Microsoft ISE: Earning Agentic Complexity — Guidance on Avoiding Unearned Complexity in LLM Production Solutions
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User queries RAG chatbot
A user submits a query to a Generative AI chatbot built on a Retrieval Augmented Generation architecture.
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.
What failed first
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.