Concepts

This page maps the Vertesia platform: what each building block is, and where to read more. Each section of the documentation covers one of these areas in depth.

The building blocks

Concept	What it is	Learn more
Prompts	Reusable text templates with variable injection, assembled into the prompt for a task.	Prompts
Interactions	A task you ask a model to perform: prompts, an optional result schema, and a model configuration.	Interactions
Agents	An interaction that runs as an autonomous loop, calling tools and reasoning over their results until it reaches an answer.	Agents
Processes	Durable state-machine workflows with named steps, deterministic routing, human pauses, and child subprocesses.	Processes
Content Types & Collections	Structured document storage: JSON Schema validation for document shape, collections for organization.	Intake Configuration
Content Preparation	Turning uploaded documents into data your agents can use — extraction, markdown conversion, embeddings.	Content Preparation
Views	Reusable content navigation, faceted or agentic search, and result layouts, rendered in Studio or your own app.	View Experiences
Events	Platform events and the subscriptions that route them to a workflow, agent, process, or webhook.	Event Subscriptions
Environments	Connections to the LLM inference providers that run your models.	Environments
Data Platform	SQL data stores and dashboards for structured data and analytics.	Data Platform
Access Control	Roles and RBAC grants for platform actions; ABAC rules for per-document access.	Access Control
Custom Applications	Apps, MCP servers, and UI plugins that extend Studio or embed it elsewhere.	Custom Applications

The Studio Assistant can build most of the above for you, and is the fastest way to get a first version of something working.

Choosing building blocks

Two decisions come up early, and getting them right saves the most rework.

Direct call or an agent?

An interaction always has a prompt and usually a schema. The question is whether the model needs to act or just respond.

Use direct inference — a single model call — when everything the model needs is already in the input. Classify, extract, summarize, translate, rewrite. It is faster, cheaper, and predictable.

Use an agent when the task needs tool use or iteration:

• it must look things up — search, fetch documents it cannot name in advance, call an external API;
• each step depends on the previous one;
• the reasoning is too hard to land reliably in one pass and benefits from planning and revision;
• a person needs to steer it mid-run.

Start with direct inference and promote to an agent when the task clearly needs it. "It might occasionally need to search" is not a reason — that is just a larger prompt.

An agent or a process?

Use an agent when one autonomous worker can reason its way to the result.

Use a process when the workflow itself is part of what you are delivering and needs explicit named steps, deterministic routing, durable pauses for human work, reusable subprocesses, or fixed parallel branches.

Business workflows of the kind you would normally draw as a flowchart map to processes, not agents.

Agent capabilities

An agent's abilities come from the tools it can call. Tools are granted through skills — a skill bundles a whole domain's tools together with instructions on how to use them, so you grant one skill rather than a list of individual tools.

For example, a research agent needs web search and document search; a document-authoring agent needs the authoring and file-editing skills. See Skills.

A single agent run can also fan out into parallel workstreams, each a child run with its own task.

Runs

A run is one execution of an interaction call — both the request to the model and the response from it.

Prompt template syntax

Each prompt template has a content_type that determines how variables are injected into its content.

Inference providers

Environments connect to the platforms that run generative models. Vertesia supports:

See Environments for provider setup.

Language model background

If generative AI is new to you, these are the ideas the rest of the documentation assumes.

Large language models interpret and produce human language — including programming languages. They are pre-trained on very large content sets and can be fine-tuned afterwards for specific knowledge, though Retrieval Augmented Generation (RAG) is usually the better way to give a model access to your own data. Chatbots were the first well-known application, but a model is more usefully thought of as a processing entity you can call automatically, which is what opens up the range of use cases this platform is built for.

Term	Description
Context Window	How much text the model can consider at once, measured in tokens. Like a notebook with a fixed number of pages: once it is full, only what is written there can be seen. Each model has its own limit.
Token	The unit of text a model processes — a word, part of a word, a character, or punctuation. Splitting text into tokens is called tokenization.
Max tokens	The maximum number of tokens the model may generate in its response. On some models this counts against the context window.
Embeddings	High-dimensional vectors representing text in a way that captures meaning, learned during training. They are what makes semantic search possible.
Similarity	How close two pieces of text are in meaning. "Happy" and "joyful" sit near each other in embedding space; "happy" and "fast" sit far apart.