PIES Studio 1.9 · User Manual
From an empty workspace to a deployed application: creating an app, building with PIES AI, configuring it by hand, previewing a real build, deploying, and managing what is live.
PIES Studio is a browser IDE: you describe or draw an application, and it generates a working front end, back end and database. Everything below was captured from a running Studio, so what you see here is what the product does today — not a mock-up.
Open Studio and sign in with your PIES account. Enterprise installs can also use Sign in with Microsoft when SSO is configured.
After signing in you land on your applications. Applications are grouped
into workspaces — use one per team or per client. Since 1.9
the workspaces are a pill bar across the top of this page, each pill counting
its applications, with the overflow behind a searchable +N more. This
is where you come back to open, duplicate or delete an app, and each card says
how many agents the application carries.
The sidebar beside it is grouped the way the work is:
Build (Applications, Themes, Library),
Agents (Agents, Playbooks, Activity),
Operate (Deployments, Previews, Tasks) and
Organisation (Decisions, Teams, SSO, Integrations, Admin),
with PIES AI and Help at the foot.
The same editor runs whether or not it is connected to anything. What changes is what it can reach — and that is the difference between the plans.
The local IDE is the client, not an edition of the product: it ships in every plan. What differs is what it connects to — nothing at all, a PIES-operated server, or a server your organisation runs.
Everything that makes an application: creating it, designing screens,
authoring events and functions, the data model, and building and previewing it
locally. The IDE carries its own stack — database, back end, build toolchain and
their caches — so a machine that has never been online can create an application,
build it and run it.
AI works offline too, against a model on your own machine or in your own network.
Nothing you type and nothing your application contains leaves the device.
Features that are inherently about somewhere else are unavailable offline
and shown with a lock: hosting and deployment, boards
and team collaboration, mobile publishing, choosing a different
language or stack, cloud storage and backup, and the full
AI governance surface — policies, audit and knowledge bases.
Offline, the AI settings remain so you can point the editor at your own model.
None of this changes the application you have built. Connecting later unlocks
those features against work you already did offline.
An Enterprise install puts the server inside your network: your database, your identity provider, your audit trail. Paired with PIES Private AI the whole system — editor, platform and model — runs with no external dependency at all, which is what an air-gapped environment requires. See the installation guide for the platform and the PIES Private AI manual for the model.
An application is the whole deliverable: data model, screens, business logic, generated code and its deployments.
Click New Application, give it a name and pick a workspace (you can type a new workspace name to create one). Names take letters, digits and spaces — hyphens are rejected. From Template starts from a published template instead of an empty app.
The app opens in the editor. A narrow icon rail on the far left switches between Apps, the Editor, Source, Deploy, Previews, Boards and Settings. Beside it is the explorer — every object in your application, in three collapsible tiers. The centre is the canvas for whatever you have open, with a menu bar (File, Edit, View, Help), the branch selector, a search box, and ▶ Run at the top right. The right-hand panel toggles between the Inspector (properties for whatever is selected) and PIES AI; the build console opens along the bottom.
Read this chapter once and the rest of the product stops being surprising. PIES has a small number of object types and one strict rule about which does what — the same rule the AI is held to, and the same one the build validates.
Screens hold widgets (a table, a form, a
button, a chart). A widget raises an event — "this button was
clicked". The event calls a function, and the function is built
from steps: read a record, validate it, write it, return a
result. The event then reacts to that result — show a message, navigate,
refresh a table.
The division is deliberate, and the build enforces it:
Since 1.9 the explorer groups everything an application contains into three collapsible tiers, and only Build is open when the editor loads. Each carries a one-line summary of what is inside it, so the thing you are looking for is one click away rather than somewhere in a long tree:
Under Build: Database holds tables — under
their schema, internal for the built-in one — and
Relationships. Screens holds the pages,
including Navigation → Menu & Header and the four
Authentication screens. Processes are
long-running business workflows rather than single actions.
Custom widgets are your own components, and
Deliveries is the board of work agents have been asked to do.
Under Configure: Users & access defines
access groups and how people sign in, API Connections exposes
functions as REST APIs, and Secrets holds email servers and
secrets. Environments (where the app runs) have their own
sidebar item, outside the editor. Under
Assets: Artefacts are documents (a BRS, a
spec) that PIES AI reads before it builds, Media holds the
images the application ships with, Document templates are the
PDF layouts, and Repositories connects generated code to source
control.
Screens, functions and events can be grouped into folders in the explorer, and a folder can hold another. The grouping is yours — by feature, by area of the business, by whatever makes the application readable a year from now. Creating a screen inside a folder puts it there directly; dragging moves it later. Folders are presentation only: nothing about the application changes when you reorganise them.
Every application has an internal database and can additionally connect to databases you already run. You never write DDL — the schema is generated from what you define here, for every environment.
Open a table — under Build → Database, inside its schema (internal is the built-in database) — to edit its columns: name, type, and constraints such as primary key, auto-increment, required or unique. PIES maintains the key and audit columns it needs, so a table you create is immediately usable by screens and functions. Seed rows can be attached to a table so previews render against realistic data.
Relationships shows the entity diagram: every table and the links between them. To link two tables, drag from a column's port on one table to the column it refers to on the other, then press Cmd+S (Ctrl+S on Windows) to save — the diagram has no Save button. A link is a foreign key from one column to another; for a many-to-many relationship, add the join table yourself and link it to both sides. Linked data is then available to widgets: a dropdown bound to the parent, a table showing the child rows. The diagram is the fastest way to see whether a model — yours or one the AI generated — is actually normalised.
An application is not limited to its internal database: add a source to work against one you already run — MySQL, PostgreSQL, Snowflake and others, plus Zetaris for read-only analytics. Credentials go to the secret store, never into the application definition. Connected tables appear alongside the internal ones and can feed screens, functions and charts.
A screen is a canvas. You place widgets on it, bind them to data, and wire their events. What you place is what renders — the canvas resolves widths against the same drawable area the generated application uses.
The palette groups widgets by kind: Input (an Input Field, set to text, number, date, dropdown and the other input types once placed), Data (Table, Form, List, Tab Group), Visualization (Count Card, bar, line, pie and the ECharts family), Visual (Container, Text, Icon, Image, Video, Link) and Shapes (including Line). Drag one onto the canvas to place it.
Select a widget and the right panel configures it: which table or query feeds it, which columns show, validation, default values, styling, and its events. A data table takes its columns from the bound table; a chart takes a measure and a dimension. Containers group widgets and lay them out in flex or grid, so a screen keeps its shape at different widths.
