How do you hand off context between agents?
Part of Shared context for agents: how teams and agents work on the same data
Handoffs between agents break because people pass a transcript or a summary instead of the actual state, and detail gets lost in the copy. The real fix is to remove the handoff: keep the work in a shared data layer that the next agent reads directly, so nothing has to be transferred. The hard part of multi-agent setups is rarely the agents, it is the handoff layer.
Why are handoffs the hard part?
Every handoff is a lossy copy. When one agent finishes research and another starts writing, someone has to move the context between them, usually by pasting it. Each copy drops detail and adds a chance for the next agent to work from a stale or partial picture.
What does a good handoff carry?
A useful handoff is more than the transcript. It needs the decisions made, the files touched, and the current state of the work, in a form the next agent can act on directly. Reconstructing that by hand on every step is exactly where multi-agent workflows fail silently.
How does a shared layer remove the handoff?
When the work already lives in a shared data layer, there is nothing to hand off. The next agent reads the same files and state the previous one wrote, so it picks up exactly where the work was left. With adlass, every agent operates on the same Space over MCP, so research, writing, and review share one source of truth instead of copying context between sessions.
Related comparisons
Frequently asked questions
- Why do my multi-agent workflows fail silently?
- Usually at the handoff: context is copied between agents and detail is lost, so a later agent works from a stale picture. Sharing one source of truth removes the lossy copy.
- Is passing the full transcript a good handoff?
- No. A transcript carries words but not the structured state, files, and decisions the next agent needs to act. A shared layer lets it read the real state instead.
Work with your agents on the same data
adlass is the shared data layer where you, your team, and their agents work over the same documents and datasets.