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Start by defining the prediction
Specify the target, subject, and prediction time
Before assembling data, state what the agent must predict, for which person, object, or process, and at what point the prediction is made. Define the outcome precisely—for example, whether a customer cancels within a stated future period—and ensure each training example can be linked to that outcome after it becomes known.
The prediction time is essential because it determines which inputs are legitimate. A cancellation date, final invoice, or inspection result may be useful for confirming the label but would be unavailable when predicting the outcome. Including such information as a feature creates data leakage: offline scores can look strong even though the model could not make the same prediction in practice.
Match the records to the decision
Decide how each prediction will be used and what population it will cover. A model intended to predict next month’s demand across existing stores has a different data need from one intended to estimate risk for newly opened stores. The historical examples should reflect the relevant operating conditions, not merely the records that happen to be easiest to collect.
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Choose records and fields for the task
Each example needs an outcome in the form the task requires, plus predictors that are available at prediction time. Add timestamps and stable entity or series identifiers when the task depends on time or repeated observations of the same entity. The appropriate record shape and evaluation approach vary by task:
| Task | What the target represents | Important data considerations |
|---|---|---|
| Classification | A category, such as whether a claim is fraudulent | Define the categories consistently and include enough examples of relevant categories, including minority classes, to evaluate them meaningfully. |
| Regression | A numeric value, such as delivery time or expected revenue | Check units, valid ranges, and how the numeric outcome is measured; large or inconsistent errors in labels can undermine the model. |
| Forecasting | A future value or outcome for a time series | Preserve timestamps, series identifiers, observation cadence, and the forecast horizon. Missing or irregular time intervals need to be understood rather than silently treated as equivalent observations. |
For forecasting, Google Cloud’s Gemini Enterprise Agent Platform documentation specifies a target, time field, and time-series identifier for each observation, with consistent intervals and a narrow/long data format. It accepts BigQuery tables or CSV as training sources. These are requirements and options for that platform, not universal rules for every forecasting system; another tool may accept different formats or handle time fields differently.
How much data is enough?
There is no row-count threshold that by itself establishes reliability. Adequacy depends on how varied the population is, how often the outcome occurs, the number and complexity of features, and whether the dataset covers the conditions the model will encounter. Google’s tabular guidance cautions that even 1,000 rows may not be enough for a high-performing model, depending on the number of features.
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Google Cloud’s Gemini Enterprise Agent Platform documentation gives the following platform-specific heuristics and limits. They should not be read as general guarantees of model quality or as universal minimums:
| Documented figure | Meaning and qualification |
|---|---|
| At least 1,000 rows for a tabular dataset | Platform guidance; the same documentation warns this may not suffice, depending on feature count. |
| At least 10 rows per column for classification; 50 rows per column for regression | Platform heuristics, not a substitute for assessing whether the examples support generalization for the task. |
| At least 10 time series for every feature column used for forecasting | Platform guidance for forecasting data. |
| 3–100 columns, 1,000–100,000,000 rows, and no more than 3,000 time steps per series | Documented forecasting data limits for that platform, not a definition of how much data is adequate. |
The publication date is not stated on the reviewed Google Cloud pages. Check the platform’s current documentation before relying on these figures for an implementation decision.
Make labels and features dependable
Profile records and outcomes
Check for missing values, invalid ranges, duplicate records, inconsistent categories, and labels that are ambiguous, delayed, or wrong. Confirm that fields mean the same thing across sources and over time. For example, a status code that changes meaning between departments or product versions should not be treated as one stable category without resolving the definition.
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Use features that can be reproduced at inference
Derived features—such as lagged values, historical aggregates, calendar signals, or distance calculations—can help when they capture real information available at prediction time. Record how each is computed and make the same logic available when the agent serves predictions. Google’s tabular guidance warns that generating training and inference features differently can create training-serving skew: the model is trained on one representation and receives another in use.
Time signals can be useful when behavior changes by season or period. Location-based features or aggregates should represent meaningful relationships, and only use information that would be accessible for the future case being scored.
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Split data to reflect deployment
Keep training, validation, and test data separate. Training data is used to fit the model; validation data supports choices such as feature or model selection; the test set is held back for a final assessment rather than repeatedly used to tune the system. Fit preprocessing steps—such as imputation, scaling, or category handling—on training data, then apply those fitted transformations to validation and test data.
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- For time-dependent predictions: split in chronological order so that evaluation uses later periods than training. Randomly mixing past and future records can allow information from the future to influence an estimate of performance.
- For predictions on new entities: keep records for the same entity in one split rather than distributing them across training and test sets. Otherwise the evaluation may reward recognition of an entity already seen during training rather than performance on genuinely new ones.
- For changing populations or conditions: make validation and test examples resemble the population and horizon the agent will face, while preserving a genuinely held-out test set.
The Australian Government Digital Transformation Agency’s AI Technical Standard summary treats purpose-aligned, representative data and separate training, validation, and testing sets as requirements within its scope. It also recommends profiling, label-quality checks, and data-engineering practices. Its applicability depends on the system and jurisdiction; it is not a rule governing every organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate predictions beyond a single score
Compare the model with a simple baseline, such as a straightforward historical estimate or a basic rule appropriate to the task. A complex model that does not improve on a credible baseline may not justify its added operational cost. Choose metrics that fit the outcome and decision: for example, classification metrics should account for class balance and the cost of different errors, while forecasting metrics should be assessed over the intended horizon.
Inspect results for meaningful population slices, not just an overall average. Performance may differ across locations, customer groups, product types, or time periods; those differences can determine whether the system is useful or fair in practice. Record the data schema, feature definitions, transformations, split logic, metrics, and experiment settings so another person can understand what was evaluated and reproduce it. Google’s predictive ML guidance recommends baselines, separate holdout testing, representative splits, repeatable preprocessing, and experiment tracking.
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Give the agent governed, repeatable data access
The agent needs dependable access to authoritative sources through suitable query or API tools, with permissions scoped to the data and actions it is allowed to use. Clear definitions for tables, fields, metrics, and update timing help prevent the agent from confusing similarly named values or drawing conclusions from stale or incomplete data. Access should be traceable so that the source and analytical steps behind a prediction can be reviewed.
Google’s reference architecture describes separate analytics, database, and machine-learning agent roles using BigQuery and AlloyDB as example sources. Microsoft’s guidance likewise emphasizes that agent accuracy depends on the quality and accessibility of underlying sources and on secure, governed access. These are vendor examples, not evidence that a multi-agent design or either vendor’s products are necessary.
Plan for monitoring and change
After deployment, monitor input quality and data distributions, then review prediction performance as outcomes become available. Define who investigates missing fields, unexpected shifts, degraded results, or changes in how labels are produced. Also decide how features and models will be refreshed when the underlying process or population changes. The cited guidance does not establish a universal monitoring cadence or alert threshold; set them according to the risk, update rate, and consequences of the specific use case.
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