When nothing in the palette fits — a signature pad, a Kanban board, a third-party SDK — build a custom widget in the Widget Designer and it appears in the palette like any other. Custom widgets targeting a screen or event are React and run in the browser; those targeting a function are Go and run on the server. This is what stops a no-code application hitting a ceiling: you drop to code for one component, and everything around it stays generated.
This is where an application stops being a set of screens and starts doing something. Events and functions are built the same way — a flow of steps — but they are allowed to do different things.
An event is triggered by a widget: a button click, a row selection, a screen load. Its steps call a function and then branch on the result — show a success message, show the error, navigate to another screen, refresh a table. An event must not touch the database directly; it asks a function to.
A function takes parameters, does the work, and returns data. It is drawn as a flow from one start to one end. A step that is not linked into the flow never runs: the Inspector lists it under Disconnected steps, and the build reports it as a warning in Problems → Functions, naming the function and the step. Inside, steps read and write records, call other functions, transform values, branch on conditions, loop, call REST APIs, send email.
Add a step from the catalogue and configure it: which data source it acts on, where its input comes from (a parameter, an earlier step's output, a literal), and where its output goes. Steps are chained by linking them, and a later step reads an earlier one's result by referencing it. The groups are Custom Widgets, Call, Control (if, loop, end), Error Handling, Concurrency, Record (insert, update, delete, retrieve), Comms (email and notifications), AI, Document, Retrieve (files) and Scheduler — plus Return Data, which hands a result back to whatever called the function.
A process models work that spans time and people — approvals, onboarding, case handling — rather than a single action. It is a workflow of tasks and gateways, and the engine tracks each running instance. Use a function for "save this record"; use a process for "get this contract approved".
Applications produce paperwork — agreements, invoices, reports. In PIES the paperwork is designed once as a document template, and the running application fills it with live data, renders it as a PDF on the server, and either downloads it in the browser or emails it as an attachment. The text stays crisp and selectable — these are real PDFs, not screenshots of a page.
Each application carries its own document templates. They live in the explorer under Assets → Document templates. The + button creates one; clicking a template opens it as an editor tab. Templates are part of the application, so they version, export and deploy with it.
The template editor has three panes. On the left, the document's frame:
page size and orientation, a header (logo, title, subtitle) that repeats on
every page, a footer with optional page numbers, and the base font and accent
colour. In the middle, the content as an ordered list of sections —
headings, paragraphs, key-value blocks, tables, images, signature lines,
dividers and page breaks — each edited with a small form and reordered by
dragging. On the right, a live preview of the page as you edit.
Anywhere you can type text you can also write a $token —
$customer.legal_name, $order.total_monthly. Tokens are
left as-is in the design and filled with real values when the document is
generated. A table section binds its rows the same way: its
data variable names a list (for example $items), and each
column names a field inside the rows, so a forty-line equipment schedule
paginates by itself.
A function turns the template into a file with the
Generate Document step (in the Document Actions
group of the step catalogue). It takes the template and a file name — the name
accepts tokens too, so lease_$customer_id.pdf names each customer's
document after them. The step's output is the generated file, ready for the next
step. The $tokens inside the template are filled automatically from
whatever the function has in scope: its parameters and the outputs of earlier
steps, dotted paths included — retrieve a customer record into
customer_row and $customer_row.legal_name resolves on
its own. The older Generate PDF step still exists for plain
title-and-text output; designed documents use Generate Document.
Two steps finish the job, and they compose with everything else a function can do:
The AI knows this whole chapter. Ask for the outcome — "create a rental invoice template and add a button on the Orders screen that emails it to the customer" — and it designs the template, builds the function with the right steps, wires the button, and reports what it made. The template it creates appears under Assets → Document templates like any hand-made one, so you can restyle it in the editor afterwards.
PIES AI builds inside your application using the same tools the IDE uses, so everything it produces is a normal PIES object you can open and edit by hand.
Open the PIES AI panel and describe it. Be specific about the shape of the thing — "add a screen that lists books with a search box" beats "make it better". Each request runs against the application as it stands, so you build an application up in steps.
The panel streams each tool call as it happens. How far the AI may go on its own is set by the mode selector under the message box:
When it finishes you get a receipt: exactly what was created or updated, which model built it, and how many tokens the turn used. Everything appears in the explorer and is yours to edit. Rewind undoes a build you did not want.
Loops — the PIE Loop, on its own tab beside Chat in the PIES AI panel — takes a larger request, decomposes it into units of work, builds them, then reads the build errors and fixes them, repeating until the application compiles. Use it for "build the whole thing"; use Chat for single changes.
Name the entities and how they relate, not just the theme. “A clinic
booking system” leaves every decision open; “patients, doctors and appointments,
where an appointment belongs to one patient and one doctor” produces the data
model you meant. The same applies to screens: describe what the page is for and
what it shows, and say which table it reads.
Ask for one thing at a time. A request that names a schema, four screens and a
report is planned as one large job; the same work asked for in three turns is
easier to steer, and each turn can be rewound on its own.
A large request is planned before it is built. The plan lists the units of work — tables, then functions, then screens. In Edit automatically the build carries straight on; to read the plan first, use Plan mode, which writes the plan and changes nothing, then switch back and send again. Correcting a plan costs a sentence; correcting a built application costs a rewind. If a very large build pauses to stay within the time limit, Edit automatically continues on its own and the other modes show a Continue button.
Each turn ends with a receipt: what was created or updated, counted from
the application itself rather than from the model's description of its own work.
Where the two disagree, the receipt is right. A turn that reports
“0 screens added” did not build a screen, whatever the surrounding text says.
The receipt also names the model that did the work, which matters when an
organisation routes some requests to a model inside its own network and others
to a cloud provider.
Every turn takes a version snapshot before it changes anything, so a turn that went the wrong way costs a click rather than an afternoon. Rewind returns the application to the state before that turn — and closes anything it removed that you had open. Rewinding does not undo work you did by hand afterwards, so rewind before you continue building on a bad turn, not after.
The AI works through the same tools you have — it cannot reach outside the application, and it cannot write code the platform would refuse from you. Where a request needs a component the platform has no widget for, it writes one (see “Views PIES has no widget for”). Where a request is ambiguous about data — which table, which column — it will ask or choose, and choosing is where it most often chooses differently from what you meant. Naming the table removes the guess.
Agent work is work the platform does for you rather than with you. There are three kinds of it — a delivery changes an application, an errand does one thing to a running one, a post stands watch on an environment — one door into all three, and one feed where every one of them reports. Three entries on the rail, under Agents, are the whole feature: Agents (the posts), Playbooks (how deliveries get built) and Activity (what needs you). Inside the product, How agents work opens a short guide covering the same ground as this chapter. For every screen, field by field and with pictures, see the Agentic AI manual.
