Build AI agents for every team
in your company, without code.

Agent Studio is a no-code builder for AI agents. Teams in legal, finance, engineering, compliance, customer service, IT, and operations build their own agents, connect them to the systems they already use, and run them in production, within the boundaries IT sets. Hosted by Regentis or on your own infrastructure.

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Agent Studio Sample data

Contract redlineKYC file reviewStatement reconciliationCustomer email repliesTicket triageRFP first draft Published v4

The platform

Four products on one platform. Agent Studio builds the agents. AI Impact, IndEx, and Financial Intelligence measure what AI and engineering return to the business.

01 Agent Studio Build, publish, and run AI agents for any team in the company, under one set of controls.
02 AI Impact Where the AI spend goes, what it changed in delivery, and how sure the answer is.
03 IndEx Follow the experience of your teams, category by category.
04 Financial Intelligence The cost of engineering by team and by type of work, with tool spend and CapEx from the same data.

01Agent Studio

Build an agent in an afternoon. Run it under the controls IT already owns.

An agent in Agent Studio has four parts: a trigger, the inputs it may read, a model or tool step, and an output. A team assembles them on the canvas, starting from a template or from nothing, and publishes.

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A trigger can be an incoming email, a schedule, a mention in chat, an API call, or an event in another system. An output can be a reply, a comment on a ticket, an email, a channel post, a callback, or a file. Each publish is a version. Each run is recorded, so the team can see what the agent did and change it.

IT decides which systems agents may connect to, who may publish, and where the data stays. The team decides what the agent does within those limits.

What teams build with Agent Studio

Legal

Contract redline

Marks up an incoming contract against your clause playbook and explains each change.
Risk & compliance

KYC file review

Screens a new client file against policy and watchlists, and writes up what needs a second look.
Finance

Statement reconciliation

Matches bank statements to the ledger at month end and lists the breaks with a likely cause.
Customer service

Customer email replies

Answers customer email using your published policies and the order record, and cites both.
IT service

Ticket triage

Classifies a request, resolves the known ones from the runbook, and routes the rest with context.
Operations

RFP first draft

Fills in an RFP questionnaire from past proposals and account notes, and marks the answers it is unsure of.

Start from a template

Templates cover common jobs such as ticket triage, contract review, and reconciliation. A blank canvas is there for everything else.
Connect the systems you already use

Agents start from email, a schedule, a chat mention, an API call, or an event in another system. They read from your tools and documents and send results by email, chat, ticket comment, callback, or file.
Set the boundaries once

IT decides which systems agents may connect to, which teams may build and publish, and where data is stored. Every agent in the workspace works within those limits.
Keep every run on record

Each publish is a version. Each run records what the agent received, what it did, and what it sent. The team can trace a result, correct a mistake, and return to an earlier version.

02AI Impact

Where the AI spend goes, and whether delivery holds up under it.

AI Impact answers the six questions an engineering leader asks about AI: are we using what we pay for, are we shipping more, did quality and flow hold, are we building more of the right things, is it worth the money, and is AI the reason. Each answer comes from your own delivery data and says how sure it is.

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AI use is only a gate. It earns no points. Below it, the page shows no number. It says “Not attributable”. Above it, three stages are scored with equal weights: Output, from throughput, deployment frequency and lead time; Integrity, from change failure rate, recovery time, pull request size, review turnaround, time to first review, build time and the developer experience index; and Return, from the share of effort going to new capabilities and the cost of a new-capability hour, both against a baseline. The score can never sit more than ten points above Integrity, so more output cannot carry a team past failing quality or experience.

Every answer is marked Measured, Estimated or Not attributable. Agent-involved pull requests are shown next to the rest. The page calls this an association and says it does not prove cause. AI spend is shown as context: in total, per developer, and per new-capability hour. Regentis does not claim a dollar return on AI without a control group.

AI Impact / Platform group, Q2 2026 Sample data

Gate passed on git evidence 43% of merged PRs agent-involved

AI Impact score

71 / 100

Healthy

Capped at Integrity 61 + 10. More output cannot carry a team past failing quality or experience.

OutputEstimated 1.9 PRs per engineer a week · +27% since connecting
84 Top tier
IntegrityMeasured Lowest: PR size 30 · review turnaround 45
61 Emerging
ReturnEstimated New-capability share +2 pts · cost per hour −6%
74 Healthy

AI spend, this quarter: $55.9K
Per developer: $1,398

Each answer says how sure it is

Every figure is marked Measured, Estimated or Not attributable, and the page says why. Nothing unmeasured is ever shown as zero.
How the score is built

AI use opens the gate and earns no points. Output, Integrity and Return are scored with equal weights, and the score can never be more than ten points above Integrity.
A baseline from before your AI rollout

You give the date your AI rollout started. Regentis checks whether repository history reaches thirteen weeks before it, and if it does not, says so and labels every comparison as change since connecting.
The team is the smallest unit

Nothing is scored per person. Throughput carries no targets, and it is always shown next to the quality and experience numbers that balance it.

