AI Agent Stocks Dashboard Excel: Agentic AI Software Screener for Summer 2026

AI Agent Stocks Dashboard Excel - if you are trying to separate the software vendors with a real agentic AI flywheel from the ones just pasting "AI" onto their slide deck, the dashboard linked below is built for exactly that triage. Agentic AI (software that can plan, take multi-step actions, and call external tools, not just answer questions) has become the single biggest narrative in enterprise software for 2026, and the public market has rewarded it unevenly. Some names trade at a P/S above 20 on the strength of agent revenue that still does not show up as a separate disclosed line. Others have shipped credible agent platforms and the multiple has barely moved. This dashboard is the tool I would use to organize that universe on one screen. It ships a professional-grade Excel workbook that ranks twelve major agentic AI software vendors on growth, valuation, margins, and a hand-curated agentic AI composite score, then classifies each ticker into Buy / Hold / Watch / Avoid action zones, and stress-tests the screen across an adoption-rate and attach-multiple grid. The template is built on live MarketXLS formulas, so when you open it with the MarketXLS Excel add-in every value refreshes against the latest fundamentals. The visual language is dashboard-style (KPI tile row, embedded charts, conditional-formatted heatmap, scenario grid, comparison matrix) and not the flat single-sheet feel of most free Excel templates.

What you get inside the AI Agent Stocks Dashboard Excel

Above-the-fold summary of every sheet in the workbook. Read this before deciding which file to download.

AI Agent Stocks Dashboard Excel: why a dashboard, not a list

Most investor-facing AI agent coverage looks like a list. Vendor A shipped X feature, Vendor B added Y skill, the slide deck moves on. That is fine for product news, but it is the wrong shape for an allocation decision. An allocation decision is a multi-variable problem: agentic product maturity matters, but so does the price you pay for that maturity, the margin profile of the business behind the product, and the rate at which today's revenue is actually compounding. A list cannot show those four dimensions on one screen. A dashboard can.

The AI Agent Stocks Dashboard Excel is built around three KPI rows and a screener table that is conditional-formatted on five metrics at once. That layout lets you see, in a single glance, that Palantir is the highest agentic AI composite score in the universe but trades on the richest P/S, while CrowdStrike combines a strong agent product (Charlotte AI) with the highest revenue growth among security-software names and a P/S that is high but not extreme. You cannot get that view from a bullet list, and you cannot get it from a generic stock screener that has no concept of "agentic AI maturity" as a column.

The agentic AI shift, and why the market is mispricing parts of it

The reason this dashboard exists at this specific moment is that the public market is not yet pricing agentic AI consistently. Three observations:

Observation 1: revenue attribution is opaque. Salesforce sells Agentforce as a consumption-based SKU on top of its existing seat licenses, ServiceNow sells AI Agents as add-ons inside the Now Assist family, Microsoft ships Copilot as both a per-user seat and a per-message consumption product, and Palantir sells AIP as bundled software plus services. None of these vendors yet break out "agent revenue" as a separate disclosed financial line. That makes attribution from the outside very hard, and it makes the multiple investors are willing to pay highly dependent on management commentary on the earnings call rather than on a reported number.

Observation 2: composite quality varies more than the headline narrative suggests. Every enterprise software vendor announced an "AI agent" product in the last twelve months. The marketing language is identical. The actual differentiation is enormous. Palantir's AIP is a full ontology-grounded decisioning system with deep customer references. ServiceNow's AI Agents leverage the Now Platform's workflow primitives, which gives it a credible distribution advantage. Pega's GenAI Coach is a more incremental layer on top of an existing workflow stack. The market often treats these as similar bets. The agentic AI composite score in this dashboard separates them.

Observation 3: valuations are bifurcated, not uniform. Looking at the screener, the universe splits into two camps. The first is the high-multiple growth camp (Palantir on a P/S above 25, ServiceNow on a P/S above 16, CrowdStrike on a P/S above 22) where the market has already priced in significant agent revenue contribution. The second is the lower-multiple mature camp (Oracle on a P/S around 11, Alphabet on a P/S below 7, Adobe on a P/S around 9) where agent revenue is being delivered but the stock is being treated like a legacy software business. That bifurcation is the entire investment question, and it is what the scenario sheet in this workbook is designed to stress-test.