Two questions decide what a piece of work is: does it end, and does it
change the application's design or act on a running one. Answer both and you get
three kinds, and keeping them apart is what makes the rest of this chapter make
sense.
A delivery is build work: a brief against one application and
one branch, worked on that application's own Tasks board, producing application
a baseline taken at start that it can be rolled back to. It ends when its
playbook's steps have run; the Verify step closes each card.
An errand is one action against a running application —
reconcile yesterday's failed payments, investigate one alarming site. No board,
no version; it ends with an outcome.
A post is a standing assignment on an environment: a monitor, a
triage agent, an app user, or a watch that opens deliveries when it finds work.
It runs until you fire it, and it is the only one of the three that costs an
agent seat.
Underneath all three is the same thing — a run. A delivery
produces runs for its steps, an errand is a single run, a post produces a run per
trigger. That is why one feed can honestly show every kind of work.
Three kinds of work need one way in, or people learn three.
Start work opens from the Activity header, an
application's Deliveries tab, the PIES AI chat's
Build something menu, New agent's Build or change an app
card, and Start delivery on a playbook — the same dialog in each.
It asks for the app first, then What needs doing? Then it reads what
you typed and proposes the kind under How it will run, badging
its proposal proposed and leaving the other two one click away:
Delivery (changes the app by running a playbook's steps on a
branch, with a baseline to roll back to), Errand (one
action against a running app. No board, no version) and
Post (a standing watch on an environment, on an agent
seat).
What you pick decides what it asks next: a delivery wants the application, the
branch and the playbook; an errand wants the environment, what it may touch and a
model tier; a post wants the environment, the trigger and the autonomy. A footer
line states the consequence before you commit, and the button names the kind —
Start delivery, Start errand or
Post.
The chat's other two items are unchanged: Build it now is the
chat build, and Plan first proposes a plan and waits for you.
Agents on the rail is the roster, and everything on it is a
standing post: who is watching what, on which environment. It sits
outside any one application because an agent can look after several. A new
install opens on an empty page — "No agents yet. An agent watches one of your
running apps and acts for you — on a schedule, when something happens, or when
you ask." — and a single + New agent button.
Builders are not agents. Nothing that builds an application has a row here, a
version, a seat or a pause: build work is a delivery, whose steps run as workers that
the platform makes for that delivery and discards when it ends. If you are looking for how
applications get built, that is Playbooks, two entries down.
The same page has two views behind one toggle — Table, the
roster, and Map, the live picture: posts clustered by the
application they watch, delegation as edges, whoever is working right now lit up.
The map is navigated like a map: wheel to zoom towards the cursor, drag to pan,
click a cluster or an application to open it.
Activity is the tray, and its badge counts how many items
need you.
An agent is designed once, on the application — but it is posted
per environment. The same agent can run version 8 on production
and version 9 on staging, each with its own version, its own pause state and its
own approvers. That is not a quirk; it is what lets you try a new brief on
staging without touching what production is running.
The agent page carries a Where it is posted card — each post
runs its own frozen version — listing every environment it runs on, with
Post again, Pause and Fire on each
row, and posting on another environment to add one.
Auto-post when the app deploys decides what a deployment
re-arms: All environments, Selected, or Off.
The deployment page's Agents tab is the other side of the same
fact: who is posted on that environment, live or paused, with Post an
agent and Run an errand. A post cannot be placed on a
preview — deploy the app to an environment first. The
Agentic AI manual walks through it.
The single most confusing thing about posts, so it is worth being blunt.
Only when you post again. The brief, the access grants, the triggers and
the guardrails are frozen into a numbered version when you post. Editing them
changes nothing on the running agent until you Post again, which
freezes a new version. Run rows show the version they executed, so a run that
ignored your edit is telling you it ran an older one.
Immediately. Pause and resume, a standing always allow
rule, and the agent's own memory take effect at once, with no need to post again.
Live vs draft on the agent page diffs the running version
against the draft, so you can see exactly what posting again would change before you
press it.
None of this applies to deliveries or errands: neither is a post, so neither has
a version to freeze. Editing a playbook changes the next delivery that uses it.
The roster is paged and faceted on the server, so it stays usable at
hundreds of posts rather than becoming a wall of cards.
There is a search; a status control —
All, Posted, Draft, Paused, Fired —
with a count against each; an App select once there are posts to
narrow; and a
Filters popover for role, tag, environment and how the agent was
posted. You can group by application, and sort by what is live, by
name or by when it was last updated. Select several rows and the bulk actions
apply to all of them: post, fire, pause, resume, delete, and set tag or
approver — the approver being whoever posted it, whoever runs the application, or
named people.
Every row carries a lifecycle strip —
Draft → Posted → Working → Needs you → Paused → Fired — showing where that agent
actually is, with one plain-English line under it: "Posted v8 3d ago · next
in 12m", "2 waiting on you", "Paused by simon 20m ago". A
failing agent shows red.
Two posts that call each other fold into a single row, as a pair —
"Watcher → Tasker" — because that is one arrangement, not two unrelated
workers.
+ New agent is on the Agents page and on the +
beside Agents in the app editor's Build explorer. From the Agents page it walks
three steps — Who, Brief,
Freedom. From inside an app, Who is already answered, so it walks
two.
Who does this agent act for? has two answers: Me — my
assistant acts with your permissions on any app you can open;
One app lives inside one app and works with its data and users.
A third card, Build or change an app, is not an agent at all: it
opens Start work for a delivery. Brief is the job description,
with a name unique in the workspace. Freedom sets the autonomy
(Supervised, Autonomous or Locked down), the access group it runs as, its reach
(read only, or read and write) and when it runs.
The result is a draft: nothing runs until you post it on an environment. There is
no Role picker — the role shown on the roster is worked out from the agent's reach
and triggers. The Agentic AI
manual shows each step.
One page per agent, and it opens on what the agent is doing rather than on its
settings.
The header says which environment it is posted to and at which
version, when it next runs, and whether the design was edited since, with
Run now, Post again and a menu for Open
conversation, Live vs draft, Fire and Delete. Glance tiles show
how it runs, which model tier, what needs approval and who approves.
Brief is the job description the live post is running.
Runs lists every run — who or what started it, when, how many
steps, and its outcome — each opening to its steps and report.
Where it is posted and Conversation follow.
Configure is four steps — Brief,
Access, Triggers, Guardrails —
and those are exactly the things a post freezes. In the app editor the same steps
open as design, with no Post, Pause or Fire; posting happens on the deployment's
Agents tab. See Reading the agent
page.
