Is This Agentic Workflow with Modular Tooling Optimal for Low-Latency, Scalable Assistants on Azure?

Question

Hi everyone,

We’ve architected a conversational agent using Azure OpenAI (GPT-4 Turbo) and LangChain ReAct, with four well-scoped tools handling discrete tasks. The entire pipeline is built for high scalability, low latency, and strict grounding (no hallucinations beyond stored history and vector-retrieved content). We’d love some expert feedback on whether this design aligns with best practices — or if there are ways we can optimize further.

🔹 Our Agent Workflow

The system follows a disciplined routine:

1. fetch_conversation_context  • Looks up session history using user_id, session_id, department_id, org_id  • Returns: Updated conversation summary + last N message turns

search_documents  • Generates embeddings for current query and last N messages  • Uses those to retrieve top-K relevant document chunks from a Cosmos DB vector store

answer_query  • Consumes: query + last N turns + summary + retrieved chunks  • Output: A strictly grounded response (no model assumptions beyond given context)  • Includes self-reflection loop to refine response before final output

update_summary  • Updates the conversation summary with the new query/response  • Stores full output (query, answer, updated summary) in Cosmos DB, indexed by session metadata

🔹 Goals

✅ Minimize latency without sacrificing accuracy

✅ Keep token footprint compact via summary + limited recent history

✅ Scale efficiently across departments/organizations

✅ Fully grounded responses with traceable reference documents

🔹 What We’d Like Feedback On

Does this modular ReAct-style architecture follow best practices for scalable, production-ready agents on Azure?

Are there optimizations for latency or Cosmos DB performance we should consider (especially under high user concurrency)?

Are embedding-based retrievals on top of summaries + recent turns effective enough, or would caching or reranking approaches help?

Should we introduce additional validation/guardrails between tools for enterprise use cases?

Any suggestions, real-world learnings, or guidance from Azure and LangChain practitioners would be hugely appreciated!

Thanks in advance!Hi everyone,

We’ve architected a conversational agent using Azure OpenAI (GPT-4 Turbo) and LangChain ReAct, with four well-scoped tools handling discrete tasks. The entire pipeline is built for high scalability, low latency, and strict grounding (no hallucinations beyond stored history and vector-retrieved content). We’d love some expert feedback on whether this design aligns with best practices — or if there are ways we can optimize further.

🔹 Our Agent Workflow

The system follows a disciplined routine:

fetch_conversation_context
 • Looks up session history using user_id, session_id, department_id, org_id
 • Returns: Updated conversation summary + last N message turns

search_documents
 • Generates embeddings for current query and last N messages
 • Uses those to retrieve top-K relevant document chunks from a Cosmos DB vector store

answer_query
 • Consumes: query + last N turns + summary + retrieved chunks
 • Output: A strictly grounded response (no model assumptions beyond given context)
 • Includes self-reflection loop to refine response before final output

update_summary
 • Updates the conversation summary with the new query/response
 • Stores full output (query, answer, updated summary) in Cosmos DB, indexed by session metadata

🔹 Goals

✅ Minimize latency without sacrificing accuracy

✅ Keep token footprint compact via summary + limited recent history

✅ Scale efficiently across departments/organizations

✅ Fully grounded responses with traceable reference documents

🔹 What We’d Like Feedback On

Does this modular ReAct-style architecture follow best practices for scalable, production-ready agents on Azure?

Are there optimizations for latency or Cosmos DB performance we should consider (especially under high user concurrency)?

Are embedding-based retrievals on top of summaries + recent turns effective enough, or would caching or reranking approaches help?

Should we introduce additional validation/guardrails between tools for enterprise use cases?

Any suggestions, real-world learnings, or guidance from Azure and LangChain practitioners would be hugely appreciated!

Thanks in advance!