)
Agentic Data Pipeline Factory
Automating the construction of given products with shared memory
THE PROBLEM
Building enterprise data products remains slow and manual
Bringing a raw source to a data product ready for business intelligence is still, in most organizations today, a long and largely manual job. It is necessary to understand the sources, build the transformations, write the tests, document, and keep up when something upstream changes. Each phase requires specialized expertise and time, and the result is a path that struggles to scale with the number of sources and projects.
One of the reasons is that the knowledge necessary to operate effectively tends to not be centralized. Business rules are distributed among different teams. The definitions of indicators are not always unique. Decisions made in earlier phases of the project are not systematically tracked.
The result is a process that does not capitalize on accumulated experience: every new activity starts from conditions similar to those at the beginning, every new team member takes weeks to become operational, and every answer to the question “how is this indicator calculated?” risks varying depending on the interlocutor.
The introduction of AI agents promises to accelerate these activities, but the risk is that agents lacking a shared knowledge base amplify the problem instead of solving it: they reinvent existing definitions, ignore decisions already made, and diverge from each other. Multiplying agents without giving them a common memory does not solve the dispersed knowledge; it amplifies it.
THE CHALLENGE
Automate without losing consistency
The challenge is twofold. On one hand to automate as much as possible the process that leads from the source to the data product, putting to work specialized agents on the different phases of data engineering. On the other hand, ensuring that these agents operate consistently in the same domain, capitalizing on every interaction instead of starting over from scratch each time. To achieve this, it is necessary to overcome three typical limitations: the fragmentation of knowledge between teams and systems, the absence of a single source of truth for definitions, rules, and decisions, and the lack of a validation mechanism that ensures its reliability over time.
THE SOLUTION
An agency factory with a shared memory
)
The answer is a multi-agent platform, the Agentic Data Pipeline Factory, which automates the journey from raw source to BI-ready data product.
The idea is to cover the different phases of data engineering with specialized agents instead of repeated manual labor: agents that read and profile the sources inferring structure and semantics, agents that build transformations and models, agents that generate and maintain tests, agents that document and track dependencies. Above them, a level of orchestration coordinates the flow, and a process of validation keeps the human expert at the point in the cycle where their judgment really matters.
By itself, however, such a platform would fall into the problem described earlier: many competent agents who, without a common memory, start from scratch each time and diverge from one another. What transforms a set of agents into a coherent factory is the technical component that constitutes its backbone, the cognitive layer.
The cognitive layer: the technical protagonist of the Factory
The cognitive layer is the structured and queryable memory of the factory: the layer where the client's domain knowledge lives in a persistent form, is read symmetrically by agents and human operators, and grows with the use of the platform.
It is worth clarifying what it is not: it is not an agent, it does not make decisions nor orchestrate workflows. It is the informational foundation that agents consult to operate competently in the client's domain. The cognitive layer responds, it does not act: the agents are the workers, it is the well-cataloged knowledge they draw from.
Three fundamental choices explain why it works. The first: enterprise knowledge has different natures, relational (connected entities, dependencies, derivations), factual (precise definitions and rules) and semantic (free text, discussions, narratives), and the cognitive layer adopts different storage forms for each. Value arises when they work in combination, allowing for answers to complex questions that none of the three would cover alone, bringing together dependency structure, certain facts, and narrative context in a single answer.
The second, and it is the principle that distinguishes a true factory from a toy: no information enters the system as acquired truth. Every entry has an explicit validation state. Agents propose, competent people validate or reject, and only then does the knowledge become reliable and reusable. A dedicated component receives the proposals, automatically promotes those with low risk and routes the others to the competent domain expert, escalating to those with authority the decisions on critical assets. Every step leaves a trace in an audit log.
The third: knowledge has a history. An indicator in 2023 is not the same as in 2026, and a replaced rule remains relevant to explain a past report. The cognitive layer versions knowledge over time, returning by default the current version but always allowing to ask what a definition was at a certain point in the past.
Finally, agents never speak directly with the archives: they query tools with explicit names, each of which answers a domain question. Thus, different agents draw from the exact same source and receive consistent answers.
End-to-end automation
Less manual work, more speed: phases that were previously sequential now run in parallel, and the team scales without growing proportionally.
One single truth, for all agents
Divergent definitions disappear: each agent draws from the same source, and the responses become consistent with each other instead of contradictory.
The experience that does not fade
Every human validation remains in the system and becomes reusable capital: those who come later do not start from scratch, and the scale becomes sustainable because the context does not need to be rebuilt every time.
Predictable impact, not discovered retrospectively
A structured view of dependencies allows you to anticipate what happens downstream before intervening, not after.
Traceability: the guarantee that holds everything together
Each response carries its own sources and level of reliability — this is what makes all the previous benefits verifiable, and therefore reliable.
Use Case
The use cases show the factory at work, with the cognitive layer providing context at every step.