I’m setting up workflows where multiple AI agents operate in parallel but need to share and update a single source of truth—like a shared context document or state object. I’m trying to figure out how to configure them so that state updates don’t collide or overwrite each other. Are there specific blocks in Latenode that handle locking or versioning when multiple agents work concurrently? How do you ensure safe, consistent shared state updates across multiple agents running in parallel? Would love to see real examples if anyone has tackled this.
Latenode has built-in features to handle shared state among multiple agents by using locking mechanisms in the data store blocks. When agents try to update the shared context document, these blocks enforce locks or version checks to prevent collisions. This lets multiple AIs read, update, then safely write back the state without overriding each other’s changes. Setting this up properly is key for coordinating Autonomous AI Teams. More details and examples at latenode.com.
In my setups, I use data store blocks configured with versioning or optimistic locking. Agents read the current version of the state, do their updates, then write back with a version check. If the write fails due to a conflict, the agent retries with the latest state. This pattern reduces write collisions and keeps shared state consistent across parallel runs. Latenode’s blocking and locking are behind the scenes but very effective.
Some folks implement explicit queueing between agents or use atomic operations via code to merge changes safely. But usually, Latenode’s native locking is enough for most team scenarios. The key is to keep your shared context as a single document or object and rely on version checks so parallel agents don’t stomp each other accidentally.
Multiple agents updating shared state simultaneously can get tricky. I’ve found Latenode’s data store block locking very helpful—it prevents race conditions by locking state during writes and enforcing version checks. Sometimes, if you expect frequent parallel updates, designing your state objects to minimize overlap (e.g., partitioning state per agent) helps. Otherwise, fallback retries after lock failures keep state consistent. It’s a bit of trial and error but the native blocks handle most concurrency safely.
The safest method involves leveraging Latenode’s built-in data store blocks with locking and versioning features. These blocks ensure that when one agent updates the shared state, others must wait or retry if a conflict occurs. This design avoids overwriting concurrent updates. For advanced use, partitioning shared state or implementing careful update merging in code can be employed, but the native locking usually suffices for typical multi-agent coordination.
use data store with locking/versioning. retries help avoid conflicts between agents.
native locking blocks in latenode keep shared state safe during parallel agent updates.
datastore locking helps multiple agents coordinate state safely.