Introducing OutSystems AI Agent Builder: Streamlining Enterprise AI Development

As artificial intelligence (AI) reshapes business, enterprises strive to integrate generative AI for efficiency and innovation. OutSystems, a leading low-code platform, introduces AI Agent Builder—a powerful tool simplifying AI agent creation for professional developers. This article highlights how AI Agent Builder accelerates development, transforming ideas into production-ready agents in days, empowering professionals to drive digital transformation.

Foundation and Integration

Introduced in 2024 and enhanced in 2025 with Agent Workbench, AI Agent Builder offers a visual drag-and-drop interface, enabling developers to build autonomous AI agents without deep AI expertise. Agents process inputs, reason, and execute tasks via tool invocation.

Its integration is a standout feature. The tool connects to large language models (LLMs) like OpenAI or Google’s Vertex AI and supports custom models from Hugging Face or AWS Bedrock. Retrieval-Augmented Generation (RAG) allows agents to access enterprise data, including databases and SaaS platforms like SAP. The Model Context Protocol (MCP) ensures accurate API calls for context-aware responses. For Hong Kong enterprises, this supports localized data while meeting privacy regulations like the PDPO. Agent Workbench manages the full lifecycle, from prototyping to monitoring, with real-time performance metrics.

Core Capabilities

AI Agent Builder focuses on three areas: agent creation, orchestration, and governance.

Agent creation offers a click-based experience with marketplace templates for rapid customization. Developers can design workflows—e.g., one agent retrieves data, another generates reports. Human-in-the-Loop ensures oversight, aligning outputs with business needs, suitable for tasks like lead qualification.

Collaborative orchestration enables agent interactions. Agent Workbench’s visual mapping supports automation, like HR processes where one agent screens resumes and another handles onboarding. OutSystems Data Fabric unifies data, enhancing insights for personalized marketing. This speeds development, freeing teams for strategic work.

Governance validation ensures reliability. Security controls and compliance checks mitigate risks. High Availability supports scalable deployments, while analytics track performance. The Agent Marketplace provides pre-built agents, accelerating iteration.

Real-World Impact

AI Agent Builder shines across industries. A European bank automated compliance, integrating LLMs with policy data, cutting approval times by 70%. Retail agents deliver real-time product recommendations, boosting conversions. In Hong Kong, a supply chain firm built procurement agents, integrating with SAP to monitor risks. Healthcare agents manage patient queries, reducing administrative burdens. These cases show automation and revenue potential, like identifying sales opportunities.

Benefits for Professionals

AI Agent Builder’s low-code agility shortens development from months to days, using reusable components. It reduces technical debt, unifies governance, and future-proofs AI strategies, enhancing productivity and ROI for enterprises.

Conclusion

OutSystems AI Agent Builder redefines AI development with a no-code interface and robust integrations, enabling professionals to build intelligent agents for complex challenges. Hong Kong professionals should try OutSystems’ free version to unlock its potential. Share your insights below, and let’s explore low-code AI’s future!

More Updates

Leveraging OutSystems' rapid development strengths, our team achieves true Agile development, focusing intensely on user requirements. However, requirements are never fixed; they take time to refine within the project's cycle. If a developer simply builds projects based on the initial requirements, it leads to significant rework when those requirements are inevitably revised. This creates serious technical debt that can derail a project's schedule. To combat this, we strictly follow the OutSystems Canvas Design architecture to define each module's usage and content. We generalize logic into foundational modules, optimizing reusability and providing high adaptability when requirements change. This approach allows us to eliminate complicated dependencies—avoiding the deployment nightmares that plague monolithic systems. The Real-World Challenge: "The Spaghetti Monolith" We’ve all seen it. A project starts fast. The "Idea-to-App" time is record-breaking. But as sprints pass and requirements evolve, the "interest rate" on technical debt spikes. Suddenly, changing a simple UI element breaks a core business process because the logic was trapped inside the screen. Deployment becomes a "big bang" event where everything must go live at once because of circular dependencies. In our team, we don't just "code fast"; we architect for resilience. Our Solution: The 4 Layer Canvas Strategy We treat the 4 Layer Canvas not just as a suggestion, but as our structural imperative. Here is how we use it to handle volatile requirements:  Isolating Volatility (End-User Layer): We keep our User Interfaces (UI) and interaction logic in the End-User Layer. This layer is highly volatile—it changes constantly based on user feedback. By isolating it, we can redesign a "Customer Portal" without risking regressions in our core business rules.Stabilizing Business Logic (Core Layer): We abstract our entities and business rules into the Core Layer. This is the backbone of our factory. Whether the data is accessed by a Mobile App, a Web Portal, or a Timer, the validation rules remain consistent. This promotes the "Don't Repeat Yourself" (DRY) principle.Enabling Independent Deployments: By using Service Actions (Weak Dependencies) in our Core layer, we decouple our modules. This allows different squads to deploy changes independently without forcing a factory-wide refresh—a critical enabler for our CI/CD pipelines.The Governor: AI-Driven Architecture How do we ensure we stick to these rules when moving at Agile speeds? We don't just rely on manual code reviews; we use the AI Mentor System. This tool acts as our automated architect. It scans our entire factory to detect architectural violations that humans might miss, such as: Upward References: Preventing foundational libraries from depending on business logic.Side References: Ensuring our End-User apps don't tightly couple with one another.Circular Dependencies: Identifying the "deadly embrace" between modules that locks deployments.The AI Mentor System quantifies this debt, allowing us to pay it down proactively before it hinders our release velocity. Join a Team That Values Architecture In our Taiwan office, we believe that low-code doesn't mean "low-architecture." We are building resilient, composable enterprise ecosystems that can scale. If you are a developer who cares about structural integrity, clean code, and mastering the art of OutSystems architecture, we want to hear from you. 

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Germany's Data Protection Conference just released comprehensive guidance on AI systems with Retrieval Augmented Generation (RAG) - a game-changer for organizations implementing AI governance under ISO 42001.Key Compliance Requirements:🔎Data Accuracy - Enhanced Large Language Model (LLM) responses but error accountability remains🔎Transparency - Improved document traceability within RAG knowledge bases🔎Purpose Limitation - Technical implementation through client/functional separation🔎Data Minimization - Strategic vector database content management🔎Data Subject Rights - Full rights coverage across prompts, outputs, and databases👉 Why This Matters for ISO 42001:RAG systems are becoming mainstream for internal chatbots and enterprise AI. The Data Protection Conference guidance directly aligns with ISO 42001's requirements for AI risk management, data governance, and algorithmic accountability.Organizations deploying RAG technology must now ensure their AI management systems comply with both German data protection standards and international ISO 42001 frameworks.Our cybersecurity and privacy consultation expertise helps organizations navigate these complex requirements, ensuring your RAG implementations meet regulatory standards while maximizing business value.