How poor data quality drives up AI agent costs

Enterprise AI deployments are running into an unexpected problem: unpredictable operating costs. Cost overruns have become so severe and widespread that Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating, unbudgeted spending. 

Often, the source of uncontrolled AI costs lies in the enterprise’s data layer. Poor data quality has always stood in the way of teams achieving desired business outcomes. But when data quality errors meet agents trained to solve problems, data liabilities go from an operational impediment to an unacceptable liability for the enterprise. 

In this article, we’ll show how common data quality problems produce unwanted agent behavior that rapidly increases token consumption and leads to unsustainable AI costs. We'll also outline concrete steps data leaders can take to close these gaps and confidently launch AI agents that drive business outcomes and ROI. 

How poor data quality multiplies AI inference costs

When faced with missing, incorrect, or incomplete data, AI agents lack the context and judgment to handle the situation in accordance with business goals. While a human analyst will stop what they are doing and flag the underlying problem, AI agents persist. They will retry queries, reformulate prompts, and chain extra tool calls together until they reach a brute-force solution. 

In many agent implementations, each model call includes some or all of the accumulated conversation and tool history. As that context grows, input-token consumption can increase substantially, especially when agents retry tasks or investigate missing information. When such behavior scales across thousands of daily queries, AI spend can quickly reach unsustainable levels. 

Which data quality issues reliably drive agent step inflation?

Not every data quality error reliably produces expensive agent behavior. The costliest errors trigger an agent to kick off one of its more intensive fallback workflows, like attempting to verify conflicting information, recover from a blocked response, or search across separate repositories for missing answers. 

With this in mind, here are three categories of data quality failure that have the most potential to turn routine lookups into budget-busting loops. In each category, we calculate the input and output tokens the model would expend beyond what a response to healthy data would require, then apply OpenAI’s documentation and pricing structure to determine the potential additional cost above baseline.

Without lineage-aware classification, models try to expose sensitive data

Estimated cost multiplier: 2–3x compared with completing the same task using data handled appropriately upstream.

Models that lack appropriate context on data lineage may try to generate outputs that expose sensitive data like Social Security numbers, credit card details, or health records. If an output guardrail blocks the generated response, the application may sanitize the request and try it again.

• Healthy scenario: The application supplies data along with provenance information that instructs the model on how to handle it appropriately, and the agent produces a usable response in one model call.‍
• Healthy cost: 2,800 input and 180 output tokens ($0.0088 under our assumed rates).‍
• Failure scenario: The agent, lacking information on data lineage that would tell it how to protect the data according to the organization’s policy, generates a response containing an unmasked Social Security number or credit card detail. An output guardrail blocks it after generation, after the model has already processed the request.‍
• Recovery workflow: The application sanitizes the request and calls the model again. If the replacement also fails, it permits one final retry. Assuming comparable token consumption per attempt and 2-3 total calls, the total token consumption is 5,600–8,400 input and 360–540 output.‍
• Estimated failure cost: 2–3x the healthy baseline. Pre-call masking or stopping after rejection avoids this retry path.

When data pipelines lag, models try to use stale data

Estimated cost multiplier: 2.2–3.5x compared with completing the same task using up-to-date data.

Data pipelines that don’t run in real time can serve outdated records. An agent instructed to verify account details before responding may query transaction histories, support tickets, or audit trails to resolve the discrepancy, turning a routine lookup into a multi-step investigation.

• Healthy scenario: The application supplies the customer’s current address record, and the agent answers in one model call.
• Healthy cost: 2,800 input and 180 output tokens, costing $0.0088.
• Failure scenario: A customer asks about an address change made an hour ago, but the database returns the old address. The agent requests additional evidence rather than answering from the stale record.
• Recovery workflow: The agent checks the audit log and makes a second model call to determine whether the address was updated. If the log confirms the change, the agent answers. If not, it checks a support ticket and makes a third model call to review that information. Each additional call includes the earlier conversation and the new records, giving the model more text to process.
• Estimated failure cost: We assign 2,800 input tokens to the initial call, 3,400 to the second, and 4,000 to the optional third, with 180 output tokens per call. Total consumption reaches 6,200–10,200 input and 360–540 output tokens, resulting in a cost 2.2–3.5× the healthy baseline.

Missing data sends agents roaming for answers

Estimated cost multiplier: 4–8x compared with completing the same task using complete data.

When an AI agent sees empty fields, it may search supporting documents for the missing value. If those documents don’t provide a clear answer, it may search for more information and make additional model calls to review what it finds, sending more text to the model each time. A null rate spike, or a sudden increase in missing values in normally populated columns, can activate this recovery workflow across many requests at once. 

• Healthy scenario: A high-volume B2B freight marketplace uses an autonomous billing reconciliation agent to audit detention and fuel surcharge claims. With complete data, the application supplies the surcharge rate and calculation inputs, allowing the agent to complete the audit in one model call.
• Healthy cost: At 2,800 input tokens and 180 output tokens, the audit costs $0.0088 under assumed rates of $2.50 per million input tokens and $10 per million output tokens.
• Failure scenario: An update to a critical database table causes the null rate in a critical column to jump from 0.05% to 32%. The agent’s instructions tell it to “resolve disputes using available contract documentation,” so when the database lookup returns null, the agent begins searching through PDF contracts.‍
• Recovery workflow: The agent’s hunt for the missing value triggers four separate calls. The first call identifies the missing rate and requests a semantic search of the contract repository. The search returns 3,500 tokens of contract passages. The second call interprets those passages to identify the relevant surcharge terms. The third call extracts the rate formula and invokes a calculation tool. Finally, the fourth call interprets the calculation result and produces the user-facing summary. Conversation and tool history accumulate across calls: the contract passages and earlier results remain in context, increasing the input processed by subsequent calls.‍
• The cost difference: Across the four calls, our modeled token budget reaches 19,400 input tokens and 720 output tokens. Applying the same assumed rates raises model-token cost to $0.0557, or a 6.3x cost multiplier. 

Closing the gap

As a first step towards addressing critical data quality issues, start by tracking cost per conversation and checking spending spikes against known data quality incidents. Consolidate those signals into a single dashboard, and prioritize the tables and columns that agents query most often, where a single defect can trigger the same expensive fallback hundreds or thousands of times.

Bigeye’s Agent Trust Hub brings agent costs and data quality signals together. Its Cost Anomaly Detection flags spending changes and connects them to conversations and the underlying data, giving teams a place to investigate without a manual audit. 

Book a demo to see how Bigeye helps you trace rising agent costs to the conversations and data behind them.