Langchain package total dependencies90
Langchain-core package total dependencies24
Langchain-openai package total dependencies59
Langchain-community package total dependencies155
Show all 6 reported metrics
langchain package total dependencies90
langchain-core package total dependencies24
langchain-openai package total dependencies59
langchain-community package total dependencies155
agent pattern accuracy and latency variancewide swings in accuracy and latency
known vulnerabilities in LangChain test scenarioNo known vulnerabilities found
Reported stack
LangChainlangchain-corelangchain-communitylangchain-openailangchain-experimentalChatGPT-4AdaAzure Cognitive SearchSemantic KernelReActpipdeptreeGraphVizpip-auditDALL-E
Source
https://devblogs.microsoft.com/ise/earning-agentic-complexity/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Recent ISE customer projects have explicitly avoided agentic designs in favor of more explicit chained component designs that are easier to debug and optimize, and ISE recommends starting with a simple fixed-path base…

What tools did this team use?

LangChain, langchain-core, langchain-community, langchain-openai, langchain-experimental, ChatGPT-4, Ada, Azure Cognitive Search, Semantic Kernel, ReAct.

What results were reported?

Langchain package total dependencies: 90; Langchain-core package total dependencies: 24; Langchain-openai package total dependencies: 59; Langchain-community package total dependencies: 155 (source-reported, not independently verified).

What failed first in this deployment?

LangChain, as a pre-v1 library, caused large breaking changes requiring significant rework on customer projects, carries a heavy software bill of materials with numerous sub-dependencies that expand the attack surface…

How is this workflow AI workflow structured?

User queries RAG chatbot → Ada model creates embeddings → Azure Cognitive Search retrieval → LangChain agent generates response → Dependency and vulnerability audit.