The silent failure of many agentic AI projects in production is not explained by immature models or a lack of technological sophistication. It reveals a deeper structural mismatch: organizations are now delegating decisions to autonomous systems without having formalized, in their data, the exact framework within which these decisions are to be made.
The first article in this series showed how autonomy makes long-tolerated ambiguities operational. A more pressing question now emerges: what conditions really need to be in place for agentic AI to operate reliably, explainably and sustainably at scale?
[↳ see also: Why agentic AI fails in production]
The answer lies neither in an additional layer of artificial intelligence, nor in a refinement of agents' reasoning capabilities. The answer lies earlier in the value chain — in the quality, structure, and governance of the data that defines the boundaries of their action. As AI moves from decision support to autonomous execution, data ceases to be a mere informational asset. It becomes the explicit framework within which autonomy can be exercised without drift.
Traditional AI systems could work with imperfect data as long as their results remained descriptive, exploratory or assisted. The human then retained a central role of interpretation, arbitration and correction, capable of compensating for what the data did not explicitly formalize.
Agentic AI fundamentally changes this dynamic. As soon as an agent triggers an action, prioritizes a workflow or makes a judgment without human mediation , every approximation contained in the data ceases to be a tolerable imperfection and becomes an executed decision.
The question is therefore no longer whether the data is "good enough", but whether it is sufficiently defined to be executed without interpretation. In an agentic environment, data no longer describes an observed reality; it prescribes an operational reality. It defines what is possible, what has priority, what is acceptable - and what is not.
Changing status of data in an agentic context
It is precisely for this reason that datasets deemed acceptable in analytical contexts become structurally insufficient once autonomy is introduced. A missing value, an ambiguous business rule or a partially aligned data source no longer produce a one-off anomaly. They generate behavior that is consistent on a large scale, but progressively disconnected from the original business intent.
This shift explains why data quality ceases to be a subject of continuous optimization and becomes a prerequisite for execution. In an agentic system, what is not formalized is not interpreted differently; it is executed as is.
Data quality is still too often understood in terms of classic technical criteria: completeness, accuracy, freshness. While these dimensions remain necessary, they are largely insufficient when AI is no longer content to analyze or recommend, but to act.
In an agentic context, the issue is no longer "clean" data, but unambiguously executable data.
In this context, quality data can be defined as :
Data whose origin, transformations, business context and usage rules are explicitly documented, traceable and governed, so that it can be executed without human arbitration.
This definition addresses a fundamental operational constraint: an agent can neither guess at an intention, nor compensate for implicit assumptions, nor negotiate a historical compromise. Where humans fill in the gaps through experience, agents strictly apply what they are given.
Data quality thus becomes the only mechanism for aligning automatic execution with initial business intent.
In an agentic system, poorly defined data does not necessarily generate an immediate error. It creates a fragile decision-making trajectory, the effects of which only become visible as autonomy is exercised over time. The drifts observed in production appear precisely when areas of organizational ambiguity are automated without having been stabilized beforehand.
This mechanism recurs when :
These situations do not produce random errors. They generate consistent, reproducible, yet erroneous decisions at scale. Agents do not deviate from their logic. They faithfully execute the data environment they are given
that the organization has never really clarified or stabilized.
It is precisely in order to deal with these ambiguities upstream - formalizing rules, stabilizing repositories and making transformations traceable - that an approach is needed to make data reliable before it is exposed to autonomous AI systems, so that autonomy is based on an explicit, governed framework, and not on implicit interpretations.
As autonomy progresses, data quality issues cease to be confined to data teams. They take on direct economic consequences. As a system becomes more business-critical, the cost of late remediation increases, not linearly but cumulatively.
Correcting a data quality problem after it has gone live costs on average five to ten times more than when it is identified and dealt with upstream. In an agentic context, this differential no longer only affects IT costs. It directly impacts operations, compliance, risk management and the credibility of automated decisions.
The reason is structural. Imperfect data in an autonomous system doesn't just produce an isolated error. It feeds a chain of decisions that are executed, reproduced and amplified over time. Remediation is no longer a matter of correcting a piece of data, but of reconstructing a decision trajectory already underway.
At this point, data quality ceases to be a cost center. It becomes a driver of operational sustainability.
Data governance is still frequently perceived as an obstacle to innovation. Agentic AI radically reverses this perception. Without explicit governance, autonomy becomes unstable by construction.
Organizations that succeed in industrializing agentic systems share a common characteristic: they have invested upstream in a governance framework capable of supporting autonomous execution over time. This framework is based on :
This framework is not imposed by regulations, even though it fully complies with them. It is imposed by the very logic of autonomy. As soon as a system acts without human mediation, any ungoverned zone becomes a point of systemic fragility.
This is where approaches such as Reliable Data for AI Projects become strategically critical. Their aim is not to improve data in the abstract, but to build a data foundation capable of supporting autonomy without drift.
Continuously auditing data sets, formalizing business rules, documenting each transformation and making flows auditable can significantly reduce the risks of silent failure observed in production. Not by limiting agentic AI, but by providing it with an explicit, governed and justifiable framework for action.
Agentic AI doesn't require perfect data.
It requires understandable, explainable and defensible.
Agentic AI marks a profound break in the way organizations design their information systems. This break is not primarily technological. It's structural. As autonomy progresses, data quality ceases to be an adjustment variable. It becomes the very condition for action.
Organizations that have understood this do not seek to correct after the fact. They build the foundations upstream for sustainable autonomy, capable of creating value without generating systemic risk. Having analyzed why agentic AI fails in production, the next question naturally arises: how can we prepare data that is sufficiently reliable, traceable and governed so that autonomy becomes a lever, not a threat?
At this point, the issue is no longer theoretical. It becomes operational. Before scaling up, it is essential to objectively assess the actual level of maturity of datasets, identify implicit rules and map areas of ambiguity that could be industrialized. This is precisely the vocation of a Flash Audit: to provide a rapid, structured assessment of data fragilities, reveal gaps between business intent and potential execution, and enable organizations to secure their foundations before exposing their systems to autonomy.
This is precisely where the difference lies between promising experiments and truly production-ready agentic systems.