AI Agent

Responding to increasing demand for agility, efficiency and intelligent automation, APIPASS started developing its first AI agent as a strategic evolution of the platform. The initiative leverages core elements of the existing engine to validate new capabilities through a pilot, while laying the foundation for future AI-driven experiences across the product.

While engineering focuses on implementation, my role is to define the product experience and interaction model — translating technical capabilities into a coherent, scalable, user-centred design vision that will be fully realised once the engine is finalised.

To ensure strategic alignment and market relevance, I ran benchmarking and comparative analysis across leading automation and integration platforms such as n8n, AWS and Digibee — where, as a former team member, I led initiatives in the monitoring domain — as well as best-in-class AI products including Copilot, OpenAI, Claude and Manus. Those references informed expectations around interaction patterns, transparency, user control and trust in AI-assisted workflows.

The agent space

Users interact directly with the agent to learn about the platform and request the creation of features or assets, and can ask for guidance on how to build specific solutions or workflows. Conversations can be exported and reopened at any time, and chat interactions can be turned into reusable prompts — supporting continuity and scale.

Creating an agent

This section lets users define an agent's capabilities by configuring parameters, writing custom prompts or importing existing ones. Once created, agents can be shared, edited and reused across teams. By default users start from a library of templates — either as-is or customised into a new agent. Advanced users can also build an agent from scratch with code when they need deeper technical control.