An agent's brief is its job description in plain language, and it reads it on
every run — so running it needs no prompt at all. A prompt, when you give one,
is extra direction for that one run.
Write it the way you would brief a person: what it owns, what it must not touch,
and what "done" means. The shipped templates are worth reading as examples —
each names the tools it should use, the evidence it must produce, and the line
it must not cross ("you never build", "you never close a card you have not
probed").
Alongside the brief an agent keeps memory: long-lived notes it
carries between runs. Memory is the agent's own, it takes effect immediately,
and it is never shared with another agent.
Access is the real guardrail. An app agent has a
reach in its app — read only, or read and write — and writes
still obey its autonomy.
Runs as is the app access group the agent acts as, and it is
required. The app enforces that group's permissions on every call: functions the
group is not granted are refused, direct data reads are limited to the tables the
group may read, and rows the agent writes carry a system user named after its key.
Each deployment issues one credential per access group; templates ship each agent
with its group set.
Documents attaches files for the agent to read. The agent already
receives a generated contract of the app's tables and functions, so its brief does
not need a tool list.
Autonomy decides what parks for approval:
Supervised (the default) parks any call that writes and lets reads
through; Autonomous never asks, so keep it for read-only agents;
Locked down parks every call, reads included. Who is asked
sends a parked call to whoever posted it, the app's operators, or named people.
A parked call waits 5 minutes for Run now and 15 minutes for scheduled runs, events
and errands, unless the agent sets its own window. If nobody answers, the change is
skipped, the agent is told, and the run carries on to its report. Nothing deploys
or restarts production from inside a run without explicit approval.
Budget caps each run — iterations, tokens and minutes — plus
monthly tokens, where 0 means no cap.
There is no model picker. Every run uses the model your organisation's
AI governance policy selects for the app, and an agent
cannot override it. A playbook's per-step Small, Mid and
Strong tiers choose among the models that policy already permits.
On request is always on: the agent runs when someone talks to it
in Conversation or presses Run now. On top of that you can add:
Schedule — a cron shown in plain words, the next run, and the
prompt it runs with (0 9 * * 1-5 is weekdays at 9am).
App event — runs within seconds when a platform signal fires for
the app: a deployment or build fails, or a bug is reported from the preview.
Webhook — runs when an outside system calls its endpoint; sign the
raw body with HMAC-SHA256 and send it as X-Pies-Signature: sha256=….
The signing secret is issued at the first post and kept by Post again. The endpoint
runs every unpaused post of the agent; add ?deployment_id= to call one
environment's post.
Triggers are frozen with the rest of the post, so a changed schedule arms only when
you post again.
An errand is one action against a running application. It has
no board, it produces no application version, and it ends with an outcome. Start
one from Start work, which proposes Errand when your wording asks
for one action, or from Run an errand on a deployment's Agents tab.
You choose the environment, what it may touch, and the model. Then it runs, and it
gets its own run page with its steps and outcome.
An errand always runs Supervised: approvals for anything that
writes go to you and to that environment's operations approvers, and whoever
answers first decides.
A playbook is the recipe a delivery follows: a set of
rules that every step runs under, plus an ordered list of
steps, each with a role, its tools, a model tier, and whether it
runs once per delivery or once per card. Nothing in a playbook is posted, versioned
or seated. The workers that run its steps are made fresh for one delivery and gone
when it ends.
The Playbooks page has a workspace bar, a search over names,
descriptions and step names, a Stock / Made here
filter, and sorting by name, most used or most steps. Each row shows its steps, how
many run per card and how many deliveries have run on it; Start
delivery appears when you hover the row.
One playbook serves any number of deliveries, and you will not need many. See
Playbooks in the Agentic AI manual.
Every workspace comes with three playbooks marked Stock; edit or
delete them like any other. New playbook starts from one of them or
from blank:
Full delivery route — Intake, Architecture, UX design, Data layer,
Functions, Screens and wiring, Verify. For anything with several requirements; each
step judges in its own context.
One builder and a verifier — Design, build and wire, then Verify.
A single slice when the full route is too much ceremony.
Fixer — Reproduce, Fix, Verify. For an app that is built and
broken.
Three principles the stock playbooks follow, and yours should too: whoever builds
never closes a card — Verify closes it, with probe evidence; Verify never fixes; and
the one Strong step is the one that has to be right first time.
A playbook's page has two fields, Rules and
Steps. The header shows how many steps the playbook has and how
many deliveries use it. A playbook is rules and ordered steps, not a roster:
nothing in it is posted, versioned or seated.
Rules is injected into every step's run. It is the playbook's
standing instruction — what the work is for, what it must not do, what "done"
means — written once and read by every step.
Steps is the route every card walks, one worker at a time, each
handing over on the board. Each step has a title, a role
(Lead, Builder, Operator, Monitor, Tester, Architect, Data Engineer, Backend
Engineer, UX Engineer, UX Developer, Analyst, or Custom), a model tier of Small,
Mid or Strong, and a per card
toggle — on, it runs once per card; off, once for the whole delivery. Expand a
step for its own Brief and Tools; leave Tools
empty and the step gets the playbook's default tools.
Every step needs a role, because a role is what the step's worker is instantiated
as. Reorder, add and remove steps freely: granularity here is data, not
architecture — adding a step is editing a list, not changing the platform.
One note for anyone scripting against PIES Studio: the API still calls a playbook
a squad and still calls its rules a charter
(/agents/squads/list, /agents/squads/create and the
rest are unchanged). Nowhere in the product does either word appear.
A delivery is build or change work on one application
and one branch, with a board of cards and a baseline to roll back to. You
find it in the editor under Build → Deliveries. Deliveries were
called missions before 1.9.
When a delivery starts, PIES runs the playbook's steps in order. Each step is a
separate worker in its own context, and steps hand over through the board's cards.
The workers are not records: you never post, pause or fire them, and they never
appear on the Agents page. When the delivery ends, none of them remain.
The delivery opens in its cockpit. The header shows the app,
branch, playbook, who started it and a status — Running,
Paused, Awaiting decision, Stopped, Done or
Failed; a delivery you stop is saved as Stopped, not Failed. Below it are
the Brief and Board, the Route
of the playbook's steps with the runs under each, and Live: the
running step's trace and a box to say something to it at its next turn.
Actions are Pause, Resume, Stop, Retry or Skip when a decision is
waiting, Roll back to the baseline, and Delete when nothing is running.
Nothing reaches production without a deploy, which is a separate decision. See
Deliveries in the Agentic AI
manual.
