What is Agent Memory?
Agent Memory helps your AI agents remember important details about the people they work with. Instead of starting every conversation from scratch, your AI can remember that Sarah likes quick updates, John’s team is preparing for the Q4 launch, or that the marketing team prefers longer explanations. Think of it as giving your AI agents the ability to build relationships and provide more personalized help over time.Build Better Relationships
Your AI agents can create genuine connections by remembering context and personalizing interactions.
Stop Repeating Yourself
No more answering the same questions every time you talk to your AI agent.
Work More Efficiently
Get more relevant, helpful responses that save you time.
Stay Compliant
Built-in protection for data privacy that meets global standards.
The key point: Agent Memory turns your AI agents from basic tools into truly intelligent helpers that understand your context.
How Agent Memory Works: Three Levels of Control
Agent Memory uses a three-layer system that gives you complete control over how your organization uses AI memory.- Layer 1: Organization Policy
- Layer 2: Agent-Specific Configuration
- Layer 3: Individual Control
Global Agent Configuration - Set your company-wide memory policyMaster Switch: Enable or disable memory collection across your entire organizationPolicy Guidance: Define what types of information align with your business goals. For example, “Do not store address information”.Consistency: Ensure all agents follow the same organizational principles
This layer establishes your organization’s “memory philosophy” and ensures consistent policies across all AI deployments.
What Gets Remembered
Agent Memory intelligently categorizes information into meaningful groups:Professional Context
Job roles, team relationships, project involvement
Communication Preferences
Meeting styles, information formats
Work Patterns
Availability, deadlines, collaboration styles
Goals & Objectives
Project priorities, professional development
Each fact includes full context: which conversation it came from, which agent collected it, and when.
Administrative Tools
Human Facts Management
Navigate to any individual’s Human Facts tab to access:📊 Facts Table
📊 Facts Table
Central dashboard showing all collected information, sortable and searchable (Coming Soon)
✏️ Edit Capabilities
✏️ Edit Capabilities
Modify any fact while maintaining complete audit trails (Coming Soon)
📥 Export Functions
📥 Export Functions
One-click exports in CSV format for compliance reporting
⚡ Bulk Operations
⚡ Bulk Operations
Select multiple facts for batch operations (Coming Soon)Currently we can delete all of a user’s facts via bulk operations
Compliance Features
Automatic Legal Framework Support:- EU AI Act (2024): 6+ month logging retention and decision transparency
- GDPR: 30-day response automation for all data subject rights (Articles 15-22)
- CCPA/CPRA: 45-day processing with comprehensive deletion capabilities
- US State Laws: Virginia, Colorado, Connecticut, Utah coverage
- PIPEDA: Full accountability and access rights implementation
- Right to Access: Instant visibility of all collected facts
- Right to Rectification: Direct editing with full audit trails (Coming Soon)
- Right to Erasure: Complete deletion with verification across all systems
- Right to Portability: Professional data exports with metadata
Real Examples of How Agent Memory Works
🎧 Customer Support Excellence
Your customer support AI talks to Sarah, who calls monthly about her software setup. Without memory, every call starts over. With memory, the AI agent immediately knows:
- Her technical setup details
- She prefers brief, technical explanations
- Her team’s current project
Setup: Enable both reading and writing memory, with guidance focusing on technical preferences and solution history.
📊 Project Management Help
Your project management AI works with multiple teams. Memory helps it remember:
- Who does what on each team
- Project deadlines and dependencies
- Context across different project phases
Setup: Full memory abilities with guidance emphasizing project context, deadlines, and team relationships.
🔒 HR Conversations
Your HR AI handles sensitive discussions. You might set it up to:
Read existing context for continuity
Read existing context for continuity
Without storing new sensitive information
Not write new memory
Not write new memory
To prevent storing confidential details
Follow strict guidance
Follow strict guidance
About appropriate workplace context
Setup: Reading enabled, writing disabled, with very specific guidance about appropriate context.
Getting Started
Set Your Foundation
Start with your organization’s memory policy. Go to Global Agent Configuration and establish guidelines about what information would genuinely help your AI agents provide better service.
Try It Out
Choose 1-2 AI agents for initial testing. Customer support and project management agents often show immediate value. Enable both _reading _and contributing with specific guidance about what to remember.
Watch and Improve
Monitor how your AI agents use memory over the first few weeks. Are they collecting useful information? Are users finding conversations more helpful? Use the Human Facts tab to review what’s being learned.
Expand
Based on your trial results, add memory to more AI agents. Each type may need different guidance - your sales agents focus on different information than your technical support agents.
Pro Tip: Start with AI agents that have frequent repeat conversations - they’ll show the most immediate value from remembering context.
Common Questions
- Setup Questions
- Privacy Questions
- Troubleshooting
How do I know if Agent Memory is working?
How do I know if Agent Memory is working?
Look for these signs: Users stop repeating basic information, AI agents give more relevant responses, and you see fewer “starting from scratch” conversations. The Human Facts tab will show growing, useful information over time. An admin would know that memory is working on a specific agent if they tell the agent a fact in the Preview tab and the 💡 fact status update shows
What's the difference between reading and contributing memory?
What's the difference between reading and contributing memory?
Reading means the AI can use existing information about someone. Contributing means the AI can learn and store new information. You might have some AI agents that only read context, and others that focus on learning new information.
How specific should my memory guidance be?
How specific should my memory guidance be?
Be as specific as helpful. Instead of “remember user preferences,” try “remember preferred communication style (email vs. phone), typical project deadlines, and team collaboration preferences.”
Agent Memory transforms transactional AI interactions into intelligent, evolving relationships while maintaining the highest standards of data protection and regulatory compliance.