03IndEx

A running read on how your teams are doing.

A yearly survey tells you how a team felt a few months ago. IndEx, short for Individual Experience, shows how your people are doing now.

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It shows where experience differs from team to team, so leaders hear about trouble from the data before they hear about it in a resignation.

IndEx / panorama Sample data

Overall experience: 78 / 100
Participation: 74 %

Team	Onboarding	Tooling	Reviews	CI/CD	Autonomy
Platform	88	91	79	76	92
Payments	81	72	58	44	69
Mobile	90	84	82	71	88
Data	77	80	74	68	85

Short surveys on a rolling schedule

A few questions at a time, on a schedule you set. A yearly snapshot is stale by the time anyone reads it.
Specific enough to act

Each driver of developer experience, from onboarding to CI/CD, scored by team and set against a global benchmark.

04Financial Intelligence

See what engineering costs and which of it can be capitalized.

Engineering is often the largest line in the budget and the hardest one to explain. Financial Intelligence puts it in financial terms.

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Each contributor carries a cost rate, set by integration, entered by hand, or uploaded as a CSV, and each hour of work is classified from your issue tracker and repositories. From that you see what engineering costs in total and by team, how much of it goes to new capabilities, keeping the lights on, quality, and internal productivity, and what a merged pull request, a deployment, or a resolved issue costs.

Every tool your engineers use is listed with its cost, its seats paid for and in use, and its renewal date. The CapEx / OpEx view classifies the same work for capitalization, holds anything the classifier is unsure of for review, and locks each month once it is settled. Report Center produces the board pack and the CapEx, OpEx, and tool spend reports as PDF and XLSX.

Financial Intelligence / overview, 2026 Sample data

Total cost of engineering: $4.82M; People $4.51M · Tools $312K
Tool & platform cost: $312K; 6% of total · 23 tools
Capitalized (CapEx): $1.29M; 29% of people cost · 8 projects authorized

Investment	Cost	Share
New capabilities	$2,164,000	48%
KTLO guide ≤15%	$992,000	22%
Quality improvements	$947,000	21%
Internal productivity	$406,000	9%
People cost	$4,509,000	100%

Cost by type of work

People cost is divided across new capabilities, keeping the lights on, quality, and internal productivity, by team and by month. KTLO has a guide you set, and when a team passes it the product says so and names the team.
Cost per unit of work

Cost per active engineer, per merged pull request, per deployment, and per resolved issue, together with the share of people cost that went to defects and rework. When output changes, the cost shows it.
Each tool, with its seats and renewal date

Each tool your engineers use, from your integrations or entered by hand, with its monthly cost, seats paid for and seats in use, metered usage, and a renewal calendar that shows the date by which you have to decide.
CapEx and OpEx from the work itself

Capitalizable work is identified from the projects you authorize and the work already recorded in your issue tracker and repositories. There are no timesheets. Items below a confidence threshold you set wait in a review queue. A month is locked once it is reviewed, and the CapEx report shows how every dollar was classified.

Deployment

Your infrastructure or ours.

Some organizations can put engineering data in a vendor’s cloud. Some cannot. Regentis is built for both, and all four products are available either way.

SaaS

Regentis Cloud

We host and operate the platform. There is nothing to install or patch, and your teams sign in from the browser.
On-premises

Regentis Self-hosted

You run the platform inside your own environment, behind your own network controls. Your data stays where your policies say it must.

Bring a real task.
We’ll build the agent on the call.

A demo takes thirty minutes. You leave with a working agent and a clear view of how the other three products would read your organization.

Book a demo Contact us

Build AI agents for every team in your company, without code.

Four products on one platform. Agent Studio builds the agents. AI Impact, IndEx, and Financial Intelligence measure what AI and engineering return to the business.

Build an agent in an afternoon. Run it under the controls IT already owns.

Contract redline

KYC file review

Statement reconciliation

Customer email replies

Ticket triage

RFP first draft

Start from a template

Connect the systems you already use

Set the boundaries once

Keep every run on record

Where the AI spend goes, and whether delivery holds up under it.

Each answer says how sure it is

How the score is built

A baseline from before your AI rollout

The team is the smallest unit

A running read on how your teams are doing.

Short surveys on a rolling schedule

Specific enough to act

See what engineering costs and which of it can be capitalized.

Cost by type of work

Cost per unit of work

Each tool, with its seats and renewal date

CapEx and OpEx from the work itself

Your infrastructure or ours.

Regentis Cloud

Regentis Self-hosted

Bring a real task. We’ll build the agent on the call.

Build AI agents for every team
in your company, without code.

Bring a real task.
We’ll build the agent on the call.