Anastasios Stamoulakatos

Anastasios Stamoulakatos

Data Scientist at Satalia | PhD in Applied AI

Creative AI: Evaluating and Ranking AI Creativity

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Researched multi-agent collaboration strategies and LLM-as-a-judge paradigms to evaluate and rank creative outputs. Benchmarked agent architectures (CrewAI, AutoGen, LangGraph) on creative marketing tasks.

Contributed to internal R&D and technical reports published by WPP Research.

Agentic Data Guardian: Enterprise Data Quality Management

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Designed and built AI multi-agent workflows to automate the detection and remediation of data quality anomalies in enterprise databases. Implemented Google ADK and AutoGen agents that continuously validate and update schemas, keeping model answers grounded and reducing hallucinations.

Retrieval-Augmented Generation (RAG) 101

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Retrieval-Augmented Generation (RAG) bridges the gap between static LLM parameters and dynamic external data. In this guide, I break down the core components of a RAG pipeline—from document chunking and vector embeddings to context retrieval and generation—and explain how real-time tools like Web Search integration keep model answers accurate and grounded.

Agentic Frameworks: A Comparison

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Selecting the right framework is crucial when moving from prototype agents to production. This article compares popular agentic development frameworks (such as CrewAI, AutoGen, and LangGraph), analyzing their design philosophies, state management capabilities, and developer ergonomics.

Building Effective AI Agents: A Brief Guide and Best Practices

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AI agents are redefining how we automate workflows. Building reliable agents requires more than just calling an LLM in a loop; it demands robust architectures for task planning, execution monitoring, tool access (like web search), and evaluation.