A delivery walks its playbook's steps in the order you wrote them, and nothing
is filled in behind your back. Each step is a worker built from that step's brief,
its tools and its model tier. A step that is not per card runs once, where
it sits; consecutive per card steps take every card through them before the
next once-per-delivery step begins.
A step can only change the application if you gave it the tools to build, which is
what makes a verifier a verifier. Cards carry the handover: every step writes what
it did onto the card and the next step reads it.
Only the steps the playbook actually has take part, so a two-step playbook works as
well as a seven-step one. Platform configuration — sign-in, roles and permissions,
SSO, environments, secrets, theming — is configured in Studio and is never treated
as work to build. A step that needs a decision waits as Needs you
in Activity, where you can retry or skip it. After a restart, an interrupted
delivery offers Run again.
Each step's run keeps its full trace — what the worker was asked and every tool
call it made — and any run of any kind can be opened from Activity.
Every application version is stamped with the delivery and the step that caused it,
so "why is this screen like this" has an answer long after the delivery's workers
are gone.
Activity is the one tray. It replaced the Comm Center, the
inbox drawer and agent items in notifications, because three places to look
meant no place to look. One feed merges approvals, questions, deliveries,
errands, posts and deploys, filtered by Needs you,
Running, Today, Failed or
Everything, and narrowed further by application, environment and
kind — Approvals & questions, Deliveries,
Errands, Posts, Deploys.
Each row is badged with its kind and reads as one line: "Risk dashboard v2 ·
delivery · 5 of 7 tickets", "Reconcile failed payments · errand ·
production · done", "Loan Risk Watch · scheduled run · production".
Colour follows state rather than kind, so what needs a person and what broke
still stand out whichever kind of work produced it.
An approval shows the exact payload the agent intends to send — never approve
blind — and is allowed or denied inline.
Always allow turns a decision you keep making into a standing
rule for that agent, and it takes effect immediately rather than at the next
Post again. A question is answered inline and the run resumes in place. Every row
opens whatever produced it: the cockpit, the errand's run page, the agent page,
the deployment.
Nothing else gets a feed. The rail badge counts how many items need you across
all three kinds of work, and parked approvals reach your phone when the companion
app is installed. Start work sits in this page's header, because
Activity is the one place where no kind of work is more at home than another.
Who is asked depends on the kind of work.
A post. The agent's Who is asked setting:
whoever posted it, the app's operators for that environment, or named people.
Approval routing is per environment, so production can require approvers that
staging does not.
An errand. Always Supervised: you and the environment's operations
approvers, and whoever answers first decides.
A delivery. Decisions a step is waiting on appear under Needs you,
where you can retry or skip.
Every action is audited with the agent, the run and the posted version, and a post
costs one agent seat. See Activity
and approvals for approval modes and what happens when nobody answers.
An application carries its own agent designs. They are configured inside the
editor under Build → Agents and open as tabs like screens — as
design, with no Post, Pause or Fire. An agent designed inside an application is
posted from the deployment page's Agents tab onto whichever
environment you choose, which is how a monitor written for one app comes to be
watching its production deployment. Templates can mark an agent to post itself
whenever the app is deployed.
The application's Deliveries tab is the other half: the
deliveries on this branch and Start work to begin another.
Deleting an application deletes its agents and ends their posts.
Three ways, and no others — which is why an agent's behaviour can be
reasoned about at all.
Calling. An agent granted Call other agents hands work
to a named agent; delegation is depth-capped and stays inside your organisation.
A delivery coordinating through the board. A playbook's steps do not
chat at each other: they hand work over through the application's Tasks board,
which is why the board is the honest record of a delivery.
Events and data. An agent reacts to something that happened — a
deployment, a failed build, a record the application wrote.
Agents do not share memory. Each keeps its own notes between
runs, and anything one agent must tell another travels by one of the three
routes above. How agents work, in the product, says the same
thing with the diagram.
Every run is governed and audited like every other AI call, whichever kind of work produced it. Decisions, in the Organisation group of the sidebar, shows whose authority agents spend, who answers for them, and the decisions record — filter by agent, person, outcome or date, open the full context, export CSV — and the audit log in AI governance answers "what did the agents do, on which models, at what cost". Every run records the tokens it consumed and is metered against the application it ran in.
Enterprises need to say which AI may see what. Governance is policy applied to every AI call, with an audit trail — not a setting somebody remembers to respect.
A policy decides which provider and model serves a request, by request type,
tier and data classification: cloud frontier models for the hardest work,
PIES Private AI on your own GPUs for anything that must not
leave the network, and an air-gapped policy that permits nothing else. Every
answer in the chat carries a chip showing which policy routed it, and
Why? explains the decision.
The enabled policy is what routes a build — not the model picker.
The model chosen under AI Settings configures endpoints and
keys; if a policy names a different provider, the policy wins and the model
picker is overruled. That is deliberate: it is what stops a user routing around
an air-gapped rule. When a policy allows the local provider, the model actually
loaded on your Private AI host is used rather than the name written into the
policy — provided that model is certified build-capable.
If governance authorises no model at all — no matching policy, or a deny — the
request fails rather than falling back to a cloud default.
A request nobody approved does not run.
A policy is scoped either to the organisation or to the whole install. An
enterprise install is single-tenant — one organisation — so both reach the same
people; install scope is the natural home for a platform-wide rule (an air gap,
a compliance pack). On PIES Cloud, where many organisations share a platform,
install-scoped policies apply to all of them and org-scoped policies stay
private to one.
Templates → Routing presets holds seven ready-made postures —
Air-Gapped, Regulated Industry (HIPAA / PCI), Cost-Optimised Hybrid, Frontier
Quality, Hybrid Burst-and-Train, Public Sector / Sovereign and Default
Balanced. Each is a single
policy, not a bundle; Instantiate creates an editable copy in
your organisation, and from that moment it is an ordinary policy you can retune
on the Policies tab.
Instantiating offers two modes. Add creates the policy
alongside what you already have. Replace current preset removes
the policies you previously created from these same presets and installs this
one in their place — your own hand-written policies and any compliance-pack
policies are left alone. Use Replace when you are switching posture, so you do
not end up with two presets quietly competing.
Priority decides who wins. A preset that matches only part of
the traffic relies on a broader policy underneath it: Cost-Optimised Hybrid
matches only repetitive work, so it must sit above Default Balanced for
everything else to fall through to the cloud. Air-Gapped is the opposite — it
matches everything, which is what makes it an air gap.
Three presets put work on your own GPUs, and they differ in how much.
