Exploring Compass: An AI Conversational Agent for Personalized Skill Discovery

In the rapidly evolving landscape of generative AI applications, Compass is designed to engage users in exploratory conversations to gather information and identify their unique skills, focusing on abilities over credentials, including informal and unpaid work. This tech-focused blog post dives into how Compass leverages agentic workflows and other AI techniques to offer a personalized and engaging user experience and how Compass utilizes Tabiya’s Inclusive Taxonomy to discover skills from both the formal and the unseen economy.

Agentic Workflows That Mimic Human Conversations

At the core of Compass is its ability to mimic human conversational patterns. An overarching agent breaks down a conversation for smaller, specialized agents, each with specific responsibilities and goals. This multi-agent system allows Compass to approach conversations and tasks much like a human would. For instance, one agent might engage in dialogue to gather specific information, then process that information and pass it to another agent for further use.

Each agent maintains an individual internal state across user interactions, enabling it to apply strategies to accomplish its goals effectively. Agents have access to the user’s conversation history and various tools based on Large Language Model (LLM) prompts, and they offer interfaces for other agents to interact with them.

The conversation process consists of multiple stages:

1. Introduction: The user is greeted and asked if they would like the process to start. Alternatively, the user can ask questions or general clarifications about the process.
2. Experience Gathering: The user provides basic information about their work experiences, including roles, locations, and periods. The agent prompts for all types of experiences, including unpaid work, informal work, and volunteering, ensuring a comprehensive view of the user’s skills.
3. In-depth Exploration: Compass dives into each experience with open-ended questions, encouraging the user to discuss their work in detail. This helps the system accurately extract user’s responsibilities in each position and subsequently link them to the relevant skills.
4. Skill Identification: Finally, Compass processes all the information obtained in the previous steps to present a list of skills associated to the user’s original experiences.

Compass leverages an LLM to drive the conversation, allowing for natural and adaptive interactions. However, it remains focused on the task, like a professional interviewer, while showing empathy and gracefully handling unexpected inputs. Compass attempts to maintain a balance between open conversation and setting boundaries to achieve the tasks at hand.