E6DATA AGENTFLOW

AI agents for the work you can't afford to get wrong.

Ask what changed and get the answer with its proof. Let it watch the metrics you answer for and come to you early with the fix. Or hand it the whole process: it runs the same way every time and stops before it publishes a number it can't prove.

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Used by the teams who own the numbers: finance, operations, supply chain, customer success. Governed by AI engineering, who keep control of the models, the tools and where it runs.

WHAT IT DOES

Start by asking. Then stop having to.

It begins with a question answered correctly, with proof. From there it goes two ways: AgentFlow can watch your numbers and come to you, and it can run the work itself. Both stand on answers you have already learned to trust.

you ask · on demand

Ask

Chat with your data. Correct answers, with proof, and a plain "I don't know" when it isn't sure.

it comes to you · always on

Anticipate

You don't have to ask. It catches problems and openings early and tells you what to do while there is still time.

you assign · on schedule or event

Automate

The workflows that can't go wrong, run the same way every time, with the proof on the record.

ASK

Ask what changed. Find out why.

Ask in plain English. AgentFlow finds the right measure in your semantic layer, runs the query on your data, and walks the metric down through everything that drives it until it reaches the cause.

Every answer carries its proof.

PROJECT · COMMERCE MARGINsemantic layer bound

What changed in order margin this week?

Order margin is 23.4%, down 2.1 points. Freight explains 1.6 of them: expedited shipments in the Northeast tripled after the carrier change on June 1. Deeper promo discounts on two SKUs explain the rest.

DRIVERS · CHANGE IN MARGIN

fulfillment cost−1.6 pts

net revenue per order−0.5 pts

cost of goods0.0

returns0.0

measures: order_margin, fulfillment_cost_per_order · tables: orders, shipments, carrier_invoices · fresh 14 min · confidence 0.93

Which lanes?Show it by dayWhat would rerouting save?

The sources, how fresh they are, and how confident it is. When it can't answer honestly, it declines and tells you why rather than filling the gap with a guess.

The answer, the cause, and the proof: which tables, how fresh, how sure.illustrative

It declines out loud.

And margin by sales rep?

I can't answer that one honestly. Margin isn't defined by sales rep in your semantic layer, and I won't guess at the join. The closest governed cut is by region. Want that?

MEASURE_NOT_DEFINED declined, with the reason

A model left alone will always produce something. This one stops when the measure is not defined, when the data is thin, or when a question has two honest readings, and it gives the reason as a code your team can act on.

Reason codes and thresholds are yours to set per project.illustrative

ANTICIPATE

It watches the numbers you answer for.

It works around the clock and knows what drives each number and what each number drives. So it sees trouble while it is still small, and openings while they are still open. Then it comes to you early with what is happening, why, what it is worth, and what to do about it.

Tuesday night, not Friday afternoon.

AGENTFLOW · TUESDAY 02:14 · A PROBLEMwatching order margin

Order margin is slipping. Down 0.6 points since Monday, and on pace for 2.1 by Friday.

Why

Expedited freight in the Northeast is running at 3.1 times normal. Since the carrier change on June 1, ground shipping misses the two-day promise, so orders are being upgraded automatically.

Cost

About $212K of margin by Friday if nothing changes.

Fix

Ship Northeast orders from Newark, which restores two-day ground for 84% of them. Pause automatic upgrades on orders under $75.

Who

Routing belongs to the VP of logistics. Both moves are inside the logistics playbook.

Approve the rerouteSee the evidenceNot now

Sources: orders, shipments, carrier_invoices · fresh 9 min · confidence 0.91 · run 8f3a2c

Ask on Friday and you learn the week was lost. Anticipate told you on Tuesday night, with the cause and the fix attached, while the week could still be saved.

What, why, what it is worth, what to do, and who decides.illustrative

AUTOMATE

Automate the work that can't go wrong.

Run, manage and improve the workflows that matter most. The same path every time, checked at every step, with the cost, the speed and the proof on the record.

Fourteen steps across eight systems, with nobody awake.

RUN 7C41E2 · OUT-OF-SPEC-BATCH · V6completed 03:18

02:40:12

MES event. Batch B-24817 on line 3 fails viscosity at final QC.