What's inside the template (10-sheet walkthrough)

Every sheet has a defined job. Here is the tour.

1. Cover

Branded title page. Big agentic-blue title, summer 2026 edition tag, MarketXLS attribution, and a table of contents that lists all ten sheets with a one-line description each. Gridlines are hidden so the cover reads like the front of a designed product rather than a spreadsheet. This is also where the "Data as of" date is anchored on the sample workbook so anyone who opens it knows what snapshot they are looking at.

2. How To Use

Step-by-step tutorial. Seven numbered steps explain how to open the Inputs sheet, review the Dashboard, run the Scenario grid, build the watchlist, size positions, cross-check the Comparison Matrix, and read the Methodology before acting. Below the steps, a full table lists every MarketXLS formula used anywhere in the workbook with a one-line description, so a user new to the add-in can see what is doing what.

3. Dashboard (the headline view)

The dashboard sheet is the one you would screenshot if you wanted to show somebody this workbook in two seconds. It opens with a row of seven KPI tiles: median agentic AI score, average revenue growth, average gross margin, average P/S, count of high-score names, count of agent leaders, and aggregate universe market cap. Each tile has a big bold number, a label above, and a delta line below. Underneath the tiles sits the twelve-name screener: ticker, company, agent product, use case category, price, market cap, P/S, forward P/E, revenue growth, gross margin, agentic AI score, and an auto-computed action column. The action column is color-coded green/amber/red. The numeric columns are conditional-formatted with a red-amber-green color scale so the leaders pop visually without having to sort. Two embedded charts sit below: a bar chart of agentic AI composite score by ticker, and a scatter chart that plots revenue growth on the Y axis against price-to-sales on the X axis. The scatter chart is the single most useful artifact in this dashboard for answering the question "what am I actually paying for".

4. Inputs / Controls

Yellow cells with bold borders, the only cells in the workbook a user should ever edit. The screening rules section sets minimum agentic AI score, max P/S, minimum revenue growth, minimum gross margin, scenario toggle, and bear-case adoption discount. The portfolio settings section sets total portfolio size, max position weight, sizing method, cash reserve, risk tolerance, and investing horizon. Data validation dropdowns enforce valid values for the scenario, sizing, and risk fields, plus a ticker drilldown dropdown that lets you choose a single name to spotlight downstream. Below the inputs sits a scenario presets reference table that documents what each preset actually does.

5. Scenario Analysis

This is the stress-test sheet. The headline grid is an 8 by 7 matrix of blended revenue growth, computed from a transparent arithmetic model: Blended Growth = Baseline 15% + (Adoption Rate x Attach Multiplier x 25%). Rows are agent adoption rates from 5% to 50% of the existing customer base. Columns are attach multipliers from 0.5x to 2.0x of expected per-customer agent spend. Each cell is the implied blended revenue growth and is color-coded red-amber-green so you can read the shape of the sensitivity at a glance. Below the grid sits a reading guide that interprets red, amber, and green zones, and below that, a universe-wide outcome table that shows how many tickers pass the Buy screen under Bear, Base, and Bull scenarios, plus the median P/S that survives the screen in each case.

6. Strategy & Watchlist

The action sheet. Each ticker is listed with its ticker, company name, agent product, agentic AI score, revenue growth, P/S, an auto-classified action label (BUY / HOLD / WATCH / AVOID), and a one-line rationale. The classifier logic is the same as the dashboard but the rationale column is added so you can audit why each name landed where it did. Conditional formatting paints the action column green, amber, red, or gray. This sheet is what you would export as a starting watchlist for your broker.

7. Portfolio Allocation

Position sizing tied to the inputs sheet. A KPI block at the top shows total portfolio size, equity allocation, cash reserve, and number of Buy positions. The position table then sizes each Buy name equal-weighted against the equity allocation (or the alternative sizing method chosen in Inputs) and computes an approximate share count from the current price. A donut chart visualizes the resulting weights. Useful for sanity-checking that no single name ends up an unintended concentration.

