Solutions / Agentic Data Streaming

Your agents act on the business as it is right now.

An agent working from last night's batch does not just report late. It acts wrong: it confirms the order that was cancelled, escalates the ticket that was resolved, quotes the stock you already sold.

As changes happen, agentic data streaming delivers them from your operational systems to the places your agents read from.

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Benchmark

Built for low-latency, high-throughput use cases

See the engine and its benchmarks →

Source

Your operational systems

Database log

log-based CDC — tables untouched

Event streams

picked up as-is

PostgreSQL MySQL SQL Server Oracle MongoDB SAP HANA Apache Kafka Azure Event Hubs G Google Pub/Sub + 300 more

as changes happen

Dataddo

Speed Security Governance

every agent sees only what it is allowed to see

Streaming pipeline

+ INSERT ~ UPDATE − DELETE

every commit → one ordered event

Zero Copy Connectors

SmartCache holds live data query-ready — queried at the source, nothing stored

streaming pipeline → storage

DWH / Lake / ODS

always-current mirror

Snowflake Databricks Google BigQuery Amazon Redshift SQL Server + Others

Event streams

event-driven agents

Apache Kafka Azure Event Hubs G Google Pub/Sub + Others

read & act

+ served direct, no storage layer

Your agents

Acting on the business as it is right now

Claude OpenAI Gemini Mistral Azure AI Foundry Bedrock agents Databricks agents Agentforce C Custom LLM apps I In-product copilots + Others
Any source engine → any AI destination
From the systems of record

The facts your agents act on live in operational systems. Stream them from there.

Orders, tickets, stock levels, permissions - the state an agent acts on is born in operational databases, not in the warehouse. Dataddo captures committed changes natively from every major engine and keeps delivering them while the agent works, without adding load to the system that runs the business.

PostgreSQL PostgreSQL MySQL MySQL MSSQL MSSQL Oracle Oracle MongoDB MongoDB SAP HANA SAP HANA AWS Aurora (PostgreSQL) AWS Aurora (PostgreSQL) AWS RDS (PostgreSQL) AWS RDS (PostgreSQL) Google Cloud SQL (PostgreSQL) Google Cloud SQL (PostgreSQL) AlloyDB AlloyDB CockroachDB CockroachDB
See all 400+ connectors →
Performance

The state changes, and your agent already knows.

What that means for an agent: the change is in the mirror before the agent's next query, and the event fires while acting on it still matters. A cancellation lands mid-conversation, and the support agent stops the refund it was about to promise. A high-value order arrives, and the review agent picks it up while the order is still being processed - not in tomorrow's batch, when the goods have shipped.

Metric Result
p50 latency, committed at source → visible to the agent 351 ms
p90 latency, committed at source → visible to the agent 568 ms
change events sustained per second 35,000/s

SQL Server CDC, internal benchmark of the underlying engine

The solution

Always-current state for your agents.

Dataddo reads committed changes straight from your database's transaction log and delivers them where your agents read. That is the whole architecture: no broker to operate, no stream processors to write, no team to hire for it. See how the pattern is built.

Always current, never a batch behind

Changes are captured the moment they commit, so what the agent reads tracks production continuously. There is no polling interval to tune and no batch to wait for. The state behind the agent's next answer is the state of the business right now.

Deleted records are delivered, not missed

Query-based sync cannot see a row that has been removed - it simply stops appearing. Log-based capture delivers the delete as an event like any other change, so the cancelled order or the revoked permission - often precisely the fact the agent needed - always reaches it.

No extra load on your production database

An agent may read hundreds of times an hour. All of it hits the mirror, while capture itself reads only the transaction log: no polling queries, no triggers, no extra indexes. The database that runs your business keeps serving customers at full speed, which is why the owner of a core ordering system or SAP will actually sign off on this.

Self-healing, no committed change is lost

A silent stall is the worst failure mode for an agent: it keeps acting, confidently, on aging state. The built-in CDC Supervisor health-checks every replication process and restarts it from the tracked log position, picking up exactly where it stopped. Recovery needs no one on call.

How agents consume it

Three paths from Dataddo to your agent.

The difference is where the agent reads. It can query a store you already run, be pushed events the moment state changes, or ask Dataddo directly - and most real setups combine paths rather than pick one: an event tells the agent that something happened, and the agent then queries the mirror, or asks Dataddo directly, for the context around it.

1 Agent pulls

Warehouse, lake, or operational data store

Dataddo keeps an always-current mirror of your operational data in the store your agent already queries - a warehouse, a lake, or an operational database. The agent asks “what is X now” in SQL and gets current state plus as much history as you keep.

BigQuery Snowflake Databricks + operational DBs
2 Agent is pushed

Event streams

Every committed insert, update, and delete is published as an ordered event to your event backbone. The agent, or its orchestration layer, reacts the moment state changes instead of polling for it.

Kafka Azure Event Hub Google Pub/Sub
3 Agent pulls Zero infrastructure

Direct from Dataddo, via SmartCache

SmartCache, Dataddo's built-in storage, holds the latest synced data for direct retrieval - a de-facto operational data store you don't have to run. Agents connect straight to Dataddo, with no warehouse to stand up and no broker to operate.

MCP REST API Apache Arrow
Aspect Warehouse / lake / ODSEvent streamsDirect via SmartCache
Agent behavior The agent pulls: it queries current state when it needs itThe agent is pushed: it reacts when state changesThe agent pulls: it asks Dataddo directly, with nothing in between
Freshness Continuously current - changes land as they commit at the sourceEach change is delivered as it happensLatest completed sync
Data shape Materialized relational tables, queried with SQL - delivered with the connector’s own metadata: what each dataset and field means, and which fields are sensitiveAn ordered stream of insert, update, and delete events, each carrying its metadata - operation type and the source engine’s sequence identifier; see ordered change eventsLightweight structured objects served over MCP, REST, or Apache Arrow, metadata included - ready to consume, no reshaping step of your own in between
History Full time series - as much as you choose to keepWhatever your broker's retention window holdsThe latest state, not a long archive
Infrastructure you operate Your warehouse, lake, or ODSYour event backbone and its consumersNone - SmartCache is hosted by Dataddo
Typical use Support chatbots and assistants answering “what is X now”, analysis that also needs historyEvent-driven automation: a high-value order triggers a review agent, a cancellation triggers retentionAgents that need governed data without standing up a data platform first
Governance

Agents act on what you deliver. Govern it before it arrives.

Governance is stricter here than for analytics, because whatever reaches the agent can end up in an action or a customer-facing answer.

PII never reaches the agent

Sensitive columns are excluded or hashed at the source, before delivery, so they never land in the mirror or the event stream the agent reads. Hashed columns stay joinable without exposing the original values. See PII exclusion and hashing.

Bad data gets stopped, not forwarded

The Data Quality Firewall validates records before they reach the destination and can block or alert on anomalies, which is the signal to pause automated actions before an upstream fault propagates into agent behavior.

Commit order is preserved and verifiable

Changes arrive in the order they were committed, and each event carries the metadata to prove it downstream. An agent reconstructing state never sees an update applied before the insert it depends on.

The agent reads a replica, not production

Give the agent read-only credentials scoped to the replicated tables. Its access, its query volume and its blast radius are all bounded by the mirror rather than by your operational database.

Try agentic data streaming with your stack.

A scoped, time-boxed POC with your database and your agent. Bring the awkward one, it is more useful that way.

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