AI Memory in ServiceNow

When it comes to AI, memory is one of the most essential, and least understood, ingredients of intelligent applications.

Most people imagine memory as a static database: information goes in, and later, if you’re lucky, it comes back out. But inside ServiceNow, memory isn’t a bolt-on. It’s a dynamic system embedded into the AI framework, enabling agents to learn, recall, and personalise interactions across time.

Memory is what transforms a chat into a relationship.

The Anatomy of Memory in ServiceNow

At the heart of ServiceNow’s memory system are three layers that mirror how humans think and recall:

1. Short-Term Memory (STM) – ephemeral context within a conversation. It helps the agent keep track of the current flow and respond coherently.

2. Episodic Memory – the historical record of interactions and events: chats, incidents, actions.

3. Long-Term Memory (LTM) – the enduring facts and preferences that persist across sessions, linked to individual users.

Long-term memory is where the magic happens. It lets an agent say, “Welcome back, I remember your last laptop issue,” without ever needing a case history in front of it.

Behind the scenes, this system runs on a clean schema:

• Memory Categories (sn_aia_ltm_category) define what types of information can be remembered.

• Agent–Category Mapping (sn_aia_ltm_category_mapping) controls which agents can access which memories.

• Memory Table (sn_aia_memory) stores the individual facts tied to users and categories.

Each memory has a relevance score that increases when it’s recalled successfully, effectively teaching the system what’s most valuable to remember.

From Conversation to Recall

The flow looks something like this:

User → Agent → Memory Category → Memory Table → Retrieval → Response

When a user mentions something memorable like “I prefer Teams calls”, the agent checks whether it’s allowed to store that fact. If so, it saves it under the right category.

In future sessions, when context demands, the agent retrieves the fact, injects it into its reasoning chain, and responds accordingly.

The more a memory is used, the stronger its “relevance” becomes, keeping the system both personalised and efficient.

Beyond the Defaults

Once you start customising memory, it gets interesting.

Each layer offers opportunities for intelligence and control:

• Agent Layer – Define when an agent should remember or forget. A conversational agent might store preferences; a troubleshooting agent might remember configurations.

• Retrieval Layer – Optimise how memories are fetched and ranked; use relevance scores or recency filters to keep responses sharp.

• Governance Layer – Implement privacy rules, retention limits, and user-controlled deletion to balance usefulness with trust.

• Prompt Layer – Decide how and when memories are surfaced in the conversation (“Last time you said…” versus silent context injection).

This modular design turns memory into a programmable capability rather than a black box.

The Bigger Picture

This shows that memory isn’t just a feature, it’s the connective tissue between conversations, data, and experience that makes the system "intelligent".

In ServiceNow, it’s what allows AI agents to evolve from reactive assistants to proactive collaborators. By combining memory with orchestration, retrieval, and reasoning, you create systems that not only know what’s happening now but remember what happened before.

And that continuity is the essence of intelligence.

Closing Thoughts

Memory in ServiceNow isn’t magic, it’s architected using tables just like any other feature in the platform.

Each table, mapping, and relevance score is a piece of a larger pattern designed to make AI human-centred, contextual, and trustworthy.

As AI agents mature, memory will define their personalities as much as their capabilities. The so-called smartest systems won’t just generate answers, they’ll remember why those answers matter.