CC BY-NC 4.0Lakshmana Murthy, SwethaSwethaLakshmana MurthyPanneer Selvam, SangamithraSangamithraPanneer SelvamWelß, MartinMartinWelß2025-11-122025-11-122025-10-21https://publica.fraunhofer.de/handle/publica/499106https://doi.org/10.24406/publica-623410.3233/FAIA25144010.24406/publica-6234Developments in Artificial Intelligence (AI) today have sparked growing interest in topics such as agentic workflows and Long-Term Memory (LTM) architectures, which extend the capabilities of Large Language Models (LLMs) beyond their current limitations. Agentic workflows represent a significant paradigm shift by enabling LLMs to exhibit goal-oriented behavior, decision-making, and adaptability within dynamic environments. On the other hand, LTM systems enable LLMs to retain and retrieve information across multiple interactions, allowing for personalization, context-aware reasoning, and additional functionalities. This paper elucidates two illustrative use cases, namely the Agentic Event Planner and MemoryGraph, which exemplify the integration of agentic workflows, long-term memory, LLM switching, and related elements into cohesive, hybrid AI pipelines for experimentation purposes. Implemented on the AI-Builder platform, these prototypes benefit from modularity, reusability, and a user-friendly drag-and-drop design environment, with modest orchestration complexity compared to the flexibility and interoperability afforded by the platform.enArtificial IntelligenceAILong-Term Memory architecturesLTMLarge Language ModelsLLMsconference paper