Air-Gapped sends every request to PIES Private AI and blocks
Anthropic and OpenAI outright. Regulated Industry sends only
the sensitive classes (PII, PHI, PCI) on-prem, with redaction, and lets
everything else use the cloud. Cost-Optimised Hybrid splits by
effort rather than sensitivity: repetitive work runs locally and free, real
thinking still goes to the cloud.
That split is decided by a heuristic read of the request — no extra model call,
no cost. Renaming, recolouring, adding a widget and other mechanical edits count
as light and route local; anything that names logic, knowledge, a build or a
design counts as heavy. Anything ambiguous is treated as heavy
on purpose: a real build handed to a small local model is the expensive mistake,
so the tie is broken towards quality.
Whichever preset routes it, the model that actually answers is the one
loaded on your Private AI host, not the name stored in the policy, and
it must be certified build-capable before it may drive a build.
If nothing loaded satisfies the policy, the request fails — there is no silent
fall back to a cloud model.
Two switches on Governance → Overview control the hybrid
behaviour. Cost-optimised routing is the one-click form of the
preset above. Decompose builds goes further: inside a single
build the cloud plans the work and writes the logic while the local model
executes the repetitive items, and any item the local model cannot finish is
escalated to the cloud automatically.
These switches are the source of truth. A
PIES_HYBRID_DECOMPOSE environment variable may be set on a server to
bootstrap a headless install, but it applies only while no choice has been made
here; once you use the toggle your decision wins and survives restarts. If the
environment is currently forcing decomposition on, the page says so beneath the
switch rather than showing you an Off that isn't true.
The savings panel underneath reads in requests, not tokens: the
headline percentage is the share of calls served locally and the figure beside
it is cloud spend avoided. Token share is shown separately and always looks
smaller — one cloud build outweighs hundreds of local edits.
Policies are only as good as the traffic they meet, and the way to find out
what yours does is the Test Console
(PIES AI → Governance → Test Console). Describe a request — the
prompt, the request type, optionally an app, a user, their roles and a region —
and it returns the decision the live engine would make: which policy matched,
whether it was allowed, the data classification, the provider and model chosen,
and what redaction would strip. View on a policy draws the same
decision as a diagram, with the matched path highlighted.
A shadow policy is one that never affects a live request. It is
evaluated only when you simulate or replay, which is how you measure what a new
rule would have done to real traffic before you let it route anything. The
Overview counts how many you have.
Overview also lints the set — the usual finding being a policy that can never
match because a broader one above it already caught everything. Priority is the
only thing that decides who wins, so a narrow policy sitting below a
catch-all is dead, not subtle.
The same page scores an output for factuality: paste a model
answer and the source text it should be faithful to, and it reports how much of
it is grounded and which citations are not.
A policy that names a cost-efficient model still allows its whole provider,
and not every request is the same size. Where a request is classified
heavy — screen authoring, architecture, a real build — the engine
upgrades to the stronger model within the provider that policy already
allows. The provider, the redaction and the audit trail are untouched; only
the model within an already-permitted boundary changes.
This is why agent and delivery output should not read as weaker than the chat's.
Before it existed, every heavy step of a delivery took the cheaper model the
policy named while chat ran the frontier one, and the screens showed it.
Every governed call is recorded — provider, model, tokens, policy decision
and a factuality score — in a signed, hash-chained audit log. That includes the
assistant inside your deployed applications: every message an end user
sends is evaluated against policy as it arrives, redacted, routed, and audited
against the tenant that owns the app and the user who typed it. The
Data class column is the data classification of the content
(internal unless something sensitive is detected), not where the call
was routed — an internal row beside an Anthropic route means ordinary
content that policy allowed to go to the cloud.
Redaction strips sensitive data before it reaches a model. The
Knowledge Base holds documents the AI may build against, and
per-application Artefacts (a BRS, a specification) are fed into
builds so the AI works from your requirements rather than its own assumptions.
Point Studio at a model running on your own GPUs and no prompt, application definition or data leaves your network. Installing it is covered in the PIES Private AI manual; connecting it is a URL and a key under PIES AI → AI Settings — the PIES AI entry in the workspace sidebar, whose tab row is Overview, Governance, Audit, Knowledge Base, AI Settings, Private LLM and Help.
Applications rarely live alone. These surfaces let an application call out, be called into, and share credentials safely.
Integrations, in the Organisation group of the sidebar, connects the platform to the systems around it: databases, cloud providers (AWS and Azure) and Kubernetes clusters for deployment, and Apple and Google Play accounts for publishing mobile apps. A connection is made once and reused by every application. Source control and the Microsoft Teams connector are set up separately, as the Source Control and Teams Connector tabs under Admin.
API Connections — under Configure in the
explorer — defines the outbound calls your application makes: the
+ opens New Endpoint (name, method, URL such as
https://api.example.com/v1/resource/:id, headers, query, body and the
expected response, with a Test button). A REST Call step then
uses a connection from a function, with the response mapped into your own model.
In the other direction, every generated application already serves its own REST
API, documented as Swagger: open /docs (or
/swagger/index.html) on the running app, fetch the spec from
/openapi.json, or use API docs in the build console's
Preview tab while a preview runs.
Credentials for both live in Configure → Secrets &
environments → Secrets, never in the application definition.
A preview is a real build: the generated code is compiled and run against a real database. This is where errors surface — an AI chat receipt is not proof that an application builds.
▶ Run, at the top right, opens the build console: pick the
environment beside the green Build button — Preview
by default — and build. The console reports each layer as it goes — the database,
the Go back end and the React front end — with a percentage and a live output
log, and a red Stop while it runs.
The caret beside Run is the same actions as a menu: Build and
Deploy open a submenu per environment (Build Preview;
No deploy targets configured until you set one), alongside
Problems and Build and export. Until an
application has been built, the menu says build required.
The PROBLEMS tab groups anything that failed by area — Database,
Interfaces, Events, Functions, Other — each with a count, and offers
Analyse & Fix with Pie Loop and a per-item Fix.
An application with build errors never reaches preview, so clear
these first.
On a clean build, ▶ Run starts the preview and the console grows a PREVIEW tab: a health panel listing the front end, the back-end API and the database with their status and latency, the external data sources it connected to, and rails for tests and logs. It also shows how long the session has left. When the services are up, Open Preview opens the real application in a new tab — logging in, saving records, navigating between screens.
Build turns your definition into a real application — a front end, a Go back end and a database schema — and compiles it. It is not a preview of the canvas: it is the same generation that produces the application you deploy, run against the Preview environment. That is why a build failure is worth reading rather than retrying; it is telling you the application as defined cannot be produced.