02:40:1301
Pull the batch recordMESruns as code
02:40:1502
Pull the lab resultsLIMScheck
02:40:1603
Compare to spec and the last 30 runsWarehouseruns as code
02:40:1904
Check the equipmentCMMScheck
02:40:2105
Trace the raw material lotsERPruns as code
02:40:2406
Find every batch that used itERP + MESfans out
02:40:4107
Draft the root causemodelcalls a model
02:40:5208
Propose containmentplaybookcalls a model
02:40:5309
Ask the quality lead to approveSlacka person decides
03:02:1010
Place the holdsMES + WMSchanges something
03:02:1211
Ask the plant managerSlacka person decides
03:14:3012
Update the scheduleERPchanges something
03:14:3313
Open the supplier corrective actionQMS + emailchanges something
03:18:0214
File the deviation reportQMS + Slackruns as code

runs as codecheckcalls a modela person decideschanges somethingfans out

This run starts at 2:40 a.m., when a batch fails its spec. It reads from five systems, writes back to four, puts eleven agents to work at once, and stops twice to ask a person. Four minutes of work and thirty-four minutes of waiting for people. Your quality lead wrote it in plain English, with no code and no engineering ticket.

Event to containment in 38 minutesillustrative run; the use case it is drawn from is on the Use cases page

WHAT IT KNOWS

It knows more than the numbers.

A number moving is a fact. Knowing why it moved, what it is attached to, and what you are allowed to do about it is judgment. All three capabilities draw on the same three layers, and you govern every one of them.

SEMANTICS

What the numbers mean.

Your metrics, defined once and governed: measures, dimensions, and how each metric rolls up into the next. Order margin is made of revenue, cost of goods, freight and returns, and each of those is made of something too. That tree is how AgentFlow knows where to look.

ONTOLOGY

How the business fits together.

The things your company is made of and how they connect. Orders ship on carriers from distribution centers. SKUs come from suppliers and run on lines. Accounts have contracts, seats and owners. It is how a number gets traced to a thing someone can fix, and to the person who owns it.

CONTEXT

What is going on, and what the rules are.

The contract that renewed on June 1. The promotion that ends Friday. The margin floor, the approval policy, the playbook for a late supplier, and what happened the last time this came up. It is how AgentFlow knows what matters and what you are allowed to do about it.

Semantics find the change. Ontology finds the cause. Context finds the fix.

WHY YOU CAN TRUST IT

It would rather stop than guess.

The more it does without you, the more you have to trust it. Any agent can produce an answer. The one you can trust is the one that will refuse.

The wrong step is unreachable.

The order of the steps is fixed before the run starts, and each step can use only the tools it was given. No prompt has to forbid the wrong move, because it is not in the toolset.

Every step checks its own work.

A step has to pass its checks before the next one begins: rows present, data fresh, totals that reconcile. Fail twice and the run stops and says why.

A person signs first.

Anticipate recommends. A named person approves. When a step changes something in the real world, the plan is frozen first, so what runs is exactly what was signed.

It keeps the receipts.

Every step, query, tool call, approval, token and cent is written to a ledger in your own storage. Three months from now you can see what ran and who signed off.

COST

Careful turns out to be cheap.

Same workflow, same model, same answer to the last decimal. AgentFlow calls the model where judgment is needed and nowhere else. The rest runs as code, which is faster, costs almost nothing, and can't change its mind.

380model tokens per runAgentFlow, supplier-risk workflow

22xfewer tokens than the best-tuned AWS build8,289 tokens, Step Functions

90xfaster than a general agent loop14.8s against 22.4 min

$0.01a run, from the receipt$0.07 to $2.21 on the other eight

Nine builds of one supplier-risk workflow, same model and data, identical computed output. Multipliers are derived from each platform's own metering and use each vendor's best-tuned build. AgentFlow figures are provisional until the final receipt is attached. Method and receipts: the Benchmark page.

USE CASES

Built for the teams who own the numbers.

Two numbers that can't be left unwatched, and two processes that have to be right every time.

ANTICIPATE · REVENUE

Churn, caught early

AgentFlow watches net revenue retention and everything under it: seat use, active users, support backlog, release adoption. When a driver slips, it names the accounts behind it, explains why, sizes the renewals at risk, and drafts the fix.

At-risk accounts flagged five weeks earlier, on averagea B2B software company

ANTICIPATE · SUPPLY CHAIN

On-time in-full, protected

It watches on-time in-full and its drivers across suppliers, lines, inventory and carriers. When a supplier's lead times stretch, it finds the SKUs and lines exposed, checks who else is qualified, and proposes the switch before the month is lost.

$1.3M in chargebacks avoided in two quartersa consumer packaged goods maker

AUTOMATE · QUALITY

The out-of-spec batch

A batch fails its spec and the MES fires an event. AgentFlow pulls the batch record, the lab results and the maintenance history, traces the raw material lots, finds every batch that shares them, drafts the root cause and proposes containment. A named person approves. Only then does it place the holds.

Event to containment: a morning down to 38 minutesa specialty chemicals producer

AUTOMATE · FINANCE

The close, explained

Two days after close, AgentFlow finds every account that moved more than the threshold, explains each one from the journals, and cites the document IDs. Nothing is published unless the documented drivers account for 90% of the movement.