8. Comparison Matrix

The ranking view. Ten metrics across all twelve tickers, color-coded with a red-amber-green gradient on every column. AI score, revenue growth, gross margin, operating margin, FCF margin, EPS growth, P/S, forward P/E, and beta sit side by side. Green is "better" in the direction that matters for each column (high for growth and margin, low for valuation multiples and beta). An embedded bar chart of revenue growth by ticker sits below the table. This is the right sheet to use when you are debating two names and want to see which one is leading on the dimensions you actually care about.

9. Methodology

A one-page explainer of how the agentic AI composite score is built, what data feeds the financial fields, how the scenario arithmetic works, how the action classifier thresholds are defined, what the known limitations are, and how often the workbook is updated. Read this before treating the output as a final answer. The composite score is editorial (it is the author's judgment, not a formula), and the methodology sheet says so explicitly.

10. Glossary & Disclaimer

Eleven terms used in the workbook get a plain-language definition (agentic AI, agent platform, attach rate, composite score, forward P/E, gross margin, operating margin, P/S, revenue growth, RSI, use case category). Below the glossary sits the educational-only disclaimer.

The twelve AI agent vendors in scope

The screener universe is intentionally small (twelve names) so each one gets a row in the dashboard rather than a slot in a sortable list. Here is who is in scope and what their flagship agent product is.

A few names are intentionally not in the universe. Pure-play LLM vendors that are private (Anthropic, OpenAI, Mistral) are excluded by definition. Hardware AI plays (NVIDIA, AMD, Broadcom) are tracked in our AI Capex dashboard, not here, because the investment thesis is fundamentally different. Hyperscaler infrastructure plays (Amazon AWS-only thesis) are covered separately. The focus here is software vendors with an agent SKU sold to enterprise buyers.

How the agentic AI composite score is built

The 1 to 10 agentic AI composite score is editorial, not algorithmic. It is the single most opinionated input in the workbook and the methodology sheet says so plainly. The score combines:

1. Product maturity of the vendor's agent platform (does the agent ship today, in production, or is it still on a slide deck for a future quarter)
2. Breadth of available agent skills (how many concrete tasks can the agent actually execute end-to-end in a customer environment)
3. Integration depth with the vendor's existing product suite (does the agent meaningfully leverage the vendor's existing distribution, or is it a parallel product)
4. Monetization commentary from the most recent management call (is the vendor disclosing attach rates, consumption metrics, or customer wins)
5. Customer adoption signals from third-party sources (partner announcements, hiring trends, certification volume, conference attendance)

This is not a substitute for a vendor selection exercise inside an enterprise IT organization. It is a directional ranking for the public-equity investor who has to choose where to allocate without running a multi-month proof of concept. The score will be revised quarterly as disclosure improves and as more vendors publish concrete agent-revenue metrics.

MarketXLS Implementation: the formulas behind the dashboard

The live template version of the AI Agent Stocks Dashboard Excel runs on MarketXLS formulas. The MarketXLS Excel add-in turns every cell into a live pull against licensed fundamentals, options, and pricing data, which means the workbook updates the moment you open it. Below are the exact formulas you will see in the template version. Every formula was verified via the MarketXLS Function Docs before being placed in the workbook (so you will not see invented or hallucinated function names).

Pricing and market cap

=QM_Last("CRM")
=MarketCapitalization("CRM")/1000000000   right-arrow  in $B
=PreviousClose("CRM")
=OpenPrice("CRM")
=Volume("CRM")
=FiftyTwoWeekHigh("CRM")
=FiftyTwoWeekLow("CRM")

Valuation multiples

=PERatio("CRM")
=ForwardPE("CRM")
=PriceToSales("CRM")
=PriceToBook("CRM")
=PriceToCashFlow("CRM")
=EnterpriseValue("CRM")
=PEGRatio("CRM")

Growth and profitability

=RevenueGrowth("CRM")
=GrossMargin("CRM")
=OperatingMargin("CRM")
=ProfitMargin("CRM")
=ReturnOnEquity("CRM")
=ReturnOnAssets("CRM")
=HF_EPS_GROWTH("CRM",,,TRUE)
=HF_EBITDA_MARGIN("CRM",,,TRUE)
=HF_FREE_CASH_FLOW("CRM",,,TRUE)
=HF_REVENUE_GROWTH("CRM",,,TRUE)