An application with build errors never reaches preview, so a preview that looks stale is usually a build that failed. PROBLEMS groups every failure by area — Database, Interfaces, Events, Functions — with the object at fault named. Clear it top to bottom: a missing table breaks the screens that read it, so the first error often explains the rest.
Preview runs your application with its own database, its own sign-in and its own data — not a mock. You can register a user, save a record and see it persist. This is also why it takes a moment to come up, and why a preview left running holds resources until it is closed. Sample data is generated so screens have something to show before you have entered anything.
Change the definition, build, look. Small changes rebuild quickly because only what changed is regenerated. If a change does not appear, check in this order: did the build succeed, is the preview session the current one, and is the widget actually bound to the data you changed. In that order the answer is usually the first question.
Deployment takes the same generated code beyond the preview sandbox — to a container host, a Kubernetes cluster, a cloud account you connect, or a mobile app.
Deploying is an action on an environment. Open
Environments in the sidebar (under This app, outside the
editor), pick a target — Docker and Kubernetes deploy to infrastructure you
control, AWS and Azure use credentials connected in Administration — and choose
Deploy on it. A new environment starts with no deploy target and
says so. Each deployment is versioned, so you always know which build is live.
Inside an app the surface is always Environments; the cross-app roll-up
of everything running lives under Deployments (Operate) in the
sidebar. See Chapter 17.
A live environment shows Live in the Environments list; click it to open its deployment cockpit — service health and latency, live endpoints, version, and an activity log — all on the environment, not in a separate place. From the cockpit a deployment can be restarted, scaled, redeployed to a newer build, or torn down, and its logs are readable there. Rolling back is redeploying an earlier version. An active deployment still on an older build carries an amber Serving previous version badge — a failed publish that keeps the old version up never looks healthy. The cockpit also has an Agents tab for the agents running inside it, an Operations panel naming who runs it, and Open as administrator, which signs you into the running application without its administrator password. A live environment cannot be deleted, and only its name can be changed while it runs — undeploy it to change its target or settings.
Switching an application to mobile generates a Flutter application from the same definition — the same data model, functions and events, rendered natively. From there you can preview it, download the APK, or take it through to a store listing. Desktop and mobile stay in step because both are generated from one source.
A calendar, a board, a timeline — some views have no standard widget. PIES builds them as a coded component and, more importantly, connects them to your data: the query that reads the rows, the event that runs when the page opens, and the delivery of those rows into the component.
Describe the view rather than the widget — the time range, what each column represents, what a single entry shows. You type this into PIES AI exactly as in chapter 3; what follows is what comes back. PIES recognises that no standard widget fits, writes the component, and puts it on a new screen.
A titled page with the component laid out inside it, the same as any screen you would draw by hand. The component is yours to edit: its source sits under Build → Custom widgets in the explorer.
A view is only as good as the data behind it, so PIES writes the read function too — filtered on the date the view is showing, returning just those rows rather than the whole table.
An event runs when the screen opens: it calls the query and passes the result into the component. This is the step that separates a view that renders from one that shows your data — and it is wired for you. Open it and you can change what it reads, or when.
Who may open the application, and what each person may see or do inside it. This is separate from platform administration: it travels with the application to every environment it is deployed to.
Under Configure → Users & access → Access Groups, define groups — Administrator, Manager, Read-only — and grant each one access per screen and per operation. Every application starts with a user model and an Administrator group already wired, so a generated application has working sign-in from the first build.
Choose how users of the generated application sign in. Since 1.9
this is configured once on the application, under Configure → Users
& access → Configuration, and every deployment inherits it; an environment can narrow
which methods it offers. The sign-in methods are cards in a guided, numbered
setup: password, Sign in with PIES Studio,
Microsoft, Google, Apple,
OpenID Connect and SAML.
The sign-in policy sets SSO-only, allowed email
domains, and whether self-registration is open and which access group a
new account joins — never an administrator group by default. The session policy
sets session length, single logout,
two-step verification for password sign-in, and
group mapping rules per provider so an identity provider's
claims decide the access group. The application's administrator password is set
here too; there is no default.
The Login, Register, Forgot Password and Set Password screens
are created with the application and can be restyled like any other screen;
forgot-password sends a real email that opens Set Password, in previews as well
as deployments. A built-in Public group lets guests reach data
when an application needs a public face.
The things an application needs but does not own: where it runs, what it connects to, and the credentials it must never contain. Environments has its own place in the sidebar (under This app); Secrets and Email Servers live under Configure in the editor.
Environments in the sidebar (under This app,
outside the editor) lists where the application runs. An environment is a named
target — Preview (built in), Test, Production — with its own deploy target,
variables and sign-in. Each shows its state: Live when a
deployment is running in it, otherwise Idle. Clicking a live
environment opens its deployment cockpit; deploying is an action on an
environment (Chapter 14). The same application definition builds to each; only
the environment differs, which makes a promotion a redeploy rather than a
rewrite.
Tag one environment as Production (the star on its card). When
its deployment is live the application reads as Live (a green
badge) in the workspace app list, and clicking it there goes straight to its
production deployment cockpit rather than the editor. An app with
an active deployment in a non-production environment carries a blue
Deployed badge instead. The Applications toolbar has
Live and Deployed toggles to narrow the list to
each. The cross-app roll-up of every running deployment is under
Deployments (Operate) in the sidebar.
Configure → Secrets in the editor holds both. Secrets holds API keys, database passwords and tokens. They are referenced by name from steps and connections, stored in the vault, and never written into the application definition or the generated code. Email Servers configures the SMTP account the Send Email step uses, per environment.
What every screen shares: the menu, the header, and the look.
Navigation lives in one shared editor — Menu & Header,
under Build → Screens → Navigation, badged App chrome.
Since 1.9 the menu and the header are edited together on one canvas rather than
in two places: add a screen to the menu and it is reachable everywhere, and both
are locked to the application rather than to a screen, so they stay consistent.
It opens in design mode — click anything in the header or the
menu to edit it, use + to drop a widget into one of the
header's slots, or click the header bar itself for its settings. The inspector
on the right carries Menu items (add, rename, reorder, remove),
Menu body (background and active background, width, shadow, the
border on the content edge, whether the logo and app name show in the menu, and
whether the menu starts below the header or beside it), item font and icon
sizing, and Reset to app theme to drop the overrides and follow
the theme again. The header carries the brand, notifications and the user pill
with sign-out. A new application starts with a Home Page: a
welcome band, three get-started cards and a PIES AI tip.
