The anatomy of a Relevance AI agent | December ‘23
AI Summary
Summary: Anatomy of a Relevance Agent
- Introduction
- Relevance assists businesses in creating AI workforces.
- Digital workers are trained on company-specific processes and knowledge.
- Cognitive Abilities
- Short-Term Memory
- Agents remember conversation topics and continue discussions.
- Knowledge Importation
- Agents learn by importing data, such as survey responses or FAQs.
- Relevance began as a vector database in 2020.
- No-Code Builder
- Users can create LLM chains, functions, and API requests without coding.
- Skills: Processes learned through experience, like optimizing blog posts for SEO.
- Tools: Access to services like HubSpot or Google Search.
- Agent Functionality
- Task-Oriented
- Agents complete scoped tasks and report activities for management oversight.
- Process-Oriented
- Agents follow outlined processes, similar to SOPs.
- Accessibility
- Agents can use email and will soon use services like WhatsApp.
- Multi-Agent Systems
- Agents can collaborate with each other and humans, seeking input or approval.
- Future Planning
- Agents can schedule and cancel future actions.
- Work Analysis
- Agents categorize their work for better management and reporting.
- Conclusion
- Relevance is developing new cognitive abilities for agents.
- Dan from Relevance anticipates sharing more updates in the future.