Risk and technicals

=Beta("CRM")
=RSI("CRM")
=SimpleMovingAverage("CRM",50)
=SimpleMovingAverage("CRM",200)

Identifiers and classification

=Name("CRM")
=Sector("CRM")
=Industry("CRM")
=ExchangeName("CRM")

The action column itself is a nested IF that references the Inputs sheet thresholds:

=IF(AND(K11>=Inputs!C5, I11>Inputs!C7, G11<Inputs!C6),"BUY",
 IF(AND(K11>=Inputs!C5-2, I11>Inputs!C7/2),"HOLD",
 IF(OR(K11<6, I11<0.05),"AVOID","WATCH")))

That single formula is what turns the workbook from a static screener into a live decision tool. Change the input thresholds in Inputs cell C5, C6, or C7, and every action label across the dashboard, the strategy sheet, and the screener helper updates in real time.

Reading the scenario grid

The scenario sheet is where most of the analytical value lives. The grid is doing one thing: asking what happens to blended revenue growth across the universe if agent monetization comes in slower or faster than expected. The arithmetic is intentionally transparent:

Blended Growth = 0.15 + (Adoption Rate x Attach Multiplier x 0.25)

A few sample reads:

• At 5% adoption and a 0.5x attach multiplier, blended growth is 15.6%. That is basically the existing run-rate. Today's multiples are not cheap on that growth.
• At 20% adoption (a number multiple vendors have implied is achievable inside their installed base) and a 1.0x attach multiplier, blended growth is 20%. That is in line with current consensus for the leaders.
• At 30% adoption and a 1.3x attach multiplier (the bullish read), blended growth jumps to 24.8%. That is the implied growth that supports today's P/S multiples in the high-multiple camp.

The dashboard does not tell you which of these is correct. It tells you which of them you are implicitly betting on when you pay today's price.

How to use the AI Agent Stocks Dashboard for your own thesis

A suggested workflow for the dashboard:

Step one - calibrate the Inputs sheet. Change the minimum agentic AI score, the max P/S, and the minimum revenue growth to match your own thesis. If you are running a quality-growth book, you might tighten these (score >=9, max P/S 15, min growth 20%). If you are running a more eclectic GARP-style book, you might loosen them.

Step two - read the Dashboard sheet's scatter chart. That chart plots revenue growth against P/S for every ticker in the universe. The upper-left quadrant (high growth, low P/S) is where you want most of your screen to land. The lower-right (low growth, high P/S) is where you want to be skeptical. Names floating in the middle deserve a second look at margin trajectory.

Step three - run the Scenario sheet against the names that pass. If your Bull case is 30% adoption and a 1.3x attach multiplier, see how many names still pass your screen at the Base case (20% adoption, 1.0x). If half drop out, you are paying for a Bull case that may or may not arrive.

Step four - size positions in the Portfolio Allocation sheet. Equal-weight is a safe default. Score-weighted (a future enhancement) would tilt toward higher composite scores. Read the methodology before going Conviction-weighted.

Step five - cross-check the Comparison Matrix. Sort visually by color. If a single ticker is green on every dimension, that is suspicious - usually it means a metric is missing or stale. Verify in the source before acting.

Download the AI Agent Stocks Dashboard Excel

Two files. Both free. Both designed to look like a finished product the moment you open them.

Download the templates:

• - Pre-filled with snapshot data as of June 13 2026. Every data cell has a comment that shows the exact MarketXLS formula that would refresh it.
• - Live formulas. Open with the MarketXLS Excel add-in and every value refreshes. Zero static data on the dashboard or screener sheets.

Both files are dashboard-style by design (KPI tile row, embedded charts, conditional formatting, scenario grid, branded cover). If you have been opening generic free Excel screeners and wishing they looked like a Bloomberg terminal, this is the workbook for that.

Frequently Asked Questions

What is an AI agent stock?

