Customer Support with Persistent Agent Memory
Give support agents persistent memory of every customer interaction across sessions, tools, and handoffs — eliminating context loss and reducing resolution times.
The situation.
Context before architecture.
Customer support teams often handle complex issues that span multiple channels — email, chat, phone — and involve multiple agents over several days or weeks. Without a shared memory layer, each handoff forces the customer to repeat their issue, and each agent starts with a blank slate. OpenZync provides a persistent, graph-based memory that preserves context across every interaction.
The problem.
Most support stacks use disconnected tools — ticketing systems, live chat platforms, and CRMs — that don't share context with each other. When a customer follows up on an email thread via chat, the chat agent has no visibility into the email history. This leads to repeated questions from customers, longer resolution times, and increased frustration on both sides. Agents spend more time catching up on context than actually resolving issues.
How OpenZync addresses it.
OpenZync acts as a graph-based memory layer between existing support tools. Every customer interaction — email, chat, phone transcript, and order note — is ingested into a per-customer knowledge graph that tracks entities (products, orders, accounts), relationships (escalation paths, agent assignments), and resolution history.
Integration happens via webhooks with existing support platforms, and directly with the organisation's databases using the Multi-Graph Backend adapter. When a support agent opens a ticket, the OpenZync API returns a structured context brief: customer sentiment trend, unresolved issues, recent interactions, and suggested resolutions based on similar past tickets.
The system can be rolled out incrementally — starting with one channel and expanding — with minimal disruption to existing workflows.
Technical architecture.
OpenZync is deployed as a standalone service alongside existing infrastructure. The graph backend uses PostgreSQL (via OpenZync's native pgvector adapter) or any supported graph backend for persistence, with a Redis cache for real-time agent lookups. Each customer is represented as a node with edges to orders, tickets, and support agents. Resolution patterns are stored as sub-graphs that the agent similarity engine queries at runtime.
The integration surface is minimal: webhook handlers for ticket creation and status changes, and a single API call per agent session. OpenZync's idempotent execution guarantees prevent duplicate context entries even during retries.
What you get.
The durable result of running this scenario on the memory layer.
Graph-based memory eliminates context loss across disconnected tools without replacing the existing stack.
Incremental rollout minimises disruption and builds team confidence.
Structured context briefs at ticket open significantly reduce average handle time.
Read the next one.
Research Assistant with Persistent Memory
AI research agents that maintain context across weeks-long literature review sessions using persistent citation graphs and entity tracking.
Read the use caseContext-Aware Automated Code Review
A code review agent that remembers project conventions, past PR feedback, and architectural decisions across the entire repository history.
Read the use caseBuild this scenario on your own infrastructure.
Set up in minutes, not days. Docs, the SDK, and the MCP server ship alongside the core.
Run the memory layer on your own infrastructure.
Start free on the hosted instance, or self-host the AGPLv3 core in your own VPC. No credit card, no sales call.