Is “Summarized Context + Sliding Window” the Best Memory Strategy for Azure OpenAI Agents?

Question

Hi everyone,

We’re building a production-grade, multi-turn conversational agent using Azure OpenAI (GPT-4 Turbo) with LangChain’s ReAct agent architecture, integrated with Azure Cosmos DB for persistent memory and document grounding.

To manage conversational context efficiently, we’re using a hybrid memory strategy:

🔹 What We're Using “Summarized Context + Sliding Window” A combination of:

• A running session-level summary that captures key discussion points and user goals.

A sliding window of the last few conversational turns (typically 3–5) for recency and tone continuity.

Example: Summary: “User is analyzing Q4 financial reports and focusing on department-wise variances.” Recent Turns: Q: “Can you also show HR-related expenses?” A: “HR expenses were 12% above the Q3 average.”

🔹 Why We Chose This Approach

✅ Balances context quality, latency, and cost

✅ Helps the assistant maintain tone, relevance, and continuity

✅ Keeps prompt size within model token limits (~8K to 16K is safe)

✅ More flexible than full history or pure sliding window

🔹 Our Architecture

Agent: LangChain ReAct Agent

Model: Azure OpenAI GPT-4 Turbo

Memory: Cosmos DB (stores summaries and conversation turns with org/user/session scope)

Tools: Custom document search and memory fetch utilities

Constraint: Agent is strictly grounded — it must answer only based on memory and document retrievals (no freeform hallucinations)

🔹 Key Questions

Is this hybrid memory strategy considered best practice for Azure OpenAI-based assistants?

Any Microsoft-recommended patterns or thresholds for:

When to update the summary?

  How many turns to include in the sliding window?
  
     Token budgeting across prompt components?
     
     Any real-world caveats in production — especially around hallucinations or context loss?
     

🔹 What We’re Looking For

Suggestions for best practices from Microsoft or community experts

Lessons from teams who have deployed similar multi-turn agents

Optimizations to improve latency and context stability

Thanks in advance! Looking forward to feedback from the Azure AI / Conversational AI community!Hi everyone,

We’re building a production-grade, multi-turn conversational agent using Azure OpenAI (GPT-4 Turbo) with LangChain’s ReAct agent architecture, integrated with Azure Cosmos DB for persistent memory and document grounding.

To manage conversational context efficiently, we’re using a hybrid memory strategy:

🔹 What We're Using
“Summarized Context + Sliding Window”
A combination of:

A running session-level summary that captures key discussion points and user goals.

A sliding window of the last few conversational turns (typically 3–5) for recency and tone continuity.

Example:
Summary: “User is analyzing Q4 financial reports and focusing on department-wise variances.”
Recent Turns:
Q: “Can you also show HR-related expenses?”
A: “HR expenses were 12% above the Q3 average.”

🔹 Why We Chose This Approach

✅ Balances context quality, latency, and cost

✅ Helps the assistant maintain tone, relevance, and continuity

✅ Keeps prompt size within model token limits (~8K to 16K is safe)

✅ More flexible than full history or pure sliding window

🔹 Our Architecture

Agent: LangChain ReAct Agent

Model: Azure OpenAI GPT-4 Turbo

Memory: Cosmos DB (stores summaries and conversation turns with org/user/session scope)

Tools: Custom document search and memory fetch utilities

Constraint: Agent is strictly grounded — it must answer only based on memory and document retrievals (no freeform hallucinations)

🔹 Key Questions

Is this hybrid memory strategy considered best practice for Azure OpenAI-based assistants?

Any Microsoft-recommended patterns or thresholds for:

When to update the summary?

  How many turns to include in the sliding window?
  
     Token budgeting across prompt components?
     
     Any real-world caveats in production — especially around hallucinations or context loss?
     

🔹 What We’re Looking For

Suggestions for best practices from Microsoft or community experts

Lessons from teams who have deployed similar multi-turn agents

Optimizations to improve latency and context stability

Thanks in advance!
Looking forward to feedback from the Azure AI / Conversational AI community!