Memory & Context Handling

Effective memory and context management is crucial for maintaining coherent, personalized interactions across complex multi-agent workflows. In multi-agent systems, memory serves as the shared knowledge foundation that enables agents to collaborate seamlessly, learn from collective experiences, and maintain consistency across distributed operations. Without robust memory systems, agents would operate in isolation, unable to benefit from previous interactions or coordinate effectively, leading to redundant work and inconsistent user experiences.

In Knovvu Virtual Agent, the memory system supports short-term tasks, enabling agents to handle immediate actions. Short-term memory is further divided into two categories:

Working Memory: Temporarily stores active task information, current calculations, and immediate decision-making data during agent processing. Acts as a scratchpad for ongoing operations and intermediate results.
Context Window: Maintains the recent conversation history and relevant session data within the current interaction. Determines how much previous dialogue the agent can reference for maintaining conversational coherence.

Use Case for Working Memory: Multi-Step Order Processing

When a customer calls to place a complex order, working memory temporarily stores calculation data and validation results throughout the process:

Customer requests 3 different products with varying quantities
Working memory holds:
- Item IDs and quantities for each product
- Real-time price calculations and discounts
- Inventory availability checks
- Running total and tax calculations
Agent references this data to confirm order details before finalizing
Once transaction completes, working memory clears for the next task

This prevents the agent from repeatedly querying external systems and enables smooth, uninterrupted order flow.

Use Case for Context Window: Customer Service Escalation

A customer discusses a billing issue across a 20-turn conversation:

Turn 5: Customer mentions their account number and describes overcharge
Turn 12: Agent asks clarifying questions about transaction dates
Turn 18: Customer references "that charge I mentioned earlier"
Turn 20: Agent summarizes the full issue using context from turns 5, 12, and 18

The context window maintains this entire conversation history, enabling the agent to provide coherent responses that acknowledge previous statements without requiring customers to repeat information.