Public articles linked to the same research event.
arXiv The authors reframe multi-agent system coordination as a data management problem and propose describing agents by their state footprint: the state they read and write across their own local context and state as well as the state of the orchestrator and external systems; they note that current orchestrators do not track this read/write state, so concurrency anomalies manifest even in simple coding tasks, and argue that MASs require database-like guarantees, yet unlike database transactions agents do not read from a fixed schema or isolated snapshot and cannot be replayed deterministically upon failure, so they can resolve conflicts semantically instead of aborting, outlining a vision for next-generation agent orchestrators and transactional interfaces for external systems.
The authors reframe multi-agent system coordination as a data management problem and propose describing agents by their state footprint: the state they read and write across their own local context and state as well as the state of the orchestrator and external systems; they note that current orchestrators do not track this read/write state, so concurrency anomalies manifest even in simple coding tasks, and argue that MASs require database-like guarantees, yet unlike database transactions agents do not read from a fixed schema or isolated snapshot and cannot be replayed deterministically upon failure, so they can resolve conflicts semantically instead of aborting, outlining a vision for next-generation agent orchestrators and transactional interfaces for external systems.
The authors reframe multi-agent system coordination as a data management problem and propose describing agents by their state footprint: the state they read and write across their own local context and state as well as the state of the orchestrator and external systems; they note that current orchestrators do not track this read/write state, so concurrency anomalies manifest even in simple coding tasks, and argue that MASs require database-like guarantees, yet unlike database transactions agents do not read from a fixed schema or isolated snapshot and cannot be replayed deterministically upon failure, so they can resolve conflicts semantically instead of aborting, outlining a vision for next-generation agent orchestrators and transactional interfaces for external systems.
The authors reframe multi-agent system coordination as a data management problem and propose describing agents by their state footprint: the state they read and write across their own local context and state as well as the state of the orchestrator and external systems; they note that current orchestrators do not track this read/write state, so concurrency anomalies manifest even in simple coding tasks, and argue that MASs require database-like guarantees, yet unlike database transactions agents do not read from a fixed schema or isolated snapshot and cannot be replayed deterministically upon failure, so they can resolve conflicts semantically instead of aborting, outlining a vision for next-generation agent orchestrators and transactional interfaces for external systems.