Building Memory Systems for Agentic AI

The Memory Problem

Agentic AI systems have a fundamental challenge: they're stateless by default. Every API call starts fresh. Every context window is a clean slate.

But real workflows aren't stateless. Users expect continuity. Agents need to remember decisions they made three steps ago. And production systems can't afford to repeat expensive operations.

Memory Taxonomies

Not all memory is created equal. A robust agentic system needs multiple memory types:

1. Working Memory

The immediate context of the current task. This lives in the context window and includes:

• Current user message
• Recent conversation history
• Active task state
• Retrieved context

interface WorkingMemory {
  conversation: Message[];
  taskState: TaskState;
  retrievedContext: Chunk[];
  scratchpad: string; // Agent's "thinking" space
}

2. Episodic Memory

Records of past interactions and events. This is your conversation history, but structured:

interface Episode {
  id: string;
  timestamp: Date;
  participants: string[];
  summary: string;
  keyDecisions: Decision[];
  artifacts: Artifact[];
}

3. Semantic Memory

Long-term knowledge extracted from interactions. This is where RAG lives, but it's more than just documents:

• User preferences learned over time
• Domain knowledge accumulated
• Relationship graphs

4. Procedural Memory

How to do things. This is often overlooked but critical for agentic systems:

• Successful action sequences
• Failed attempts (and why)
• Optimization heuristics

Architecture Patterns

The Memory Manager

Every agent should have a memory manager that handles the complexity:

class MemoryManager {
  async remember(experience: Experience): Promise<void> {
    // Store to appropriate memory types
    await this.working.update(experience);
    await this.episodic.record(experience);

    // Extract and store learnings
    const learnings = await this.extract(experience);
    await this.semantic.index(learnings);
  }

  async recall(query: string, context: Context): Promise<Memory[]> {
    // Multi-source retrieval
    const episodic = await this.episodic.search(query);
    const semantic = await this.semantic.search(query);
    const procedural = await this.procedural.match(context.taskType);

    // Rank and merge
    return this.rank([...episodic, ...semantic, ...procedural]);
  }
}

Memory Consolidation

Like human memory, AI memory benefits from consolidation—the process of compressing and abstracting experiences:

// Nightly consolidation job
async function consolidateMemory(agent: Agent) {
  // Summarize recent episodes into key learnings
  const episodes = await agent.memory.episodic.getRecent(24h);
  const summary = await llm.summarize(episodes);

  // Update semantic memory with extracted facts
  await agent.memory.semantic.index(summary.facts);

  // Update procedural memory with successful patterns
  await agent.memory.procedural.record(summary.patterns);

  // Prune redundant episodic memories
  await agent.memory.episodic.prune({ keepSummaries: true });
}

Multi-Agent Memory Sharing

When multiple agents collaborate, memory becomes even more complex:

• Shared context: What all agents need to know
• Private memory: Agent-specific knowledge
• Communication logs: Inter-agent messages

The key is defining clear memory boundaries and access patterns.

Production Considerations

Persistence

Memory isn't memory if it doesn't persist. Choose your storage wisely:

• Working memory: In-process, fast
• Episodic: Document store (MongoDB, DynamoDB)
• Semantic: Vector database
• Procedural: Graph database or specialized store

Privacy and Security

User memories contain sensitive information. Implement:

• Memory encryption at rest
• Access controls per memory type
• Retention policies and deletion
• Audit logging

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Memory systems are the foundation of truly intelligent agents. Get them right, and your AI can learn, adapt, and improve over time.