We designed Rierino’s AI Agent Builder to meet a growing demand we have consistently seen in enterprise environments: the need for AI agents that do more than generate content or carry on conversations. Enterprises want agents that can act securely, contextually, and across real systems.
This new capability builds on our original vision for Rierino. Since day one, we have treated embedded intelligence as a core part of the platform—not an add-on. In fact, Rierino was the first low-code platform to support embedded AI capabilities as early as 2020. From orchestrated automation to rule-based logic, AI has always been integral to how our platform helps teams move faster without sacrificing control.
Last year, we launched RAI, our embedded GenAI assistant, to simplify how teams create content, templates, microservices, and UI structures within their workflows. Now with AI Agent Builder, we are taking the next step: giving agents the ability to execute actions, trigger backend flows, and operate within a fully governed runtime.
There has been no shortage of innovation around AI agents recently. From orchestration libraries to no-code copilots, the space has rapidly expanded with new ways to build conversational or task-driven agents.
But for enterprises, conversation isn’t enough. Execution is what counts.
Most tools today stop at integration. They wrap APIs with LLM prompts, or offer visual builders that simulate action without addressing the deeper architectural needs like input validation, access control, versioning, or system-level observability. That might work for internal demos, but it’s not what enterprise teams need in production. And it’s certainly not enough to scale.
We built AI Agent Builder to close that gap—to give teams a structured execution layer that turns their business logic into agent-accessible capabilities. Not by adding a chatbot UI, but by exposing real saga flows, workflows, and services as secure, reusable tools that agents can call—just like any other part of the platform.
For the architectural rationale behind this shift, see our related article on Building ‘Empowered’ AI Agents for Enterprise.
AI Agent Builder isn’t a layer added on top of the Rierino platform—it is an extension of its core architecture. Everything an agent needs to operate—tools, rules, workflows, governance—is already available as part of how developers build and deploy logic inside Rierino.
Here’s how that works in practice:
At the heart of Rierino’s orchestration model are saga flows—event-driven, stateful workflows that can span multiple services, apply custom logic, handle compensations, and enforce data validations. These flows are built visually in a low-code canvas but generate fully composable backend logic.
With AI Agent Builder, any saga flow can be automatically exposed as a structured tool for an agent to use. There’s no need to wrap it in new interfaces or define custom schemas—inputs, outputs, validation logic, and permissions are inherited from the saga flow’s own configuration.
That means an agent can:
And do so using real-time, governed logic—not a freeform prompt or unstable API chain.
Every agent-triggered action is executed within the same security boundaries as any human-initiated flow. That includes:
This kind of deep governance is essential for secure low-code AI agent development in enterprises operating in regulated environments or managing sensitive business processes. It ensures that agents act as reliable system components—not unpredictable black boxes.
Rierino supports native integration with multiple LLM providers—including OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Mistral, and more. For enterprises with strict compliance requirements or cost controls, we also support on-premise models such as Ollama, LocalAI, and other containerized LLM runtimes.
Developers can configure:
This makes it possible to optimize for cost, performance, or security, depending on the business scenario.
Agents created with Rierino aren’t tied to a single channel or surface. They can be:
This flexibility allows the same agent logic to be reused across multiple entry points, ensuring consistency without duplication.
Because everything in Rierino is built using a microservice-style architecture, the logic behind each agent action is modular, versioned, and independently deployable. This means teams can:
In essence, Rierino gives AI agents the same modular foundation that developers use to scale microservices. The result is execution that’s not only intelligent but also maintainable.
With AI Agent Builder, you're not just building chatbots or process helpers. You're embedding decision-ready intelligence directly into your operational systems—where agents can trigger workflows, enforce rules, and act with full context.
Here are a few examples of what that looks like across departments:
A finance agent receives a budget request triggered by an event or submitted through a form. It:
For the business? This means faster approval cycles, fewer errors, and policies enforced without manual policing while maintaining auditability and full control.
A support agent monitors incoming tickets through a CRM webhook. It:
This results in measurable gains in SLA performance, agent productivity, and CX quality with routing logic that’s transparent and easy to refine.
A logistics agent listens for new orders from the commerce system. It:
For operations teams, this translates to more consistent fulfillment, lower manual workload, and visibility into every step of the process.
AI Agent Builder isn’t just a capability for developers—it’s a shift in how enterprises automate, scale, and govern intelligent behavior across systems. The impact is felt across both business and technology teams, each with distinct but deeply connected priorities.
For business and operations leaders, this means finally having agents that operate within the systems you already trust. These aren’t experimental copilots with unclear outputs—they’re rule-bound, traceable actors that follow your business logic and enforce your policies. You define what they can do, monitor their actions, and adapt them as business needs evolve. This gives you scalable automation that doesn’t compromise on control, compliance, or visibility—and a real path to delivering outcomes faster.
For technology and architecture teams, it means gaining infrastructure that supports execution-first agents from day one. Agents built on Rierino aren’t orchestrated externally—they’re embedded in your service logic, with secure access to data, modular workflows, and native support for validation and versioning. You don’t need to bolt on orchestration or governance—it’s already there. And because every component is composable, extensible, and observable, you can move from prototype to production with confidence.
This is what makes AI Agent Builder different. It’s not a product on top of a platform. It’s what happens when the platform itself is built to support real intelligence from the ground up.
The future of enterprise AI isn’t about better prompts. It’s about better infrastructure.
We’re entering a new phase where agents don’t just suggest, they act. Not as external scripts or add-ons, but as trusted system components that execute logic, follow rules, and scale with your architecture. AI Agent Builder exists for this shift. It turns your internal flows, APIs, and business rules into a secure, AI-ready surface where agents can operate with full context and full control.
And because it’s built into the foundation of the Rierino platform, every workflow you have defined, every policy you have modeled, and every integration you have created becomes a capability your agents can use natively and safely.
To learn more about how this capability fits into Rierino’s broader roadmap, read the full press release. You can also explore the AI Agent Builder solution page or request a demo to see what real execution-first agents look like in action.
