Why Your Data Pipelines Aren’t Ready for Agentic AI

According to a Forrester Consulting study commissioned by Boomi, 86% of organizations have moved past the pilot stage with AI agents, yet only 34% trust the actions those agents take.

“Without trust in the data, outputs and decisions of AI models and agents, there is no value from AI,” says Rita Sallam, distinguished VP analyst and chief of research at Gartner.

An agent can work only with the data and tools it’s given. Even if that data is out of date, incomplete, or cut off from the systems the business runs on, the agent makes decisions from it anyway.

To act reliably, an agent needs data that is recent enough for the decision in front of it, drawn from every system involved, and defined in terms everyone has agreed on. In most enterprises, two teams share that job: a data team, which builds ETL data pipelines for analytics, and an integration team, which connects applications for transactions. Neither team supplies all three on its own.

So what has to change before you can trust an agent to act on what your pipelines deliver?

What an ETL Data Pipeline Does

Most enterprise pipelines do the job they were built for: they deliver data on a schedule to reports that people read later. When an AI agent reads the same data, the trouble comes from that schedule and the reporting architecture around it, not from ETL (extract, transform, and load) itself. Understanding why that happens requires separating two design choices that are often treated as one: when a pipeline runs and the order of its steps.

What is a data pipeline?

A data pipeline is any automated workflow that collects data from source systems, processes it if needed, and delivers it to a destination. That destination can be many things, such as: a warehouse or data lake, an application, or a machine learning model. Processing can mean cleaning, filtering, and reshaping records to fit the target. Some pipelines don’t process the data at all, simply copying data from a source to a destination.

Batch vs. streaming pipelines

When choosing what pipeline to build, it’s important to decide when it runs, and how frequently:

• Batch pipelines: run on a schedule and process a fixed group of records each time, so you can validate the results before anyone uses them. They suit recurring work, such as end-of-day financial reconciliation, payroll, monthly billing, and loads into the analytics warehouse.
• Streaming pipelines: process each record, or a micro-batch, as it arrives, so the data is available seconds after an event instead of hours later. Fraud detection, stock-level updates, and anomaly alerts from Internet of Things (IoT) sensors all need that speed, because any delay is a risk.

While streaming pipelines keep your data the most up-to-date, they can also incur higher costs: more batches means a higher infrastructure and data transfer workload, and more data moved can mean higher storage costs.

With an ETL pipeline, there are compute costs involved with the data access and transformation. This is why properly timing batches and optimizing transformation are essential to balance keeping data up-to-date while controlling costs.

What is an ETL data pipeline?

An ETL data pipeline runs three steps: extract, transform, and load. The destination receives a cleaned and reformatted copy of the data rather than the raw records. Here’s a closer look at each step:

• Extract: pulls data from sources such as relational databases, business applications, flat files, and APIs.
• Transform: removes errors and duplicates, maps fields so they mean the same thing across sources, and converts formats to match the target schema.
• Load: writes the result to a data warehouse, data lake, or other store.

Because the transform step comes ahead of the load, it catches errors and format problems before anything reaches the warehouse. That makes ETL a good fit for work where accuracy and consistent formats matter most, such as financial reporting and regulatory analytics.

People often associate ETL data pipelines with scheduled batch processing, but the same three steps can also run on streaming or event-driven data.

What is ELT?

ELT (extract, load, and transform) swaps the last two steps. The pipeline loads raw records into the warehouse or lake first, and tools inside that destination then transform them into reporting tables, views, and other structures, while the original records stay available alongside them. ELT pipelines often handle large volumes well, because a cloud warehouse scales more easily than a separate transformation server. Having the untransformed data on hand also helps exploratory analysis, when you don’t yet know every question you’ll want to ask. Like ETL, ELT can run on a schedule or as a stream.

4 Reasons to Modernize Your Enterprise Data Pipelines for Agentic AI

Eight in ten companies say data limitations make it harder to scale agentic AI. Many of those limitations start upstream, in jobs that were designed to feed scheduled reports rather than to give agents current data from the systems they need. Four issues in particular trace back to how these pipelines are built:

• Stale data: a scheduled load is only as fresh as its last run. A human looking at a figure usually knows roughly how old it is and checks the source system when they need to clarify something. An agent treats the latest figures as the present state of the business, unless its instructions give the age of each value and say how recent it needs to be for the task. An overnight load is fine for drafting a monthly forecast, but promising a customer a delivery date needs the stock count as it stands in the inventory system now.
• Limited lineage: every transformation, join, and copy between systems moves a value further from where it started, and unless the pipeline logs each step, the value’s history is lost. That applies to any multi-step pipeline, not only ETL or ELT. Lineage built for reporting traces a warehouse table back to its sources, but an agent combines values from several systems in a single decision. When someone needs to explain that decision to a customer, reverse it, or show an auditor what the agent relied on, there is often no complete record of the data and logic behind it.
• Schema drift: when a source system renames a column, changes a data type, or adds a field, the pipeline keeps mapping records to its original schema. If it doesn’t detect these changes, the job may stop with an error on its next run, or finish normally while leaving the new field out of what it loads. A person might spot an odd-looking value in a report, but an agent can work through large numbers of records and act on them before anyone notices that the data is wrong.
• Siloed architecture: pipelines that were built one project at a time deliver only the fields and sources each project required, so related records, documents, and operational context are hard to find or reuse. For example, an agent handling a disputed invoice through a pipeline built for financial reporting sees only the billing and payment fields loaded into the warehouse. It might need to access the records in the customer relationship management (CRM) system, the support tickets on the account, and the contract terms, but the pipeline doesn’t deliver them. In Deloitte’s “Tech Trends 2026,” 48% of organizations named data searchability, and 47% data reusability, among the obstacles to their AI automation strategies.

