Conversation Segmentation

When an AI agent talks to multiple tenants simultaneously — humans and other agents — how do you keep each conversation coherent? This interactive demo visualizes the segmentation architecture I designed to solve it.

Multi-Tenant Agents A2A Protocol Context Isolation Agentic AI

The Problem

In production agentic systems, a single agent instance receives interleaved messages from multiple sources — end users, orchestrator agents, tool-calling agents — all in one stream. Without segmentation, contexts bleed across tenants: a response meant for User A leaks into Agent B's thread.

The Solution

Conversation segmentation classifies each incoming message by tenant identity and conversational context, routing it to an isolated thread with its own state. Critically, threads share a common memory layer — so cross-cutting knowledge flows without leaking unrelated context.

With Segmentation Without Segmentation
Play Reset
Incoming Stream
Segment
& Route
Customer A
Developer B
Monitor
Billing
Shared Memory — Cross-Thread State

How It Works

1
Intercept
Every incoming message is intercepted before reaching the agent's context window
2
Classify
Tenant identity and conversation thread are determined from metadata and content
3
Isolate
Each tenant gets its own context window with isolated conversation state
4
Share
Threads read and write to shared memory for cross-cutting knowledge