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.
Managed end to end, from the database to the agent.
Agents do not just read your data. They act on it.
Which is why the batch cadence that was good enough for dashboards is not good enough here. Three things break when an agent is the consumer.
Old data makes an agent act wrong
A dashboard refreshed hourly is merely an hour behind, and a human reading it knows that. An agent working from hour-old state has no such instinct. It takes the action the data implies, and the action is wrong.
Agents read unpredictably, and often
A human analyst runs a query when they need one. An agent serving customers or driving a process may query state hundreds of times an hour, at any hour. Point that workload at production and it competes with the business the database exists to run.
A deleted row is often the most important fact
A record that disappears between two batch extractions is invisible to query-based syncs. For an agent, the cancelled order or the revoked permission is precisely the thing it needed to know.
The usual fix is a streaming platform: brokers to run, state and backpressure to reason about, specialist skills to hire. That is why most teams do nothing at all. It does not have to work that way.
You do not need a streaming platform to do this.
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.
Managed connectors, not a build project
Sources that do not stream natively get bridged for you, rather than becoming a reason to buy and run yet another tool. The same platform also covers scheduled batch, so one team and one contract cover the whole range. See how the pattern is built.
Your production system stays untouched
Reading the transaction log asks practically nothing of the source: no polling queries competing with application traffic, no triggers, no extra indexes. This matters more than raw speed. If the source is SAP or a core ordering system, its owner will refuse real-time interference outright, and they are right to. The requirement is streaming to the agent with as little database footprint as possible.
It keeps itself running
An agent in production cannot tolerate a stream that quietly stops. The built-in CDC Supervisor health-checks each replication process and restarts it from the tracked log position, so no committed change is lost and recovery needs no one on call.
Two patterns. Most real setups use both.
The difference is whether the agent asks what the state is, or gets told that it changed.
| Aspect | Live mirror | Change events |
|---|---|---|
| Agent behavior | The agent pulls: it queries current state when it needs it | The agent is pushed: it reacts when state changes |
| Data shape | Materialized tables, always current | An ordered stream of insert, update and delete events |
| Destination | A warehouse or operational database the agent can query | An event system the agent or its orchestration layer consumes |
| Typical use | Support chatbots and assistants answering “what is X now” | Event-driven automation: a high-value order triggers a review agent, a cancellation triggers retention |
They combine naturally. An event tells the agent that something happened, and the agent then queries the mirror for the context around it.
Data your agents can consume, not data you have to reshape first.
An agent does not want a topic to subscribe to and a schema registry to negotiate. It wants an object it can read.
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Structured, ordered change events
Every change carries its operation type and the source engine’s sequence identifier, so a consumer can verify or re-establish commit order without extra machinery. See ordered change events.
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Fields that explain themselves
Dataddo delivers the connector’s own metadata alongside the data: what each dataset and field means, and which fields are sensitive. Retrieval and text-to-SQL agents get semantic grounding instead of guessing from column names.
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Ready to use as context
The delivered object is a lightweight structured document an agentic system can consume immediately, with no reshaping step of your own in between.
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.
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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.
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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.
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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.
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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.
Not every agent needs milliseconds. Start with what the decision actually requires.
Latency is a business question before it is a technical one. Fraud prevention has to intercept at the moment of creation. Fraud detection is a pattern-analysis job that can run much later. Same domain, two orders of magnitude apart. Buying millisecond infrastructure for a decision that needs minutes is how real-time projects get expensive and stay narrow.
Dataddo runs the whole range on one platform: sub-second change capture where the decision needs it, scheduled batch where it does not, without a second tool or a second team to justify.
60%
Gartner predicts that adoption of data streaming for agentic AI will exceed 60% by 2028, up from under 15% in 2025.
Which is the argument for scoping it deliberately now, while you still get to choose where it matters.
The wider picture
Agentic data streaming covers agents acting on current operational state. For the full delivery layer behind AI, including RAG corpora, batch sources and direct retrieval over MCP, see the AI Data Foundation.
AI Data Foundation →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.