Imagining Corteza as an Agentic AI Low-Code Platform

Introduction

Agentic AI refers to artificial intelligence systems that act as autonomous agents, perceiving their environment and taking actions without needing explicit human prompts (What Are Agentic AI Workflows? – Interconnections – The Equinix Blog). In practical terms, an agentic AI worker is an AI-driven entity that can make decisions and perform tasks on its own while interacting with users and other systems. Implementing such autonomous AI agents requires a robust framework for data management, decision logic, isolation (for multi-agent or multi-tenant setups), and integration with external AI models and services. Corteza – an open-source low-code platform – provides key building blocks for this framework through its data modules, namespaces, workflows, and integration gateway. This report presents a high-level conceptual overview of how these components can be orchestrated to build agentic AI workers on Corteza. Each section below explains the role of one of these components and how, together, they enable AI agents to autonomously perform tasks, interact with users, and integrate with external services.

Data Modules: Structuring and Managing AI Data

Data modules in Corteza define the structure of information that an application (or AI agent) uses. On the Corteza platform, a “module” essentially corresponds to a database table with predefined fields and data types (Building data modules – Planet Crust). By creating modules, developers specify what data the system will store – for example, a module for “Tasks” might have fields for task description, status, and owner, while a module for “Messages” could store user queries and AI responses. This structured approach ensures that all data relevant to AI-driven processes (user inputs, context knowledge, intermediate results, etc.) is organized consistently and can be easily queried or updated.

In the context of an AI agent, data modules serve as the agent’s knowledge base and memory. The agent can record facts or context (e.g. a “Knowledge” module of reference information), maintain state (e.g. a “Session” or “Conversation” module logging interactions), and track tasks or outcomes (e.g. a “Workflow Tasks” module for pending actions). Well-designed modules enable the AI worker to retrieve past information and store new results in a structured way. This data structuring is crucial for AI-driven processes because it provides a reliable foundation on which the AI’s logic operates. In short, Corteza’s data modules provide the tables and records that an autonomous agent uses to know what is going on and to persist its decisions or observations. By managing data through modules, the platform ensures the AI agent always works with organized, consistent data, which improves the accuracy and reliability of its autonomous behavior.

Namespaces: Multi-Tenant Environments for Different AI Agents

A Corteza namespace is the root container of a low-code application, encapsulating all the components (modules, pages, workflows, etc.) that make up that app (Low Code configuration :: Corteza Docs). In essence, a namespace is like a self-contained schema or space where one application’s data and logic reside. This design naturally supports isolation between different applications or tenants. For implementing multiple AI agents, namespaces can be used to give each agent its own segregated environment. For example, if deploying several AI workers (each for a different department or client), one could create a separate namespace for each agent. Each namespace would contain that agent’s data modules and workflows, isolated from the others.

Using namespaces in this way facilitates a form of multi-tenancy on the Corteza platform. Each AI agent (or each client’s AI agent) operates in its own namespace, which means its data and configurations are siloed. This isolation is important for both organizational clarity and security – one agent’s records won’t accidentally mix with another’s (Multi-tenant App – Low-Code Apps – Corteza). It also allows differing configurations: one agent’s namespace might have modules and workflows tailored for customer support tasks, while another’s is set up for internal IT automation. The role of namespaces, therefore, is to provide separation of concerns and tenancy. They ensure that even if you have many autonomous AI workers running on the same Corteza instance, each can be managed independently, and data or processes meant for one won’t interfere with others. In a multi-agent scenario, namespaces are the boundaries that keep each agent’s “world” separate and well-defined.

Workflows: Automating Decision-Making and Task Execution

Corteza workflows are the automation engines that drive an AI agent’s decision-making and actions. A workflow in Corteza is a visual, no-code business process that you can design in a BPMN-like diagram interface (Workflows :: Corteza Docs). This allows you to implement custom logic – the rules and decision flows that determine how the agent reacts – without writing code. Workflows consist of a series of steps such as triggers, conditions, branches, and tasks. For an agentic AI worker, you can think of workflows as its “brain” or decision circuit: they take inputs, apply logic (possibly invoking AI computations), and produce outputs or actions.

