The next step in intelligent automation
Contribution by Melvin Kaats, Lead Business Consultancy at LINKIT
Every organisation wants to work faster, make better decisions, and reduce costs. Yet many processes remain stuck in repetitive manual work, such as validations, checks, and reporting. Agentic AI enables organisations to delegate these tasks to digital colleagues that operate independently, from retrievingdata to performing analyses and documenting outcomes.
According to recent market data, 47 per cent of organisations already use agentic AI within their workflows, and 62 per cent of executives expect to double their ROI within a year. Organisations that start smart quickly discover where automation delivers value and where human expertise remains essential.
What is an execution?
Each time an agent performs a task, this is called an execution.
Examples include:
- Performing a validation on data fields
- Merging data from multiple systems
- Generating or updating a report
An agent may use a tool within an agentic AI environment, such as the OutSystems AI Workbench. A tool enables the agent to perform a specific action, such as retrieving data, performing calculations, or updating a database.
Example:
Suppose an agent automatically verifies customer information in a CRM database. It retrieves customer numbers, addresses, and contact details, compares them with the latest data, and updates records where necessary. The entire process, from retrieving data through the tool, executing the validation, and updating the records, counts as one execution.
Each execution consumes computing power, memory, and data access. In agentic low-code environments such as the OutSystems AI Workbench, this becomes fully transparent. You can see what an agent does, how often it operates, and what that means in terms of budget and value creation.
One agent, one task, one execution
A simple rule maintains clarity: one agent performs one task. Each time it runs, it counts as one execution, including all internal steps such as data retrieval, intermediate processing, and returning results.
This makes budgeting transparent and prevents hidden costs.
Example:
A service manager uses an agent to assess incoming support tickets for urgency. The agent retrieves context, compares historical cases, and assigns a priority level in the system.
Result:
- Reduced waiting time for customers and users
- Less manual triage work
- Direct insight into usage, for example, 100 tickets assessed equals 100 executions
Transparent automation
By measuring executions, organisations gain transparent automation. You know exactly where resources are allocated and how that relates to the value delivered by an agent.
This provides:
- Insight into return per task
- Clear visibility into capacity
- Predictable cost development
Start small and learn fast.
Successful organisations do not begin with large-scale programs. They start with a focused use case. One clearly defined task, such as validating forms, consolidating reports, or detecting anomalies.
Thanks to the speed of low-code environments, such an agent can be operational within days. Results follow quickly:
- How much manual work is replaced
- How much time and how many errors are reduced
- What the actual execution costs are
These insights form the foundation for further scaling. What delivers value is expanded. What is not adjusted? Automation grows in a controlled and profitable way.
Real-world examples such as Axos Bank and Thermo Fisher Scientific show that organisations significantly improve operational efficiency and reduce human workload by automating repetitive tasks with the Agent Workbench.
Why this matters now
Agentic AI is mature, secure, and increasingly accessible. Low-code agentic environments such as the AI Workbench in OutSystems combine governance, monitoring, orchestration, and integration within one platform.
Organisations that start now with small and measurable steps build a sustainable competitive advantage. From day one, they gain insight into costs, value, and scalability.