How to Make Enterprise Data Pipelines Agent-Ready

Fixing these problems doesn’t mean replacing the ETL data pipelines you already run. They can keep feeding reports while you change how they deliver data to agents.

How do you modernize your ETL data pipelines?

Before deploying agents at scale, check that their data meets each workflow’s requirements for freshness, lineage, and quality. Three changes to your existing ETL data pipelines satisfy those demands:

• Map workflows to understand real-time data needs: for each one an agent will run, decide how old its data can be before a decision goes wrong. Where a few hours’ delay is acceptable, a more frequent scheduled load is often enough. Where it isn’t, run streaming and event-driven movement alongside your existing loads, using change data capture (CDC) to pick up each insert, update, and delete in the source as it happens.

CDC watches the source and moves only new or modified records, rather than re-extracting whole tables on every run. That keeps transfer costs down and stops the same rows from being copied over and over. Log-based CDC reads those changes from the database’s transaction log instead of querying its tables, so it adds no query load in production. A captured change can also start a process directly. For example, a posted payment can halt collections on the customer’s account as soon as it clears, and an updated order status can trigger the next step in fulfillment.

• Maintain end-to-end lineage: capture each value’s source and transformation history, and store it with the data an agent reads at runtime. Log each agent query with its timestamp, the data returned, the tools the agent called, and the lineage behind that data, so you can trace each decision back to the records that informed it.
• Validate business data: build quality rules and anomaly detection into the pipeline instead of relying on periodic cleanups. When a rule flags an anomaly, such as a column that suddenly arrives empty, or a schema check catches a renamed field, the data team can quarantine the affected records, then repair the pipeline and the schema before people or agents act on incomplete data.

Agent Integration Beyond the Data Warehouse

Not every record has to reach the warehouse before an agent can use it. Centralize shared data that needs validation and cleansing. Leave the rest where it is, in the operational applications, and give agents governed access to it through APIs and, where appropriate, Model Context Protocol (MCP) servers. An agent handling a disputed invoice can then look up the support tickets and contract terms where they’re kept, even though the reporting pipeline never loaded them.

Where several applications hold their own copy of the same customer or product, give agents one agreed version. Master data management (MDM) builds a golden record for each entity from those copies, using matching and survivorship rules to decide which value wins when they conflict. MDM then synchronizes the corrected values back to the source applications, so the CRM, the enterprise resource planning (ERP) system, and the agent all work from the same data. Among the companies PwC ranks as AI leaders, 59% say they maintain a single trusted record of their critical data that’s accessible across the business.

How to Align Data Engineering and Integration Teams on One Data Operating Model

Modernizing your enterprise data pipelines involves systems that most organizations split between two teams, and agents rely on the work of both.

Data teams build and run the warehouses and ETL platforms. They integrate and transform data from many sources, model it for reporting and analysis, and check that the numbers are right and the accounts reconcile.

Integration teams look after the hubs that connect applications, along with API management and process automation. They build the API connections and orchestrate the workflows behind business processes, making sure each one finishes on time and the systems those workflows depend on stay up.

Because of this split, people have long worked around the problems caused by data scattered across systems. A customer service representative, for example, switches between phone, email, chat, and CRM records to piece together a customer’s history, and relies on experience to fill in the missing context. While an agent can gather the same records, it has none of that human judgment to help it, so someone has to supply that context explicitly. And because an agent draws on the warehouse and several applications for a single decision, an error in either team’s data goes straight into its next action.

Who should own enterprise data pipelines?

Those errors are hard to prevent while each team looks after only its own systems. But under a single data operating model, the two share objectives and are jointly accountable for the data that agents use. And the teams don’t even have to merge to work this way. For each agent, they agree on which data it needs, how current that data must be, who may see it, and what its key business terms mean. Each team then decides how to deliver its part using the platforms and expertise it already has.

The integration team moves changes from applications as they happen, and the data team keeps the reconciled history accurate. Together, they’re responsible for whether the data behind an agent’s decisions is current, correct, and traceable, whichever system it came from.

A governed business glossary, such as Boomi Meta Hub, gives both teams the same definitions. Subject matter experts endorse each term once and link it to the data objects and agents that use it, so “active customer” means the same thing to your agents, your integration developers, and the people closing the books.

