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# Building Agentic Applications: A Deep Dive into Zooka, an AI Cardiologist Assistant

Are you building true AI agents or just glorified RAG? Learn to build an autonomous AI cardiologist using the orchestrator-worker pattern, ReAct framework, and long-term memory.

- **Speakers:** [Ahmad Adel](https://www.wearedevelopers.com/@ahmad-adel)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 27:54
- **URL:** https://www.wearedevelopers.com/videos/2030-building-agentic-applications-a-deep-dive-into-zooka-an-ai-cardiologist-assistant

## Summary

Distinguishing true agentic workflows from simple retrieval-augmented generation (RAG) or fixed prompt sequences requires a focus on autonomous decision-making. True agents do not just follow hard-coded paths; they leverage reasoning and planning to navigate complex enterprise workflows with minimal human intervention. A foundational architecture for this is the orchestrator-worker pattern, where a central orchestrator decomposes a primary goal, delegates specific tasks to specialized worker agents, and synthesizes their outputs into a cohesive final result.

Building an effective agent requires four core components: a hosting runtime, a large language model, an orchestration engine, and executable tools. The orchestration engine acts as the agent's brain, utilizing the ReAct (Reason, Act, Observe) framework to iteratively process tasks. To maintain context, this orchestrator relies on a hierarchy of memory, ranging from turn-based scratchpads capturing internal monologues to short-term session histories and persistent long-term memory banks. Furthermore, integrating these agents with external tools and databases is streamlined by the Model Context Protocol (MCP), which acts as a standardized, universal connector for AI applications.

These theoretical concepts are practically applied using the Agent Development Kit (ADK) to construct Zuka, an AI cardiologist assistant hosted on Google Cloud. The application demonstrates multi-modal data retrieval by combining vector searches for matching patient symptoms with graph searches to map those symptoms to specific diseases and diagnostic procedures. Crucially, the demo highlights how agents persist critical user information in a long-term memory bank, enabling seamless, context-aware continuity across entirely separate user sessions.

**Keywords:** agentic workflows, orchestrator-worker pattern, autonomous decision-making, retrieval-augmented generation, react reasoning loop, agent scratchpad memory, session-level memory, long-term memory persistence, MCP integration, LLM orchestration, langchain, langgraph, agent development kit, hybrid database queries, vector search optimization, graph database routing

## Chapters

1. **Defining agentic workflows versus traditional generative AI applications** (00:00) — Distinguishing true agentic autonomy from basic prompting, retrieval-augmented generation, and fixed sequences.
1. **Implementing the orchestrator-worker pattern in agentic workflows** (02:17) — How delegating tasks among specialized agents drives lower costs and higher accuracy in enterprise processes.
1. **Applying the ReAct framework for continuous reasoning and acting** (04:41) — How agents use a continuous loop of thought, action, and observation to automate complex enterprise tasks.
1. **Choosing the right runtime environment for hosting AI agents** (06:23) — Understanding the trade-offs between managed services and custom platforms for agent logging, tracing, and memory management.
1. **Managing working, short-term, and long-term memory in orchestration** (08:34) — How the orchestration layer handles internal monologues, session history, and persistent facts across multiple user turns.
1. **Integrating foundation models and specialized tools for agent actions** (11:50) — Extending the capabilities of multimodal large language models using functional execution tools and natural language database queries.
1. **Standardizing tool integration with the Model Context Protocol** (13:29) — Using an open protocol to create a unified API interface between AI agents and external data systems.
1. **Evaluating frameworks and abstraction levels for building AI agents** (14:54) — Comparing development approaches from low-level LangChain protocols to higher-level frameworks like LangGraph and the Agent Development Kit.
1. **Configuring the Zuka cardiologist assistant using the Agent Development Kit** (17:29) — Examining the Python codebase to define agent profiles, connect tools, and establish a remote server session.
1. **Demonstrating medical diagnostics and long-term memory persistence in Zuka** (21:54) — Interacting with the agent interface to process patient symptoms, search knowledge graphs, and recall medical history across sessions.

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