brillio
New member
Agentic Data and Application Management is an architectural approach to coordinating autonomous AI agents, application workflows, and shared data in high-concurrency environments. It aims to manage distributed agent state and synchronize relational data without allowing long-running reasoning cycles to block core database operations.
Unlike traditional microservices, LLM agents may spend seconds reasoning before completing tool calls. When multiple agents read and write shared operational tables, database deadlocks and concurrency race conditions can emerge as agents compete for locks and work with outdated records.
I’m interested in how Brillio ADAM addresses this challenge through decoupled agent scratchpads, event sourcing, optimistic concurrency, and a self-healing data fabric.
The architectural questions I’m considering are:
For production systems, I would want to evaluate transaction boundaries, event replay behavior, schema versioning, and recovery guarantees rather than assume the architecture eliminates deadlocks by itself.
For teams running multi-agent workloads against shared relational databases, which concurrency patterns have worked best in practice: optimistic concurrency with event sourcing, isolated agent state with controlled commits, or another approach?
Unlike traditional microservices, LLM agents may spend seconds reasoning before completing tool calls. When multiple agents read and write shared operational tables, database deadlocks and concurrency race conditions can emerge as agents compete for locks and work with outdated records.
I’m interested in how Brillio ADAM addresses this challenge through decoupled agent scratchpads, event sourcing, optimistic concurrency, and a self-healing data fabric.
The architectural questions I’m considering are:
- State isolation: Can decoupled agent scratchpads keep intermediate reasoning and workflow state separate from shared transactional tables?
- Conflict resolution: Can optimistic concurrency detect stale writes and retry conflicting operations without holding locks throughout an agent’s reasoning cycle?
- Event consistency: How should event sourcing handle duplicate events, out-of-order updates, and retries across distributed agents?
- Data harmonization: How can a self-healing data fabric synchronize relational data in real time while preventing schema desynchronization between agents and underlying databases?
- Recovery: What happens when two agents independently update related entities and both workflows appear valid at the application layer?
For production systems, I would want to evaluate transaction boundaries, event replay behavior, schema versioning, and recovery guarantees rather than assume the architecture eliminates deadlocks by itself.
For teams running multi-agent workloads against shared relational databases, which concurrency patterns have worked best in practice: optimistic concurrency with event sourcing, isolated agent state with controlled commits, or another approach?