Because existing ETL pipelines have defined owners, controls, and access rules, the alignment task is to extend the same controls to the streaming and API routes that agents use. The operating model clarifies who is responsible for maintaining this consistency, while the systems enforce the established policies.

An alignment checklist for an agent-ready data model

In Forrester’s Agentic Readiness Gap study, 48% of the companies best prepared for agents had aligned their AI and integration teams under one operating model, against 37% of the least prepared. Putting that model into practice comes down to establishing agreements between the two teams:

• Data Delivery Standards: Teams designate who should act when data pipeline and agent access issues arise. This team ensures agents are provided proper data access, and that pipelines are delivering to destinations as expected.
• Issue Response Lanes: When a bug is identified, there is clarity into how the fix will be assigned and worked. Without a defined workflow, issues can create duplicate work and take longer to resolve.
• Change Notification: Require application teams to warn both the integration and data teams before changing a field that a pipeline or agent reads. For example, if a ‘service address’ field changes to optional in the CRM, without notice, a scheduling agent could dispatch engineers to work orders with no address.
• Centralized Permissions Management: Use an MCP gateway so that agents have a single, secure source of permissions when accessing data and applications. Gartner expects 40% of enterprises to scale back or shut down autonomous agents by 2027 over governance failures discovered only after a production incident.
• Define Success: Establish a shared rubric for what pipelines and agents should achieve, such as how fast an agent gets the data it needs and how reliably it completes a workflow. Keep tracking the uptime and query performance of each team’s platforms as well.

Each of these agreements is easier to keep when both teams share a platform rather than running separate toolchains for scheduled loads, streaming, APIs, and data quality.

Unify Integration and Data Management for Agent-Ready Pipelines With Boomi

Agent-ready enterprise data pipelines give agents information that is fresh, traceable, validated, and reachable across systems, under one data model. Boomi delivers all of this on a unified platform.

Howard Hughes, for example, replaced a fragmented data stack with Boomi Data Integration. ETL runs that took up to 12 hours now finish in under 40 minutes, data warehouse costs fell by 83%, and overall architecture spending dropped by 60% to 70%. An in-house team of two manages pipelines that previously required six external consultants, saving the company $300K a year.

Boomi enables results like these through six capabilities:

• Boomi Data Integration: managed ELT, CDC replication, and pipeline operations, covering orchestration, monitoring, validation, and version control, so your pipelines are maintained rather than left waiting in a queue of change requests.
• Boomi Event Streams: thanks to real-time, event-driven integration with guaranteed delivery and ordering across pub/sub, queuing, FIFO (first-in, first-out), and REST API modes, agents can read changes as they happen instead of after the next scheduled load.
• Boomi Meta Hub: governed business context linked to your technical assets, with glossary terms that move through an endorsement lifecycle, which means an agent works from definitions that subject matter experts have approved. End-to-end lineage mapping is in early access.
• Boomi Data Hub: golden records built with automated validation, matching, third-party enrichment, and survivorship rules, then synchronized with the source applications in both directions.
• Boomi API Control Plane and Boomi MCP: the API products you already run, published as hosted MCP servers without custom code, plus a vendor-agnostic MCP catalog governed from creation to retirement, so agents can find and call the tools they’re allowed to use.
• Boomi Agent Control Plane: one place to build and govern your agents, then orchestrate and observe them at runtime, across data and integration workflows.

At Lexitas, an agent working from golden records resolves 30% of payments with no human involvement, after the company cut 80% of duplicate customer entries left by 40 acquisitions.

Are your agents only ever as current as last night’s load window? Read “Beyond Pipelines: The New Data Operating Model for AI at Scale” to see what replaces the pipeline-by-pipeline approach.

Frequently asked questions about agent-ready data pipelines

Why can’t we just use our existing ETL pipelines for AI agents?

ETL pipelines were built to deliver data on a schedule to reports that people read later. An agent treats whatever it reads as the current state of the business, so a scheduled load that is fine for a monthly forecast can be wrong for a decision like a delivery date. Existing pipelines can keep feeding reports while you add fresher delivery paths for agents.

What is change data capture (CDC), and when do we need it?

CDC picks up each insert, update, and delete in a source system as it happens, and moves only the changed records. Log-based CDC reads from the database’s transaction log, so it adds no query load in production. Use it where a few hours of delay would lead to a bad decision, such as halting collections when a payment clears.

What does end-to-end lineage mean for AI agents?

Lineage records each value’s source and every transformation it went through, stored alongside the data the agent reads. Agents often combine values from several systems in one decision, so lineage also needs to log each agent query, the data returned, and the tools called. That record lets you explain a decision to a customer, reverse it, or show an auditor what the agent relied on.

Should agents query the data warehouse or the source applications directly?

Neither approach works alone. Centralize shared data that needs validation and cleansing, and leave the rest in operational applications. Give agents governed access to that data through APIs and, where appropriate, MCP servers. Where several applications hold their own copy of the same customer or product, use master data management (MDM) to create one golden record and sync it back to the source systems.