Built for teams shipping advanced agents.
Early-access engineering teams describe what changed after replacing five stitched data systems with one backend.
Early-access teams
Our conversational agent once read session context from Redis, stored long-term memory in Postgres, and sent events through a separate Kafka deployment. Managing those systems took more sprint time than the agent logic itself. LaserData replaced all three with one API call. We reduced our infra configuration surface by about 70%, and p95 read latency fell from 18ms to under 6ms.
We operate a multi-agent logistics pipeline in which agents share shipment locations, route choices, and constraint violations. Keeping that state consistent was our toughest engineering problem. LaserData's namespace model and compiled query execution removed the contention we had battled for months. We now support 80 concurrent agent sessions with zero state-read conflicts.
Our fintech compliance agents read transaction context, query historical records for patterns, and stream state changes to downstream audit systems inside a real-time decision window. We evaluated three specialized databases before LaserData. It is the only one that supports all three access patterns natively without a synchronization layer from us. Write-to-subscriber streaming is genuinely sub-millisecond.
Reliability stood out most. We tested LaserData against our production traffic profile: 4,000 agent reads per second with bursty writes, while p99 latency remained under 9ms. A previous backend reached 200ms spikes during bursts because its vector index and relational store ran as separate processes connected by a network hop. LaserData keeps them co-located, and production metrics show the difference immediately.
LaserData agent architectures
Three broad patterns customers run in production.
Conversational agents with memory
Long-running conversation agents retain episodic memory across sessions, including summaries, user preferences, and earlier decisions as named context objects. Each turn reads the memory namespace, adds new context, and can run a semantic similarity query over stored embeddings in one round-trip.
LaserData includes the document store and vector index in the same backend, so agents need no separate embedding search service for reads. New writes are queryable immediately, removing the index-sync delay that produces stale memory reads elsewhere.
Shared-state orchestration for multi-step tasks
Parallel orchestration systems need a shared store that is consistent, fast, and subscribable. Each specialized agent writes its intermediate result to a namespace keyed by task ID. The orchestrator subscribes there and starts the next step as soon as the required output arrives.
LaserData's streaming layer replaces a separate event bus. Sub-agents write data, and the orchestrator receives real-time notification through the same client. Four infrastructure components become one, with predictable latency under concurrent load.
Real-time sensor-driven agents
Agents processing sensor streams, financial ticks, or log pipelines must read their state, compare it with historical patterns, and decide within the incoming data's latency budget. A backend adding more than a few milliseconds to reads breaks that timing.
LaserData's compiled query execution targets this access pattern. A query that reads current agent state and scans the last 100 events in the same namespace uses one-pass execution instead of two database calls. Sub-10ms p99 remains consistent when both reads occur in one decision loop.
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