As businesses increasingly rely on AI-driven solutions, agentic apps emerge as powerful autonomous applications capable of complex decision-making with minimal human intervention. However, this innovation brings challenges: fragmented tools, inconsistent protocols and compatibility issues hinder their potential.
Our whitepaper, "Architecting Agentic Apps," explores the key components of agentic apps—large language models (LLMs), memory systems, goal-setting mechanisms—and presents a roadmap to create a standardized ecosystem. Through this whitepaper you will learn about:
- Core Components: Agentic apps rely on key elements like large language models (LLMs), memory systems, goal-setting mechanisms and advanced planning, enabling them to perform complex tasks autonomously and learn from interactions.
- Challenges: Major obstacles include fragmented tooling, security and ethical concerns, and scalability limitations. Interoperability issues complicate integration and autonomous data handling raises security and compliance concerns.
- Importance of Standardization: Current fragmentation in tools, protocols and data models limits agentic apps' efficiency and integration. Standardizing these elements is essential for seamless interoperability, scalability and reliability.
- Proposed Solutions:
- Standard Protocols and Data Models: Unified communication protocols and a shared data model will improve compatibility and data accuracy
- Certified Tool Directory: A centralized, certified tool directory will ensure quality, security and compliance
- Common Runtime Environment: A shared runtime will support efficient deployment across infrastructures
- Examples and Vision: Inspired by industry standards like Kubernetes, FHIR and ISO 20022, the whitepaper advocates a collaborative, adaptive approach to foster innovation while ensuring reliability in the agentic apps ecosystem