A theme is a reusable palette and type scale — colours, fonts, radii, button gradients — applied to the whole application and to every screen the AI generates afterwards. Change the theme and the application follows; you are not restyling widgets one at a time.
An application is a versioned artefact, not a live document you hope nobody breaks.
Work on a branch when you want to try something without touching main. Create one from Source → BRANCHES → Create new branch, or from the branch menu in the editor toolbar; pick a branch in either place to switch to it. When the work is ready, Merge branches (in the same menu, once there are two branches) brings it back. Studio has no separate commit or compare step. To go back, use Rewind to here above a PIES AI response, which returns the application to the version before that change, or Restore to this on an entry in Source → Activity, which also brings back objects that were deleted since.
Connect a GitHub or Bitbucket repository under Assets → Repositories and the generated code is pushed there — so your engineers can read it, review it, and run it in their own pipeline. Code generation is not a black box: what PIES builds is ordinary Go, React, Angular, Python or Flutter.
Each AI turn is a version. Rewind to here puts the application back to the state before that turn, so an experiment that went the wrong way costs a click rather than an afternoon.
Every change to an application is recorded — what changed, who changed it and when — covering work done by hand and work done by the AI alike. It answers the question a version list cannot: not just what the application looked like at a point in time, but how it got there. An entry can be restored, returning that object to the state in the record.
Three different questions: does it build, does it behave, and why is this screen wrong.
After a build, PROBLEMS groups every failure by area — Database, Interfaces, Events, Functions, Other — each with a count and the specific object at fault. This is the authoritative view: the AI's own summary of what it did is not evidence that the application compiles.
When a build fails, Analyse & Fix hands the failure to the AI with the context it needs — the failing object, the error, and the definition behind it — and applies the correction as an ordinary change you can inspect or rewind. It is the same loop you would run by hand, without copying error text into a prompt. Failures it cannot represent as a change to your application are reported rather than guessed at.
Most build failures name the object at fault, and the fix is usually a missing binding rather than anything deep. The ones seen most often:
A preview is a real deployment of your application, so it fails in the ways deployments do. 502 Bad Gateway means the preview server is still starting or has stopped — rebuild rather than refreshing. A preview that has been running for a long time can be closed from the preview list, which frees the resources it holds; sessions are not meant to be permanent. If a change you made is not visible, check the build succeeded before assuming the preview is stale — an application with build errors never reaches preview.
The preview carries a diagnostic overlay: it reports what actually rendered and, when something is missing, works down the layers — definition, generated code, data, render — to say which one is wrong. It can hand the finding straight to the AI as a fix request.
Work you have done once should not be done again.
The Template store is where a new application can start
from a finished one: browse by category, open a template to see its screenshots,
What's inside (screens, tables, roles, functions, documents) and
About, and install it. More than thirty industry applications ship with
the platform (the store's Discover count shows how many) — hospitality, fitness, beauty, trades, professional services,
education, events, logistics, telco, visitor management, retail banking
analytics and more — with sample data and, where useful, a bundled agent pack.
A template that needs an external system finishes with its own connect step and
offers the integrations you have already saved.
Publish an application as a template and new applications
can start from it. Published assets are the smaller units —
screens, widgets, functions — shared across a workspace so a team converges on
one way of doing things.
An application can be exported and imported whole — for moving between installs, for backup, or for handing a build to someone else. Generated code can be downloaded outright, which matters if you ever need to leave: the output is yours.
Platform-level settings, separate from any one application.
Invite users from Administration → Users: select Add User, fill in Invite User and select Send Invite. Assign roles, and configure authentication — local accounts or enterprise SSO. People are invited as a role, and can be invited before they have an account; roles are presets of capabilities, and a person's capabilities are their role plus explicit grants. Teams group people, and the navigation each person sees follows what they may do. Permissions inside an application are separate, under its own Users & Access; who runs a deployed application is separate again, on the deployment's Operations panel.
On the sign-in page, Forgot password? asks for an email
and answers the same way whatever is typed, so it never reveals who has an
account. If that email belongs to an active account that signs in with a
password, a link arrives to choose a new one; it works once and expires after
60 minutes. Accounts that sign in with Microsoft or another single sign-on
provider reset their password with that provider instead. When the installation
has no mail server, the page says so and asks the person to contact their
administrator.
An administrator can start the same thing from Administration →
Users: the Send password reset action on an active
user emails them the link. If the email cannot be sent, a dialog gives the
reason and the one-time link to hand over privately. Set
password remains for setting a password directly. If the only
administrator is locked out and no email works, see Locked out of the only
administrator account in the installation guide — a command run on the
server recovers the account.
Platform name, logos, favicon and the colour palette used across Studio and the applications it generates. Themes are reusable, so every application in a workspace can share one look.
A tab view holds several panels in one screen: put a form in one tab and a table in another rather than making the user scroll. Each tab carries its own widgets and its own bindings, and the elements list shows them nested under the tab they belong to — expand the tab view to reach a widget inside it.
Any widget can be marked hidden, and a rule can reveal it when a condition holds — an approval button that appears only for a manager, a warning that shows only when a field is empty. Hidden is a property of the widget, so it applies in preview and in the deployed application, not only on the canvas.
Drag a box across the canvas, or shift-click, to select several widgets at once. Their common properties — width, height, position — can then be set together, which is the quickest way to line up a row of cards or give a column of fields the same width. Setting the coordinates directly is exact where dragging is approximate.
A table can carry an action column — per-row buttons for View, Edit or Delete. Each action names the screen it opens or the event it raises, and the row's record is passed to it, so an Edit action lands on a form already filled in. This is how a list becomes a working screen rather than a read-only grid.
A widget without a data source renders nothing. The build does not fail, but it lists the widget as a warning in Problems → Interfaces (“Unbound widget in screen”) — binding is not optional decoration. A table or list names the table it reads and the columns it shows; a form names the table it writes and the fields it edits; a chart names a table, a column to group by and a measure. Where a widget should read from more than one table, define the join on the data source rather than assembling columns in the widget.
The canvas is 1920 wide, and the navigation strip occupies the left of it, so the area your widgets actually occupy is narrower than the canvas. Widths are resolved against that same area in the generated application, which is why a widget that fits here fits there. Containers are the reliable way to keep a screen's shape: group widgets in a container laid out as a row or a grid, and the group holds together at different widths rather than each widget being positioned absolutely.
A widget's events are configured beside its properties: a button raises an event on click, a screen can raise one when it opens. The event is an ordinary event you can open and edit — see the next chapter. Two patterns cover most screens: a form whose submit button calls a function that saves the record, and a screen whose on-open event loads the rows a table or view displays.