Flux review: five days down to one morninga $3B industrial distributor

Customer results as reported by the customers, who are described rather than named. Conditions on request.

WHY NOT USE WHAT WE HAVE

The clouds give you parts. This is the product.

AWS, Google Cloud, Azure and Databricks sell strong building blocks. Turning them into a governed workflow is an engineering project. AgentFlow is the finished thing, and it runs on any of them.

Question	AgentFlow	Built on a cloud platform
Who writes the workflow	The team that owns the process, in plain English	Engineers, in Python or notebooks
What keeps the agent in bounds	A compiler. The wrong step cannot be reached.	A prompt that asks it not to
Approvals and audit trail	Built in	Yours to assemble from four or five services
Where it runs	AWS, Google Cloud, Azure, OCI, sovereign clouds, on-prem	Their cloud
First workflow in production	1 to 3 days	Days to weeks

FITS WHAT YOU RUN

Your data. Your storage. Your model. Your cloud.

Nothing moves and nothing gets replaced. AgentFlow sits on top of what you already chose.

Your data

Queries run against your tables where they already sit, through the semantic layer you already govern. One definition of revenue, for people and agents alike.

Your storage

Workflows, memory, run history and the audit log live in your own object storage, versioned. They are your assets, not ours.

Your model

Bedrock, Claude, OpenAI, Gemini, Azure OpenAI, or one you host yourself. Chosen per step, so the hard steps get the big model and the rest don't.

Your cloud

Our cloud over a private link, your own cloud account, inside your VPC, or air-gapped on-prem. The architecture is the same in all four.

MODELS

AWS BedrockAnthropic ClaudeOpenAIGoogle GeminiAzure OpenAIself-hosted

STORAGE

S3GCSAzure BlobMinIO

DATA

IcebergDelta LakeParquetCube

TOOLS AND DELIVERY

MCP serversSlackemailERPMESCMMS

THE DEMO

See it work.

Four minutes, no slides. A question about the close, the answer with its evidence, and the same conversation turned into a monthly workflow with a gate added in one sentence.

[NEED: demo video embed]

0:00
Ask a question about the close.
1:10
Turn the answer into a monthly workflow.
2:05
Add a gate in one sentence.
3:00
Open the ledger and read the run.

WORKING WITH US

We send an engineer. You pay for the result.

One of our engineers joins your team, builds the first workflows with the people who own the process, and stays to run them and make them better. The contract is tied to a number we agree before day one.

Before day oneAgree on the number
One call with the person who owns the process and the executive who sponsors it. One process, one owner, and one written definition of success.
Days 1 to 3Build and deploy
Our engineer connects your data and your semantic layer, then builds the first workflow with your team steering. Real data, real checks, in production.
From the first runRun and monitor
Our engineer watches every run with your team. Once a week you get one page, with the number we agreed at the top.
Every week afterImprove
Checks get tightened. Costs come down. The next process joins the first. By the tenth workflow, your team is writing them in hours.

Qualified use cases with an executive sponsor start with $50,000 in POC credits. After that you pay for the result we agreed, not for seats or hours. Terms: the Working with us page.

Six questions

How is this different from an agent that picks its own tools?

An ordinary agent is kept in bounds by its prompt, and prompts get ignored. In AgentFlow a compiler fixes the path before the run starts. The wrong tool is not in the step's toolset, and the wrong step cannot be reached. For regulated and audited work, that difference is the whole point.

Does Anticipate act on its own?

Only where you have said it can. By default it watches, diagnoses and recommends, and a named person approves. When a step changes something in the real world, the plan is frozen with a fingerprint of its inputs before it goes for approval, so what runs is exactly what was reviewed.

What happens when a step fails?

The run stops at that step and says which one and why. It does not carry on with half an answer. Steps carry checks: the shape of the output, a file that must exist, a total that must reconcile. They also get a retry, prompted with exactly what was missed.

Where do the metric trees and the business context come from?

The metrics come from the semantic layer you already govern, so there is one definition of margin for people and agents alike. Playbooks are packaged as skills. Facts are kept in project memory and carried from run to run. All of it lives in your own storage, versioned.

If something goes wrong three months from now, can we reconstruct what happened?

Yes. Every run is written to an immutable ledger that is part of the product. Look up a run and you get each step, the SQL it ran, the tools it called, what it cost, the outcome of every check, and who approved what.

Are we locked into one model or one cloud?

No. Route to Bedrock, Anthropic, OpenAI, Gemini, Azure OpenAI or a model you host, and choose per step. AgentFlow runs on AWS, Google Cloud, Azure, OCI, sovereign clouds and on-prem, with the same architecture on all of them.

TALK TO US

Bring us the process you worry about most.

We will show you how it would run on AgentFlow, what it would check, and what each run would cost.

See if it qualifies for POC credits