Generative AI is rapidly transforming industries and driving innovation. AI agents, leveraging the power of generative AI large language models (LLMs), are advancing automation by performing autonomous actions in areas traditionally requiring human intervention. However, the complexity of agentic workflows increases significantly with modern use cases involving multiple agents, as inter-agent communication multiplies this complexity.
This session focuses on practical implementations and ideas for streamlining these workflows, specifically agent-to-agent communication. It proposes a standard API structure to simplify agent development and implementation, demonstrating its application with live examples. The session also aims to establish semantic guidelines for agent development, promoting easier integration, efficient inter-agent communication, and self-discovery of agent capabilities. This session will use real-world agent use cases to illustrate the benefits of standardization and highlight common agent development complexities. It is designed for beginner to intermediate audiences, requiring no prior AI or agent implementation experience.

Target Audience:
- Backend API Developers/Engineers
- Data engineers
- AI and Agent developers

Key Takeaways:
1. Ideas for standardizing API design for agentic workflow: Ideas on what a standardized API schema or structure would look like for efficient agent orchestration.
2. Advantages with standardizing API design for AI agents: including use cases for wider market adoption
3. Sample real-world use cases: where standardizing the API schema for agent communication would be beneficial