The AI Taxonomy.
Terms  /  Architecture  /  Context Handoff
05 · Architecture

Context Handoff

The ability to transfer full working context from one agent, session, or tool to another without loss. A specific operation enabled by Context Portability.

Context Handoff is what happens in the moment of transition. When you switch from one agent to another, start a new session, or bring a colleague into a project, the Context Handoff determines how much knowledge survives the transition. A perfect handoff means zero loss: the receiving agent or session has everything the previous one had. A poor handoff means starting over. Context Handoff is the operational test of whether Context Portability actually works in practice.

Example
A developer is working with a coding agent on a complex debugging session. After two hours, they need to hand off to a colleague in a different timezone. With a good Context Handoff: the colleague's agent receives the full debugging context — what was tried, what was ruled out, what the current hypothesis is, and what to try next. Without it: the colleague starts from "there's a bug in the payment module" and spends the first hour re-discovering what the first developer already found.

More in Architecture

System-Centric AI

An architecture where planning, state, memory, and control live outside the model.

Model-Centric AI

An architecture where the model is expected to infer state, intent, plan, and memory from raw context.

Tool Isolation

Memory and context don't cross tool boundaries — each tool is a silo.

The Expertise Trap

Context and knowledge locked on one person's machine, inaccessible to the team.

Agent Fragmentation

Agents operating in disconnected silos with no shared state, duplicating work and contradicting each other.

Unified Context

A single, shared knowledge layer accessible to all agents and tools. The cure for Tool Isolation.

Context Portability

The ability to move knowledge between tools, agents, sessions, and team members without loss. The cure for The Expertise Trap.

Stateful, Inspectable Memory

A collaborative memory space where multiple agents and humans contribute to and draw from the same knowledge base — with full history and traceability.