Shared context for agents: how teams and agents work on the same data
When multiple agents or teammates each hold their own copy of the context, they overwrite each other and drift out of sync. The fix is a shared data layer: one source of truth that every agent and every person reads from and writes to. This guide explains why shared state, not more parallel agents, is the real unlock.
Why does multi-agent work break down?
Running more agents in parallel does not help if each one has its own view of the data. The moment two agents touch the same files, their changes collide, and you cannot tell which agent holds the latest state. The bottleneck is rarely the agents themselves, it is keeping track of who changed what.
What is shared state, and why does it matter?
Shared state means the files, datasets, and decisions live in one place that every agent and person reads and writes through, instead of each keeping a private copy. With a single source of truth, a change one agent makes is immediately visible to the next, and nobody works against a stale version.
How do a team and its agents stay in sync?
The reliable pattern is one canonical store that everyone reads and writes through clear rules, rather than passing copies around. Agents reference the shared data instead of cloning it, so there is nothing to reconcile afterward and no lost handoffs.
How does adlass provide shared context?
adlass is a shared data layer where you, your team, and their agents work over the same Spaces, files, datasets, and skills, connected over MCP. Everyone operates on one source of truth with per-Space permissions, so multiple agents and people can work in parallel without overwriting each other or losing track of state.
Deeper guides
- How do multiple agents work on the same files without conflicts?
- How do you hand off context between agents?
- How can a team and their agents work on the same documents?
In this guide
- How do multiple agents work on the same files without conflicts?
Agents conflict because each works on its own copy and writes back blindly. A shared data layer with one source of truth removes the overwrites.
- How do you hand off context between agents?
Handoffs break because they pass a transcript instead of shared state. A shared data layer removes the handoff: the next agent reads the same source directly.
- How can a team and their agents work on the same documents?
A shared folder is not enough once agents join. You need one source of truth with write access and permissions, so people and agents work the same documents.
Frequently asked questions
- Does running more agents in parallel make work faster?
- Only if they share state. Without one source of truth, parallel agents overwrite each other and you spend the saved time reconciling conflicts. Shared state is what makes parallelism pay off.
- Is shared context the same as agent memory?
- No. Memory is usually private recall for one agent. Shared context is the actual files and state that every agent and teammate reads and writes, so work carries across all of them.
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.