How Memory Makes Agentic AI Smarter

Memory is essential in human interactions. Imagine having a talking to someone who forgets everything you've ever said every time you start a new conversation - it would quickly become very frustrating. Agentic AI systems face a similar challenge: without memory (large language models don’t have persistent memory built in), every conversation starts from zero and feels disconnected.

I build structured memory into agents so they can carry useful context forward, learn from previous outcomes, and feel more like a capable teammate than a one‑off tool. I focus on two complementary forms of memory: semantic memory (facts and context) and episodic memory (what happened in past interactions). This keeps responses relevant, decisions consistent, and recommendations steadily improving over time. For a broader background on different memory types in agent systems, Harrison Chase’s (CEO of LangChain) writing on agent memory is a good place to start - link at the bottom of this post.

Semantic Memory: The Agent's Understanding

Think of semantic memory as the agent’s “general knowledge” about you, your brand, and your working style. I typically structure it into three layers:

1. User Memory (private to each individual)

   • Preferences, communication and working styles.
   • Only accessible when logged in as a specific user; not shared with other users.
   • Example: “Greg prefers concise weekly campaign summaries with key insights and recommendations.”

2. Workspace/Account Memory (shared within a certain context or group)

   • Information that should be consistent for a team working in the same account or client context (e.g., a Google Ads or Meta Ads account).
   • Examples could include: Brand guidelines, tone of voice, product catalogues, compliance notes, recent creative assets.
   • Ensures consistent outputs regardless of who on the team is interacting with the agent.

3. General/Global Memory (organisation or project wide)

   • Universal practices and “house rules” (e.g., naming conventions, reporting cadences, core QA checklists).
   • Keeps quality and safety high across all tasks.

This layering means the right facts are available at the right times when the agent needs them, without sharing custom user details and preferences.

Episodic Memory: Learning From What Actually Happened

Episodic memory is about events and outcomes. It records what the agent recommended, what the user chose to do, and what happened next.

• If an agent suggested tightening geo‑targets last month and performance improved, it can factor that into future advice.
• If a bid strategy change was rejected (and the user gives a reason why), it won’t blindly repeat the suggestion.
• When making a recommendation, the agent fetches the most recent and most relevant past episodes to ground its reasoning.

Over time, this builds a feedback loop: the more the system sees what works for you and your accounts, the more accurate its next recommendation becomes.

How I Implement and Manage Memory

Under the hood, I use vector search to store and retrieve both semantic and episodic memories. Each memory is embedded (turned into a numerical vector) so the agent can quickly pull back the most relevant snippets for the task at hand through semantic search.

Two retrieval patterns keep things practical:

• Proactive retrieval: Before a task where memory is always useful (e.g., “write fresh ad variants”), the agent fetches brand rules, key product facts, and any recent performance notes.
• Reactive retrieval: During a conversation, if a follow‑up question needs more context (e.g., “Which audience performed best after we changed creative?”), the agent fetches episodic records on demand.

Creating and Editing Memories

• Automatic (agent‑initiated): If the agent encounters a new fact (e.g., updated product pricing or a clarified tone rule), it proposes a memory entry and clearly signals that a save is occurring. It can require confirmation for sensitive categories.
• Manual (user‑initiated): Users should be able to add, edit, and delete memories through a simple UI. I prioritise transparency, showing what’s stored and giving the user easy controls to amend or remove any incorrect or outdated information.

Privacy, Governance, and Control

• Access control by layer: User memories stay private; workspace memories are shared only within that context; global rules are read‑only to most users.
• Change log & retention: Key updates are logged; outdated memories can auto‑expire (especially episodic ones) to keep context fresh.
• Opt‑in for sensitive data: The system asks before storing anything sensitive or long‑lived.

Why Memory Matters

1. Better decisions, less back‑and‑forth. With relevant context at hand and a track record of what worked, agents give more accurate, explainable recommendations.
2. More natural interactions. Conversations feel continuous and personalised. You’re not repeating yourself; the agent “remembers”, "learns" and adapts.
3. Stronger consistency. Shared workspace and global memory enforce brand, compliance, and naming rules, even across multiple teammates and campaigns.

If you’re managing ad accounts, refining creative and messaging, or running recurring optimisation cycles, memory moves your agent from “clever assistant” to reliable collaborator.

Further reading:

• Harrison Chase, Memory for Agents (LangChain blog)