Introducing Memory in Siesta AI

We’re introducing Memory, AI knowledge base capabilities in Siesta AI that let you store long-term, high-signal company knowledge, then attach it to specific Agents.

With Memory, teams can keep processes, product documentation, policies, FAQs, and playbooks in a structured space that Agents can reference as primary context when responding. The operational problem it solves is familiar: answers live across wikis, drives, and old threads, so people spend time searching, re-asking, and rebuilding the same context. When an agent doesn’t have a trusted source of truth, responses drift, teams lose confidence, and adoption stalls.

Why Memory matters

In most organizations, knowledge is not “nice to have”, it’s part of daily execution. Teams rely on documented rules: what we promise customers, how we handle exceptions, who approves what, which steps matter, which system is the source of truth.

Memory is designed to make that knowledge easier to apply in day-to-day work. Instead of relying on tribal memory or yet another repository that becomes outdated, you create a maintained knowledge layer inside Siesta AI, then connect it directly to the Agents people actually use. Update the source once, and your Agents pick up the latest guidance without rebuilding prompts or reconfiguring workflows.

A practical implication: Memory reduces the hidden cost of enterprise AI, repeated context-setting. If users keep pasting the same background into chats to get a usable answer, adoption slows. Memory moves that context into a shared, governed place.

Collections: separate knowledge by team, project, or sensitivity

Knowledge in Memory is organized into Collections. Think of them as thematic folders that map to how an organization actually operates: Marketing enablement, Sales playbooks, HR policies, Security standards, or a project-specific rollout.

Collections also give you basic administration controls to manage how knowledge is organized and who can access it. In real environments, some knowledge should be broadly accessible, while other content needs tighter control. A clean pattern is to keep shared baseline collections for company-wide workflows, and team-private collections for details only certain roles should use day to day. This reduces the risk of Agents mixing policies across teams and makes it easier to govern what each Agent is allowed to reference.

Pages and nesting: build maintainable playbooks, not document sprawl

Inside each Collection, you create Pages. Pages can be nested into subpages so you can build a hierarchy like an internal wiki, rather than a flat list of notes.

Nesting is what keeps knowledge maintainable over time. A top-level page like “Customer Support Handbook” can have subpages for tone of voice, escalation paths, refund policy, known issues, and system-specific procedures. When something changes, you update one place, instead of chasing multiple slightly different versions across tools.

A content editor that supports structure and retrieval

Each Page opens an editor where you can format content with headings (H1 to H6), lists, links, and code blocks.

This is not just about readability. Structure improves retrieval. Well-labeled sections and explicit rules are easier for humans to scan, and easier for Agents to find and apply correctly.

A simple writing guideline that improves answer quality: write Memory pages the way you would want to read them during a busy moment. Use short sections, clear definitions, decision rules, edge cases, and links to the system of record. The clearer the structure, the fewer almost-correct answers you will need to fix.

Attach Memory to Agents, keep knowledge current without rework

Memory becomes operational when you attach a Collection or Page to an Agent.

Once linked, the Agent can prioritize those sources when answering questions, grounding responses in your approved content and using your company terminology. When you update a Page, connected Agents can use the updated guidance without needing manual prompt updates. This is the difference between an Agent that responds based on general patterns, and an Agent that responds based on how your company actually works today.

Example: company contacts that stay current

A strong starting point is a Company Contacts page. Most teams already have this information somewhere, but it’s usually fragmented across org charts, spreadsheets, and old messages.

In Memory, you can capture contacts in a structured way: who owns a domain, who the backups are, and where to reach them. Attach that page to an internal help Agent, and employees can ask questions like:

• Who owns vendor onboarding
• Who is the backup for payroll approvals?
• Where do I escalate a customer issue?

When responsibilities change, you update the page once, and connected Agents can use the updated ownership structure without rework.

Availability and how to get started

Memory is available in Siesta AI now. Create a collection for a team, add a few pages (processes, FAQs, definitions), and attach the relevant collections or pages to the agents that need them.

Read more: https://docs.siesta.ai/memory

If you’re rolling Memory out across multiple teams and need a clean permission model and governance setup, schedule a demo to walk through your rollout and best-practice structure.