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.
03In production
A consumer-electronics support team replaced their tier-1 routing with an AgentPrizm-backed agent. Before: the agent could read the ticket but not the customer's last six interactions. After: the agent walked into every conversation with the same context a tenured rep would have.
- Repeat-question complaints dropped to near-zero.
- The agent could close 31% more cases without escalation.
- Average handle time fell because the customer didn't have to re-explain.
04Measured impact
| Metric | Before | With AgentPrizm | Δ |
|---|---|---|---|
| Avg handle time, tier-1 | 7m 12s | 4m 58s | −31% |
| Tickets closed without escalation | 42% | 61% | +45% |
| CSAT (post-AI interaction) | 3.6 / 5 | 4.4 / 5 | +22% |
| Repeat-question complaints | 17/wk | 1/wk | −94% |
Measured across 6+ design partners on Scale, Q1 2026. Your mileage will vary; we'll run an eval with you.
05Integrations
Native adapters for Zendesk, Intercom, Salesforce Service Cloud, Front, and Help Scout. Memory ingested from ticket threads, order systems, and your knowledge base.
See it on your data.We'll run a 2-week eval against your existing agent — same prompts, same model, only the memory layer changes. Start a pilot.