conversation handoff patterns - how do you maintain context between agents? c

building a customer support system with EverMemOS where conversations get handed off between specialized agents. struggling with maintaining context continuity when Agent A (general support) transfers to Agent B (technical specialist).

current setup:

using shared group_id (agent_name + conversation_id) for the entire conversation thread, with sender being the actual user ID. both agents store to and retrieve from the same memory space using the shared group_id.

// Agent A stores initial context
{
  "sender": "user123",
  "sender_name": "John Smith",
  "content": "user reports payment failure, card ending in 1234",
  "group_id": "support_agent_general_conv_20241211",
  "scene": "group_chat"
}

// Agent B retrieves context before taking over
{
  "group_id": "support_agent_general_conv_20241211",
  "memory_scope": "group",
  "query": "payment failure context",
  "data_source": "episode"
}

challenges i'm facing:

1. handoff context - how to signal to Agent B what specific issue needs attention vs general conversation history
2. agent capabilities - Agent A might create memories about technical details it doesn't fully understand, Agent B needs to know what's reliable vs what needs verification
3. conversation flow - when Agent B takes over, should it acknowledge the handoff explicitly or just continue seamlessly?

what i've tried:

• using group_id to track which agent conversation this belongs to (agent_name + conversation_id pattern)
• storing handoff notes as separate messages with specific content patterns
• retrieving with different queries to get focused vs broad context

questions:

• anyone found good patterns for explicit handoff documentation?
• how do you handle agent specialization in memory formation? (e.g., technical agent creates more detailed technical memories)
• is there a way to mark certain memories as "verified by specialist" vs "initial assessment"?

the episode formation seems to work well for maintaining conversation continuity, but wondering if there are better approaches for multi-agent scenarios.

curious how others handle the balance between shared context and agent-specific expertise.