At 50ms average latency, a data query becomes the bottleneck in an agent's reasoning loop, not the model. Each read must return before the agent can incorporate it into its next step. Repeat that cycle hundreds of times per second across concurrent sessions, and the delay compounds quickly.
Many early backend choices were built for interactive applications, where a human can wait 200ms without noticing. Agents cannot. Their planning loop targets 10ms, so slower reads create queue depth before the system appears slow to a person.
What real-time means for agent data
For agents, real-time means the round trip from issuing a read to using its result in the next decision is short enough that data never limits the loop. In most agent architectures we have seen, the p99 ceiling is 5 to 15 milliseconds, depending on access-pattern complexity.
The term means something else in analytics, where it can mean sub-second dashboards, trading, where it can mean microsecond order routing, and streaming media, where it describes continuous low-jitter delivery. Mixing these definitions leads to poor tool choices. Agent context reads need consistent single-digit millisecond responses for point lookups and small range scans, plus clean concurrency isolation when 30 agents read and write shared state at once. They do not need a columnar analytics store or microsecond wire latency.
The two query patterns that matter most
Agents produce two distinct read patterns. Backends that perform well on one often struggle with the other.
Context retrieval. Before each reasoning step, an agent fetches its working context: known information, completed actions, and applicable constraints. Usually this is a point lookup or small fetch by agent ID and session ID. Because the data may have been written seconds ago, the read must see it immediately after commit. This stresses write-read consistency more than throughput. Async replication or a read replica lag of 100ms can silently return stale context, causing reasoning from a false premise.
Shared state reads. Collaborating agents need prompt visibility into writes from peers. A coordinator that dispatches sub-agents and then reads their progress reports cannot afford a round trip that expands planning latency. This pattern puts more pressure on isolation and visibility guarantees than context retrieval does.
Most general-purpose databases assume a human submits a write and the system has hundreds of milliseconds to propagate it. Agent workloads reduce that window to nearly zero.
Where conventional backends fall short
Before building LaserData, we ran an early version of our agent framework on a standard hosted Postgres instance. The setup used transaction-mode PgBouncer pooling, indexes for queried columns, and a read replica for heavier lookups. At low concurrency, it worked well.
The first problem appeared at 40 concurrent agent sessions. Transaction-mode PgBouncer releases connections while queries are idle, as designed, but burst reconnections added 8 to 12ms of jitter at p95 and p99. Then write visibility failed. One session wrote context while a sibling read through the replica, whose replication lag was typically 50ms. We had to send reads to primary or accept stale context. Under load, primary routing removed much of the latency benefit the replica was meant to provide.
We are not saying Postgres is a bad database. We are saying its design targets a different access pattern, so using it for agent context means repeatedly working around those choices.
What the query execution path needs to look like
Every stage matters for agent queries. A query can spend 2ms in storage, 8ms waiting in a connection queue, and 5ms compiling a plan, producing 15ms of wall-clock latency. Faster storage alone does not improve the end-to-end result.
The important stages, in order:
Connection management. Agent sessions persist but issue reads in bursts. Reconnection overhead and queue depth make a connection model unsuitable for this workload. LaserData keeps persistent multiplexed connections per session, removing per-query connection setup.
Query planning. Most agent queries are simple: primary-key fetches, session and timestamp range fetches, and field updates. Running a full optimizer pass for each is unnecessary. LaserData compiles common agent access patterns into pre-planned execution paths during schema registration, bypassing the optimizer at query time for the common case.
Read path. Context reads need a write-ahead log consistent snapshot, not an asynchronous replica. The storage layer must provide low-overhead consistent reads without funneling every request through one bottleneck.
Result serialization. JSON serialization at the database and deserialization in the SDK adds cost across hundreds of queries per second. LaserData uses a compact binary wire format between server and SDK. JSON remains available for debugging, but not on the hot path.
Concrete tradeoffs we made building this
We are not saying a general-purpose database cannot serve agents with careful configuration. We are saying each optimization that improves agent behavior can make another workload worse. Teams gradually approach a purpose-built system, one tuning change at a time, while taking on operational complexity that the right tool would avoid.
LaserData's tradeoffs are deliberate. We do not support arbitrary SQL. The interface is limited to agent access patterns: point lookups, bounded session range scans, and state-field set operations. Bounded, predictable execution paths let us make stronger latency guarantees. Workloads requiring ad-hoc analytical queries across the full dataset should use another tool.
LaserData uses strong consistency by default for reads within a session, with optional relaxed consistency for cross-session reads when the caller explicitly accepts the tradeoff. This is more conservative than eventual consistency systems and cannot match their raw throughput at very high concurrency. For agents, stale context can produce incorrect actions, making this the appropriate tradeoff.
What to measure before choosing a backend
Run your actual access patterns under load before choosing a backend. Measure p99, not only p50. Agents operate against deadlines: p50 describes the happy path, while p99 shows how often reads exceed the planning loop budget.
Measure write-read latency from commit until a sibling session observes the write. Test under concurrency, not only with one agent. A backend's concurrency behavior can differ substantially from its single-connection performance.
The teams we have worked with that struggled most were not operating at large scale. They were running 20 to 30 agents in parallel during development and encountering contention that looked like a capacity issue but was actually an isolation model mismatch. More scale does not correct that mismatch. It worsens it.
Where this is going
Agent architectures are converging on planning loops with fast point reads, event-driven state updates with low-latency subscriber delivery, and memory systems that retrieve relevant context without a full vector scan at every reasoning step. A data backend should support all three without making the application operate a separate system for each.
LaserData serves these access patterns through one API. The query path described here is live in early access. If your current backend is creating latency problems, run your benchmark workload against a purpose-built layer directly. The free tier requires no credit card, and the quickstart reaches a running query in under five minutes.