Triggers initiate workflows, enabling autonomous operation whenever certain events occur. In Corteza, a workflow can start in response to various events or conditions – for instance, when a new record is created in a module, when a user submits a form, or on a scheduled interval (Workflows :: Corteza Docs) (Workflows :: Corteza Docs). This means an AI agent can be set to wake up and act whenever something relevant happens. For example, a trigger could be a new support ticket arriving (a record create event) which launches a workflow for the support AI agent to process that ticket. Workflows can also run on schedules (e.g. every night or every hour) to perform routine tasks without human intervention (Workflows :: Corteza Docs). Once triggered, the workflow executes a predefined sequence of steps. It can evaluate conditions (making decisions using if/else logic), update or read from data modules, loop through records, and call functions. In effect, the workflow can embody complex decision trees (“if the user’s request type is X, do Y; otherwise, do Z”) and handle multi-step procedures automatically.

Crucially for AI-driven behavior, Corteza workflows can incorporate calls to external services or script logic as part of their steps. For instance, one step in the workflow might send a query to an AI model (via the integration gateway or an HTTP node – discussed later) and wait for a result, and the next steps use that result to decide how to respond. Other steps might create or update records (storing the AI’s decisions back into the module data), send notifications, or prompt human approval when needed. By chaining these steps, the workflow automates both the decision-making (through logic and AI model calls) and task execution (through actions like updating data or invoking external APIs). In summary, workflows enable the AI agent to carry out its tasks end-to-end: from sensing an event, through deciding on an appropriate response, to executing that response – all according to a predetermined logic flow. This is how Corteza gives the AI worker its autonomous behavior, as the workflows continually run in the background handling events and performing tasks according to the rules you’ve designed.

Integration Gateway: Interacting with External AI Models and APIs

While data modules, namespaces, and workflows manage internal logic and data, an agentic AI often needs to communicate with the outside world – both to receive inputs (e.g. user messages) and to leverage external AI services (e.g. calling a large language model API) or other APIs. Corteza’s Integration Gateway is the component that facilitates this external interaction. The integration gateway allows developers to define custom HTTP endpoints on the Corteza server for integration purposes (Integration Gateway :: Corteza Docs). These endpoints can handle incoming requests (such as webhooks or API calls from other systems) and route them into Corteza’s automation pipeline. They can also be configured to forward or proxy requests to external services. In short, the integration gateway acts as the bridge between Corteza and external systems.

Through the integration gateway, Corteza can connect with any third-party source – even if that source doesn’t natively offer a REST API – by defining appropriate connectors or proxy rules (Integration Platform – Corteza). For incoming data, the gateway can pre-filter or validate payloads and then hand them off to a workflow for processing. For example, if an external chat service sends a user’s message to Corteza, an integration gateway endpoint can receive that JSON payload, verify a token or format, and then trigger a Corteza workflow that handles the message. Conversely, workflows within Corteza can use integration gateway routes or built-in HTTP request steps to call external APIs. This is how an AI agent in Corteza might query an external AI model: the workflow could hit an endpoint (possibly via a payload processer or direct HTTP step) that sends a prompt to an AI service (like a cloud ML API or a large language model) and then receives the response for further use. The integration gateway supports custom authentication, payload transformation, and even rate limiting on these calls (Integration Gateway :: Corteza Docs), providing control and security when the AI agent interacts with outside APIs.

By tying the integration gateway with workflows, Corteza enables an AI worker to not only consume external intelligence but also act on external systems. After processing data, the agent can send results out to third-party services – for instance, updating a record in a remote CRM via its API, or sending an email or chat message back to a user. In essence, the integration gateway gives the AI agent I/O channels: it’s how the agent listens to outside events and how it carries out actions in external services. This component is vital for creating AI workers that are not closed silos but rather active participants in a broader software ecosystem, capable of leveraging external AI capabilities and interacting with users on whatever platform they are on.

Putting It All Together: Creating Agentic AI Workers in Corteza

By combining data modules, namespaces, workflows, and the integration gateway, we can create a conceptual framework for an agentic AI worker on Corteza. Each component plays a distinct role, and together they enable an AI agent to operate autonomously, interact with users, and integrate with external tools. At a high level, the AI agent’s operation in Corteza can be envisioned as follows:

• Isolated Context (Namespace & Modules): We begin by giving each AI agent its own namespace, which contains all the relevant data modules for that agent. For example, an “AI Support Agent” namespace might include modules like Tickets, Customers, Agent Responses, etc. This namespace isolation means our support agent’s data is separate from any other agent or application. Within these modules, the agent records what it needs to know: new incoming tickets land in the Tickets module, a knowledge base of FAQ articles might reside in another module, and so on. This structured data setup forms the agent’s contextual world – it knows about open tickets, previous conversations, customer details, etc., through the module records in its namespace.
• Autonomous Workflow Logic: Next, we define workflows in the agent’s namespace that encode how the agent will react and what tasks it will perform. For instance, a workflow could be triggered whenever a new ticket record is created. When activated, this workflow might have the agent analyze the ticket’s content (possibly calling an AI model for sentiment or topic analysis), then decide on a course of action. One branch of the workflow might be “if the ticket is about password reset, use the knowledge base to draft a solution; if it’s something else, acknowledge receipt and forward to a human” – illustrating decision logic. Another workflow might run on a schedule (e.g. every hour) to pick up any tickets that haven’t been addressed and send follow-up messages, demonstrating the agent’s ability to perform routine tasks on its own. All these workflows run without manual intervention, effectively letting the AI agent monitor events and act continuously.
• External Interaction via Integration Gateway: To enable user interaction and external AI processing, we leverage the integration gateway alongside the workflows. For user-facing communication, we could expose a custom endpoint (via the gateway) that a chat application or web form uses to send user messages into Corteza. When a message comes in, the gateway triggers the appropriate workflow in the agent’s namespace, handing off the user’s input. The workflow then processes the input – for example, it may call an external AI service to generate a natural language reply or to classify the request. This call is done using an HTTP step or a proxy route configured in the integration gateway, allowing the Corteza workflow to invoke an external AI model (such as an NLP service) and retrieve the result. Once the AI’s response is received, the workflow can route it back to the user: perhaps by creating a record in an “Agent Responses” module which is picked up by the frontend, or directly via another gateway call that posts a reply through an external messaging API. In parallel, the workflow updates the Corteza data modules (logging the interaction, updating the ticket status, etc.), so the agent’s memory stays up-to-date.
• Continuous Learning and Adaptation: (Optional in our framework) Since all interactions and outcomes are stored in modules, we can have additional workflows that analyze this data over time to adapt the agent’s behavior. For example, a nightly workflow might summarize the day’s resolved tickets and feed that into an AI model to refine the agent’s knowledge base or adjust its response strategies. While this is an advanced aspect, it highlights how having all components integrated allows a feedback loop: the agent can learn from its stored data (with human oversight if needed) and thereby improve its autonomous decision-making. This aligns with the idea of the agent perceiving its environment, analyzing outcomes, planning improvements, and executing changes – a cycle akin to the classic monitor-analyze-plan-execute (MAPE) loop for intelligent agents (What Are Agentic AI Workflows? – Interconnections – The Equinix Blog).

Through this combination of features, Corteza provides a conceptual framework for agentic AI workers. The data modules give structure and memory, namespaces give isolation and multi-agent capability, workflows provide the logic for autonomy, and the integration gateway connects the agent to users and external AI power. For example, a fully realized Corteza-based AI support agent could autonomously handle customer inquiries: it would receive questions via an API gateway endpoint, use workflows (and perhaps call an LLM via the integration gateway) to formulate answers, consult its data modules for customer context or past solutions, and respond to the user – all in a seamless loop. If the query is beyond its scope, another workflow might escalate the issue to a human, demonstrating decision autonomy in knowing when to self-limit. Meanwhile, a different agent in another namespace could be autonomously managing background IT tasks (like monitoring systems and triggering alerts), using the same pattern of components but operating with a completely separate dataset and purpose.

Conclusion

Corteza’s low-code platform offers an integrated stack of tools that lend themselves well to building agentic AI systems. Data modules ensure that AI processes have well-structured data to work with, functioning as the agent’s internal knowledge repositories. Namespaces allow multiple AI agents (or tenants) to coexist, each in its own sandboxed environment with dedicated resources, which is essential for scalability and multi-tenant deployments (Multi-tenant App – Low-Code Apps – Corteza). Workflows bring these agents to life by automating decisions and tasks – reacting to events and executing multi-step logic flows that constitute the agent’s behavior (Workflows :: Corteza Docs) (Workflows :: Corteza Docs). And the integration gateway connects these internal mechanisms to the outside world of users and external AI/APIs, enabling the agent to both receive stimuli and exert effects beyond the Corteza instance (Integration Platform – Corteza). By weaving together these components, developers can create AI workers that exhibit agency – meaning they operate independently, interact with people and systems, and continuously carry out their objectives. This framework transforms Corteza into a launchpad for autonomous AI agents, where each agent can perceive incoming data, reason and decide via workflows (augmented by AI models), and act on those decisions in a structured, auditable manner. In summary, Corteza’s data modules, namespaces, workflows, and integration gateway form a powerful conceptual architecture for implementing agentic AI workers that are capable of autonomous operation, rich interaction, and seamless integration in a multi-system environment.