Introduction
The rise of agentic AI is transforming how companies automate processes. Instead of relying on rules, scripts, or complex integrations, you work with AI agents that understand context and perform actions autonomously.
OutSystems now makes this accessible with the AI Agent Workbench.
The result: intelligent workflows that can be built far faster than traditional automation. But one thing stands out across almost every organization: they see the potential, yet struggle to identify the use cases that actually deliver value.
The Challenge
Many organizations are currently exploring how to apply AI, but this interest often remains stuck at conceptual discussions or proofs of concept without real outcomes. IT teams focus on technical possibilities, the business is drawn to the hype, but clarity is missing about where to begin and how to turn innovative ideas into production-ready agentic AI solutions.
As a result, agentic AI becomes either too abstract or is pushed too quickly in the wrong direction, leading to missed opportunities, unclear ROI, and delays.
A second challenge is trust and governance. If you cannot safely control, oversee, and monitor agents, they will never be widely adopted.
A third challenge is integration and scalability. Agents must work seamlessly across systems and processes throughout the entire organization, rather than being isolated in silos.
Technology Explained: OutSystems AI Agent Workbench
The AI Agent Workbench makes it possible to define agents that perform tasks autonomously, make decisions based on context, and orchestrate multiple systems. In OutSystems, this combines three major strengths:
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Low-code speed for orchestrating workflows.
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GenAI-powered agents that interpret data, create plans, and execute actions.
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Native integrations with existing OutSystems applications and external systems.
Where building a process with conditional logic, integrations, and front-end elements once took weeks, an agentic workflow can now run within days.
Examples of High-Value Agentic Workflows
Here are real enterprise use cases where agentic AI is already delivering measurable results:
- Incident triage for IT support
An agent automatically gathers logs, analyzes incidents, finds relevant knowledge base articles, and proposes an initial solution. MTTR decreases, and support teams are relieved of repetitive work. - Smart onboarding for customers or employees
An agent requests missing documents, verifies data, triggers next steps, and guides users through a personalized flow. Less manual work, fewer errors. - Proactive customer service
An agent monitors customer behavior, identifies risks or opportunities, and automatically initiates actions such as outreach, compensation offers, or escalations. - Field service decisioning
An agent advises technicians using sensor data, maintenance history, and parts availability to determine the most efficient action. - Intelligent knowledge retrieval
An agent searches across scattered documentation, systems, and emails to assemble a complete answer employees can use immediately.
These are the kinds of workflows that deliver direct, tangible value: lower costs, higher speed, and improved customer experience.
Why Organizations Struggle to Identify the Right Use Cases
The technology isn’t the bottleneck. Choosing is.
Organizations often get stuck because:
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They lack clarity on where value truly lies, processes are fragmented, and ownership is unclear.
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They don’t know what data is available or usable for agentic workflows.
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They aim too high, trying to build “enterprise-wide AI” instead of starting small where value is obvious.
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They’re unsure about risks and ROI; AI feels new and uncertain.
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Business and IT don’t speak the same language; everyone sees opportunities, but not the same ones.
In short, the issue isn’t what is possible, but where to begin.
Conclusion
Agentic workflows are no longer futuristic; OutSystems makes them concrete, fast to build, and applicable today. Organizations that start now will gain a clear competitive advantage. But that advantage doesn’t begin with technology; it starts with choosing the right use cases.
That’s why the first step is simple:
identify the best opportunities together before you start building.