An AI agent stock is the public equity of a software vendor whose flagship product line includes an "agentic" AI offering - software that can plan, take multi-step actions, and call external tools on behalf of a user, not just answer chat questions. Examples in this dashboard include Salesforce (Agentforce), ServiceNow (AI Agents), Palantir (AIP), and Microsoft (Copilot + Copilot Studio).

Why use Excel instead of a SaaS screener for AI agent stocks?

A SaaS screener can sort. It cannot let you build a composite score that reflects your own qualitative reading of agent product maturity. An Excel workbook lets you change the input thresholds, the scoring weights, the scenario assumptions, and the position-sizing logic in seconds, and re-run the entire view. Combined with the MarketXLS Excel add-in for live data, you get a fundamentals-driven dashboard that is also fully yours to customize.

Which AI agent stocks are in the dashboard?

Twelve names: Salesforce (CRM), ServiceNow (NOW), Palantir (PLTR), Microsoft (MSFT), Alphabet (GOOGL), Oracle (ORCL), Snowflake (SNOW), Adobe (ADBE), Intuit (INTU), CrowdStrike (CRWD), HubSpot (HUBS), and Pegasystems (PEGA). The universe is intentionally focused on enterprise software vendors with a credible agent SKU sold today.

Is NVIDIA an AI agent stock?

For this dashboard, no. NVIDIA is an AI hardware play, and the investment thesis (GPU demand, data-center capex cycle) is different from the agentic-software thesis covered here. NVIDIA is tracked in our AI Capex tracker dashboard, not this one. The two complement each other.

How is the agentic AI composite score calculated?

The score is editorial, on a 1 to 10 scale. It blends five inputs: product maturity, breadth of available agent skills, integration depth with the vendor's existing suite, monetization commentary from the most recent earnings call, and third-party customer adoption signals. The methodology sheet documents this in detail. The score is reviewed quarterly.

What MarketXLS formulas does the template use?

Twenty-six formulas in total. The most-used ones: =QM_Last for current price, =PriceToSales and =ForwardPE for valuation, =RevenueGrowth and =GrossMargin and =OperatingMargin for fundamentals, =MarketCapitalization for size, =Beta for risk, =RSI and =SimpleMovingAverage for technicals, and =HF_FREE_CASH_FLOW for cash generation. Every formula was verified via the MarketXLS Function Docs before being placed in the workbook.

Can I add my own tickers to the AI Agent Stocks Dashboard?

Yes. The template is unprotected on purpose. Add a row to the screener with a new ticker, copy the formulas down (or paste-special), and the new ticker will inherit all the conditional formatting, the action classifier, and the KPI roll-ups. The agentic AI composite score column expects a manual editorial input from you on the 1 to 10 scale.

How often should the AI Agent Stocks Dashboard be refreshed?

Daily, if you have the MarketXLS Excel add-in connected. The live template version refreshes the moment Excel opens. The static sample version is dated on the Cover sheet. The agentic AI composite score is reviewed quarterly because the underlying product disclosures change at roughly that cadence.

The Bottom Line

Agentic AI is the dominant 2026 software narrative, and the dashboard is the right shape to make sense of it. A list cannot show product maturity, valuation, growth, and margin profile at the same time. A dashboard can. The AI Agent Stocks Dashboard Excel ships with a cover page, a how-to-use sheet, a KPI-tile dashboard, an Inputs sheet, a scenario grid, a strategy and watchlist sheet, a portfolio allocation sheet with a donut chart, a comparison matrix, a methodology page, and a glossary. Every number on the dashboard is conditional-formatted so the leaders pop. Every action label is auto-classified by Inputs-sheet thresholds. Every formula is live, MarketXLS-powered, and Function Docs-verified. The composite score is editorial, the scenario engine is transparent arithmetic, and the workbook is unprotected so you can extend the universe with your own names.

If you build investment research workflows in Excel and you want one screen that captures the agentic AI software thesis end-to-end, this is the template. Download both files above, open them in Excel, and the dashboard will tell you, in a single screen, which names are leading on agentic AI maturity and which ones are still earning the multiple they trade on. To explore the MarketXLS Excel add-in that powers the live template, visit marketxls.com or book a demo to see the full add-in walkthrough.