Bay Information Systems

Memory in Agentic AI

Agentic AI refers to AI systems that exhibit autonomy, decision-making capabilities, and memory persistence over interactions. Memory is a crucial component in such agents as it allows them to retain context, learn from past experiences, and improve their responses over time.

The emerging design pattern for agentic AI involves structuring memory into different layers, often using databases (vector stores, relational DBs, or key-value stores) to manage information at various levels.

Per-Agent Memory (Long-Term Memory)

• Scope: Persistent across all interactions with the agent.
• Storage: Uses databases like PostgreSQL, Redis, or vector databases (e.g., Pinecone, Weaviate, FAISS).
• Purpose:
  • Stores overarching knowledge, personality, and agent-specific learnings.
  • Tracks user preferences, history, and behavior across multiple interactions.
  • Enables personalization and long-term adaptability.
• Example Data Stored:
  • User profile details, past queries, interests.
  • Historical interaction summaries.
  • Evolving strategies and heuristics for decision-making.
• Extracted Insights:
  • Behavioral trends, recurring themes in user requests.
  • Personalized recommendations based on past interactions.

Per-Interaction Memory (Session Memory)

• Scope: Limited to a single user session or chat interaction.
• Storage: In-memory stores (e.g., Redis, SQLite, session-based key-value storage).
• Purpose:
  • Maintains short-term context within an ongoing conversation or session.
  • Prevents the agent from repeating itself or forgetting relevant details mid-session.
  • Ensures coherence in multi-turn conversations.
• Example Data Stored:
  • Recent conversation history (messages, timestamps).
  • Contextual references (e.g., “we were discussing X earlier”).
• Extracted Insights:
  • Sentiment and intent trends within a session.
  • Anomalies in conversation flow (e.g., user confusion).
  • Patterns in user engagement.

Per-Task Memory (Workflow or Process Memory)

• Scope: Specific to an ongoing task, goal, or multi-step process.
• Storage: Graph databases (e.g., Neo4j), workflow engines, or task-specific logs.
• Purpose:
  • Tracks progress within a complex workflow.
  • Ensures consistency in decision-making across steps.
  • Allows the agent to resume tasks without restarting from scratch.
• Example Data Stored:
  • Steps completed and pending in a workflow.
  • Intermediate outputs and dependencies.
  • Task-specific parameters and constraints.
• Extracted Insights:
  • Bottlenecks and inefficiencies in workflows.
  • Success/failure analysis of task execution.
  • Adaptive strategies for better completion rates.

Per-Experience Memory (Episodic Memory)

• Scope: Captures meaningful episodes across multiple interactions.
• Storage: Vector databases for embeddings, knowledge graphs.
• Purpose:
  • Captures key learnings from distinct user-agent experiences.
  • Provides context-aware decision-making by recalling past encounters.
  • Facilitates adaptation without explicit retraining.
• Example Data Stored:
  • Summarized takeaways from past interactions.
  • Key entities, intents, and outcomes.
• Extracted Insights:
  • Longitudinal patterns of user behavior.
  • Knowledge gaps requiring further training.

Memory & Learning

Memory does not necessarily mean “learning” in the machine learning sense. Instead, learning in agentic AI happens through:

• Pattern Extraction - The agent derives insights from stored memories.
• Reinforcement Mechanisms - Feedback loops (human-in-the-loop or automated) adjust responses.
• Fine-Tuning & Updates - Incorporating new knowledge into the agent’s base model.
• Symbolic & Hybrid AI - Some systems use structured reasoning in combination with memory stores.

Data Inference & Extraction

Depending on the memory layer, different types of insights can be extracted:

• User Profiling - Interests, sentiment shifts, and engagement trends.
• Intent Prediction - Anticipating user needs based on past interactions.
• Workflow Optimization - Detecting inefficiencies and improving task execution.
• Contextual Recall - Generating more contextually appropriate responses.

Summary

The structured memory design allows AI agents to balance short-term contextual awareness with long-term learning. By integrating databases and retrieval mechanisms, agents can operate more effectively, offering continuity and personalization without excessive model retraining.