Agent Memory Protocol for Autonomous AI Systems

Agent Memory Requirements

Autonomous AI agents require sophisticated memory management: working memory for current task, episodic memory for interaction history, and long-term memory for learned knowledge. ECM Protocol provides memory primitives.

Memory Types

Working Memory

Current task context and state:

{
  "memory_type": "working",
  "agent_id": "agent-123",
  "task_id": "task-456",
  "contents": {
    "goal": "Resolve customer billing issue",
    "current_step": "Verify payment history",
    "gathered_info": {...},
    "pending_actions": [...]
  },
  "ttl": 3600
}

Episodic Memory

Past interactions and experiences:

{
  "memory_type": "episodic",
  "agent_id": "agent-123",
  "episode": {
    "timestamp": "2024-01-15T10:00:00Z",
    "type": "customer_interaction",
    "summary": "Resolved billing discrepancy for premium customer",
    "outcome": "positive",
    "learnings": ["Check payment gateway logs for discrepancies"]
  }
}

Long-Term Memory

Persistent knowledge and patterns:

{
  "memory_type": "long_term",
  "agent_id": "agent-123",
  "knowledge": {
    "domain": "billing",
    "fact": "Gateway X often has delayed settlement on weekends",
    "confidence": 0.9,
    "source_episodes": ["ep-1", "ep-2", "ep-3"]
  }
}

Memory Operations

Store

Store memory with appropriate type. Set TTL for working memory. Index episodic memory for retrieval.

Retrieve

Retrieve relevant memories. Semantic search for related episodes. Recency weighting for temporal relevance. Importance weighting for priority.

Consolidate

Move from episodic to long-term. Extract patterns from episodes. Build generalized knowledge. Prune redundant memories.

Memory Retrieval Protocol

{
  "operation": "memory.retrieve",
  "query": {
    "semantic": "customer billing issues with payment gateway",
    "memory_types": ["episodic", "long_term"],
    "recency_weight": 0.3,
    "relevance_weight": 0.7
  },
  "max_results": 10
}

Memory Decay

Forgetting Mechanisms

Implement memory decay. Working memory has short TTL. Episodic memory importance decays over time. Long-term memory more stable but can be updated.

Active Forgetting

Remove memories when appropriate. Privacy requirements (user deletion). Outdated information. Conflicting information resolved.

Conclusion

The Agent Memory Protocol enables sophisticated memory management for autonomous AI systems. Working, episodic, and long-term memory types support different agent needs with appropriate lifecycle management.

> Agent Memory Protocol for Autonomous AI Systems