DynamoDB Streams keep change logs for 24 hours, while CockroachDB defaults to SERIALIZABLE isolation. Pick the right store for agents. Read now.
What agent swarms actually demand from storage
Agent swarms do not behave like a single web request. Many workers read shared state, write partial results, retry after timeouts, and fan out work in parallel. That pattern stresses two properties at once: how changes propagate between agents, and how safely concurrent writes can coexist. A store that is excellent for simple key lookups can still fail you when agents need durable coordination; a store built for strong consistency can feel heavy when you only need fire-and-forget event delivery.
Treat the choice as a product decision, not a fashion contest. Map each swarm path to the failure mode you cannot tolerate—missed updates, conflicting writes, or stale reads—and pick the model that makes that failure hard by default.
NoSQL for fan-out, streams, and loose coupling
NoSQL systems shine when agents need flexible documents, predictable single-item access, and change-driven side effects. DynamoDB Streams keep change logs for 24 hours, which is long enough to wire workers that react to inserts and updates without building a separate event bus for every table. Within that window you can replay recent mutations, hydrate a new consumer, or catch up after a brief outage. Past that window, you must assume the stream is gone and design another source of truth for recovery.
That model fits swarms that treat the database as a shared mailbox: agents claim work items, stamp status fields, and let downstream agents react to stream events. Keep item sizes and access patterns simple. Avoid multi-item business invariants that the database will not enforce for you—if two agents must agree that a budget never goes negative, stream-driven NoSQL alone will not save you.
NewSQL when isolation is the product
NewSQL systems aim for SQL semantics and horizontal scale. CockroachDB defaults to SERIALIZABLE isolation, which means concurrent transactions that would produce a race are rejected or retried rather than silently accepted. For agent swarms that allocate scarce resources, advance multi-step plans, or maintain a global counter of work, that default is often the feature you are buying.
Use NewSQL when an agent’s write depends on a consistent view of several rows—or when “last writer wins” would corrupt the plan. Expect more contention under hot keys: serializable safety surfaces as retries, so design agents to be idempotent and to back off cleanly. You trade stream-native simplicity for stronger guarantees on every commit path.
A practical pick list for agent workloads
- Prefer NoSQL plus streams when agents are loosely coupled, state is mostly per-key, and consumers can live with a bounded change log such as DynamoDB Streams’ 24-hour retention.
- Prefer NewSQL when multi-agent writes must stay free of write skew and you want SERIALIZABLE-style defaults like CockroachDB’s without bolting on application-level locks.
- Split the swarm: use NoSQL for high-volume task payloads and event fan-out; use NewSQL for the thin coordination layer that owns locks, budgets, and plan checkpoints.
- Always plan beyond the log window—if recovery needs more than 24 hours of history, keep an audit trail or snapshot strategy outside the stream.
Start from the coordination problem, not the label. NoSQL and NewSQL both can serve agent swarms; the right store is the one whose defaults match how your agents fail and how they recover.