Google Cloud Next Building Employee Assistants with Vertex AI session:

https://cloud.withgoogle.com/next/session-library?session=AIML156&utm_source=copylink&utm_medium=unpaidsoc&utm_campaign=FY24-Q2-global-ENDM33-physicalevent-er-next-2024-mc&utm_content=next-homepage-social-share&utm_term=-

Creating employee assistants has been simplified with Google Cloud’s Agent Builder and Vertex AI. Through this platform, various types of agents can be developed; For example code agents (for coding tasks), marketing agents (for designing marketing campaigns tailored to specific customers), customer agents (for assisting in customer interactions), and knowledge agents (for helping to get the right information from the data).

Google Cloud endeavors to assist employees in achieving their most important goals through the Agent Builder by addressing the most important concerns of enterprise organisations:

1. Compliance and responsible AI, ensuring enterprise readiness to prevent data leakage, provide data governance, maintain data confidentiality, identify data sensitivity, and monitor data usage.
2. Customisation and personalisation, with integration into enterprise systems facilitated through unified interfaces, aiding in personalised task management.
3. Factual accuracy, optimising AI responses by minimising inaccuracies and simplifying data complexities.

The Agent Builder suite provides a comprehensive set of tools not only for building diverse agents but also for effortlessly integrating them into a unified system and a variety of messaging tools. This caters to users with different levels of data analytics expertise:

• In a low-code environment, agents can be quickly set up with minimal effort, personalised using NLP, and easily put into production. Quality assurance is supported through console simulators and testing capabilities before deployment to platforms like Google Chat or Slack.
• In a full-code environment , users have the flexibility to utilise their own code and Vertex AI capabilities, securely deploying applications on Vertex AI endpoints.
• Integration with enterprise data sources has been also made easy and secure, whether from cloud storage, BigQuery, public websites, or native integrations like Confluence or Hadoop file systems. Users can use Vertex AI Search – Let Vertex AI Search do the job for you. An out-of-the-box solution for data processing, indexing, ranking, and validation. Or use RAG Systems – a do-it-yourself option, leveraging Vertex AI APIs for annotation, document processing (e.g. complex PDF or .doc files), search capabilities, and ranking.
• Compliance and responsible AI, ensuring enterprise readiness to prevent data leakage, provide data governance, maintain data confidentiality, identify data sensitivity, and monitor data usage.