Memory for support agents.

01The problem

Stateless support agents are why customers say "agent" the way they say "phone tree." The agent asks for the order number, the customer pastes it. The agent asks for the email on file, the customer pastes it. The agent escalates anyway. The deflection rate looks great on the dashboard and the CSAT score is in the toilet.

02What memory unlocks

• Customer memory persists across tickets, channels, and sessions. Order history, recent issues, preferred resolution, accommodations — all there before the first message.
• Event timelines with validity windows give the agent a real picture of the customer's recent state.
• Contradiction-aware recall stops the agent from offering a refund the customer already received.
• Audit traces for every recommendation make compliance reviews go in minutes instead of weeks.

03What it looks like

We built AgentPrizm's memory model for exactly this: an agent that walks into every conversation with the context a tenured rep would have — order history, the customer's last six interactions, prior resolutions — instead of asking the customer to introduce themselves three times. The difference shows up in the mechanics:

• Customer context is loaded before the first message, not re-collected mid-chat.
• Validity windows give the agent a real picture of the customer's recent state.
• Contradiction handling stops the agent from offering a refund the customer already received.

04Illustrative impact

Illustrative targets that model the mechanism above — not results from a named customer. We'll run an eval on your own data.

05Integrations

Native adapters for Zendesk, Intercom, Salesforce Service Cloud, Front, and Help Scout. Memory ingested from ticket threads, order systems, and your knowledge base.

Wiring is the same regardless of vertical: ingest and recall over the memory API, or connect any MCP-capable agent with the five-minute quickstart.