Backed by Y CombinatorWhat an agent needs to answer a time-series question
A useful answer connects a precise question to the right readings, their meaning, and the sources that let someone check the conclusion. A public building-energy example makes that contract concrete.
In this article
The question decides which readings matter
An agent answering a question about a time series needs a way to find relevant measurements, understand what they represent, and connect them to a conclusion someone can inspect. Giving it access to a database starts that process. The harder design work is deciding what information an answer must preserve.
Consider a request to find the biggest energy spike in a building. That could mean the highest individual reading, the fastest increase, or the greatest consumption over a sustained period. Each interpretation can select a different event. Before discussing models, we need to turn that request into a calculation with a clear unit and time window.
For our public example, we use the UCI Appliances Energy Prediction dataset. It records appliance energy in watt-hours at ten-minute intervals, alongside other building and weather measurements. We ask a specific question: which three consecutive readings have the highest combined appliance energy in the first seven complete days? This fixes what we will compare and what would count as a correct numerical answer. 1
One week becomes an inspectable event
The selected week contains 1,008 readings, from 12 through 18 January 2016. Our reference script checks all 1,006 groups of three consecutive readings. The largest sum is 560 + 1,080 + 750 = 2,390 Wh, recorded at 18:40, 18:50, and 19:00 on 16 January. The evidence includes those values and their locations in the original file: CSV lines 732, 733, and 734, counting the header as line one. 2
There is a small but consequential detail in that answer. The timestamp labels span twenty minutes, while three ten-minute energy readings represent thirty minutes of measured consumption. The source does not specify whether each timestamp marks an interval's start or end, or provide a timezone. We retain its labels and state the interval convention as unresolved. 2
The three readings let a reader check the addition. The claim that this is the week's maximum also depends on the full scan. An inspectable answer therefore needs both the selected evidence and a record of the comparison that selected it. A cropped chart alone cannot establish that no larger window was excluded.
1,008 readings · appliance energy · Wh per ten-minute interval
What the agent can establish
2,390 Wh across three readings.The largest three-reading sum in the selected week.
Source timestamp labels are preserved; interval boundaries and timezone are unspecified. Overview is downsampled; the calculation uses every source reading. Change the question to see what the same evidence can—and cannot—answer.
Preserve the meaning of the measurement
A value of 1,080 means little without its unit. Watt-hours measure energy; watts measure power. A sensor's instantaneous output, an interval total, and a cumulative meter reading require different calculations. Summing three values is appropriate for the energy intervals in this example. Applying the same operation to three cumulative meter readings would answer a different, usually unhelpful question.
The entity matters just as much. These are aggregate appliance-energy measurements. They do not identify which appliance was running. A useful answer keeps that distinction visible even if the reader asks about a particular device. Otherwise a correct calculation can become the foundation for an unsupported diagnosis.
Coverage deserves similar care. Our selected week has a consistent ten-minute cadence and no missing appliance values. In another record, three neighboring rows could straddle a gap. An agent needs that gap surfaced before treating the rows as a continuous period. Data quality is part of the answer because it determines which comparisons are valid. 2
Make the output smaller without weakening the evidence
Sending every reading to a language model is one possible interface. A more focused interface can perform the numerical work first and return the result with enough context to examine it. For this question, the useful output contains the selected week, the comparison performed, the three readings, their source locations, and the limits of the conclusion.
Our two-column timestamp-and-energy CSV contains 16,838 tokens. The complete evidence JSON contains 529, measured with tiktoken 0.11.0 and the o200k_base encoding. These are counts of the serialized files. The replay makes zero language-model calls, so it measures neither answer quality nor total agent spending. A SQL query or Python script can compute the same maximum. 2
The important design question is what survives the reduction. A sentence saying that energy use was unusually high would remove the calculation and introduce a judgment we have not established. The compact evidence retains a narrower, checkable claim. An agent can inspect the full source when the next question needs more context.
Keep the decision time separate from the event time
This example scans a completed week. A live decision introduces another boundary: what information had arrived when the decision was made? Suppose a maintenance record describes work on Tuesday but is entered on Thursday. It may help explain Tuesday's measurements in a later investigation. It could not have informed an agent answering on Wednesday unless the information was available elsewhere.
This is a practical data issue. Feast's documentation distinguishes a feature's event timestamp from the creation timestamp used to constrain when the value was available. Filtering only by event time can admit later corrections into an earlier retrieval. The same distinction matters when constructing evidence for an operational agent. 4
A retrospective explanation and an answer produced during an event can both be useful. Their evidence needs different time boundaries. An evaluation should preserve the boundary relevant to the actual job rather than quietly supplying later knowledge.
Give the agent a useful next question
The energy event is established. Its cause is open. The selected maximum does not show that the building was wasteful or faulty. Device-level measurements, operating schedules, or intervention records could support a more specific investigation. Naming the missing evidence gives the agent somewhere productive to go.
This is the interface we are working toward at Reific: a precise question produces relevant readings with enough context and source access for an agent to form a bounded answer. Anthropic's guidance on tool design makes a related point: tool responses should provide useful context, and changes to those tools should be tested on realistic tasks. 3
Our public replay makes that interface tangible. Demonstrating a product advantage requires a further comparison against an agent with appropriate query tools, using the same questions and information. The result to look for is a better complete investigation: correct readings, supported conclusions, useful requests for missing evidence, and less work left for the person checking the answer.