It is mid-June, and the budget you approved in December no longer matches reality. One line is running hot, another came in soft, and a new hire was not in the plan. The reforecast that fixes all this is mostly mechanical: pull the actuals, compare them to plan, project the rest of the year, and write up what changed. It is also a chore most finance teams dread, because it means hours of rekeying spreadsheets right when the quarter is busiest.

This guide walks through how an AI agent handles the mechanical part of a mid-year reforecast, so you spend your time reviewing and deciding instead of copying cells. It builds on the basics in what is an AI agent, and it follows the same outcome-first approach as how to set up your first AI agent.

What a reforecast agent does

A budget reforecast agent pulls your actuals to date, compares them to the original budget, projects the rest of the year from run-rate and known changes, and drafts a revised forecast with the variances explained. The human reviews and decides; the agent never finalizes a number on its own. It removes the rekeying, not the judgment.

The split matters. The agent is fast and tireless at the mechanical work: opening exports, matching line items, extending trends, and writing first-draft commentary. The judgment, whether a soft line will recover, whether a one-off should be excluded, stays with you. So the agent is a research assistant for your numbers, not a decision-maker. If you want the line between the two clearer, see AI agent vs chatbot vs assistant.

1. Define the outcome

Before the agent touches a single export, name what a finished reforecast looks like. The most reliable runs start from a one-sentence outcome rather than a list of steps. For a mid-year reforecast that sentence is usually: a revised full-year forecast, by line item, with each material variance explained, ready for your controller to review.

Why the outcome comes first

Naming the outcome first keeps the agent aimed at something you can check. A defensible revised forecast is the goal; everything the agent does exists only to reach it. The outcome also gives you the final test. If you cannot say how you would verify the forecast is sound, the task is not ready to hand over. Describing the result you want, rather than a procedure, is the same pattern that makes any agent run cleanly.

2. Gather actuals and the budget

With the outcome fixed, the agent gathers two inputs: your original approved budget and your actuals to date. These usually come from an accounting or ERP export and a copy of the board-approved plan. The agent matches them line by line, flags any line that exists in one but not the other, and asks you to confirm the mapping before it computes anything.

Known changes for the rest of the year

Actuals only describe the past. The projection also needs the changes you already know are coming: a hire starting in August, a contract renewal at a new rate, a planned price increase, a project that slipped a quarter. You feed these in plainly, and the agent attaches each one to the line it affects. The cleaner your inputs, the more defensible the draft; this is where careful data handling pays off, a theme we return to in AI agent for Stripe revenue reporting.

3. Compute variance vs plan

Once the data lines up, the agent computes variance: actuals to date against the budgeted amount for the same period, by line item. It reports each variance in both absolute and percentage terms and sorts by what is most material, so you are not reading every tiny line. The point is to surface where reality diverged from plan, fast.

Materiality and one-offs

Not every variance deserves attention. The agent applies a materiality threshold you set, say anything over a few percent or a fixed dollar amount, and groups the rest. It also flags suspected one-offs, a single large invoice, a timing difference, so you can decide whether to treat them as recurring. This is judgment work, so the agent surfaces the candidates and you make the call. It shows the math behind every figure, never hiding a number you might need to defend.

4. Build the projection

Now the agent projects the rest of the year. It starts from run-rate, the pace your actuals have set so far, then layers in the known changes you supplied and any seasonality already in your plan. Each projected figure carries the assumption behind it in plain language, so the forecast reads as a set of stated bets, not a black box.

Stated assumptions you can change

This is the part that earns trust. Next to each projected line, the agent writes the assumption: "holds at the current six-month run-rate," "adds one engineer from August at the rate you gave," "no seasonal lift, per last year." If you disagree with an assumption, you change it and the agent recomputes the projection at once. Because the assumptions are visible and editable, the forecast is yours, not the agent's. Knowing what each run costs before you lean on it is its own discipline, covered in how to estimate agent cost before deploying.

5. Draft the variance narrative

Numbers alone do not survive a review meeting; someone always asks why. So the agent drafts the variance narrative: a short written explanation for each material line, tying the variance to its likely cause and to the assumption carried into the projection. This first-draft commentary is where the agent tends to save the most time, because writing it by hand is slow and repetitive.

A draft you edit, not a final word

The narrative is a starting point, not the verdict. For example, the agent might write something like "marketing is running under plan, driven by a delayed campaign now expected in Q4," and you correct it where the real story differs. You know the context the data cannot show. The agent gives you a clean first pass so you edit and add color rather than starting from a blank page. Keeping these runs affordable as they become routine is worth planning for, which is the focus of AI agent cost control.

6. Route to a human for approval

The final step is the most important: nothing ships without you. The agent assembles the revised forecast, the variance table, and the narrative into one package and routes it to you for review. You can change any number, reject any assumption, or send the whole thing back. The agent never finalizes the forecast; approval is yours alone.

What approval should give you

Good approval is more than a yes. The package should let you trace any figure back to its source export, see the assumption behind any projected line, and keep a record of what changed from the original budget and why. That trail is what makes the forecast defensible to your CFO or board. How you pay for the work fits the same transparent spirit: on Gravity, you pay per use at one dollar for one thousand credits, only when the agent actually runs, a model explained further in AI agent cost models explained.

Frequently asked questions

Can an AI agent reforecast a budget?

An AI agent can do the heavy lifting of a reforecast. It pulls actuals to date, compares them to the original budget, projects the rest of the year from run-rate and known changes, and drafts a revised forecast with variances explained. You review the assumptions and approve the final numbers.

What data does a budget agent need?

A budget agent needs your original approved budget and your actuals to date, usually from accounting or an ERP export. It also needs known changes for the rest of the year, such as new hires, price moves, or contracts. The more complete the inputs, the more defensible the projection it can draft.

Does an AI agent decide the new budget?

No. The agent drafts a revised forecast and explains how it got there, but it never decides anything on its own. Every number and assumption is yours to review, change, or reject. The agent shows its work so you can approve a forecast you understand and can defend to leadership.

How does an agent project the rest of the year?

The agent starts from your run-rate, the pace your actuals set so far, then layers in the known changes you provide and any seasonality in your plan. It states each assumption next to the number it affects. You can adjust an assumption and have the agent recompute the projection instantly.

How do I trust an AI agent's forecast?

Trust comes from transparency, not from blind faith. A good agent shows its source data, its math, and every assumption behind a projection, so you can trace any figure back to its origin. You approve the numbers, the agent never finalizes them, and you keep a clear record of what changed and why.

Three takeaways before you close this tab

On Gravity, you do not configure any of this. You describe the outcome, a defensible mid-year reforecast with variances explained, point the agent at your budget and your actuals, and review the package it hands back in about 60 seconds. You can join the waitlist or browse the glossary if a term here was new.

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