Hello everyone,
I’m Yasin from Istanbul. I’m a computer engineer currently working on contact center technologies (voice and chatbot systems), and this year I’m focusing on advancing my work into agentic AI and RAG-based systems.
I’ve been building a PoC-level agentic AI project where I combine:
- structured tool orchestration (CRM, network diagnostics, etc.)
- a basic RAG pipeline (pgvector-based retrieval)
- Think → Act → Observe decision loops
- chatbot + voice AI scenarios (call center use cases)
My goal is not only to build the system, but also to produce a PoC-level technical paper by the end of Q2–Q3.
Here are my current OKRs:
• By the end of Q1, define at least two decision scenarios (tool usage, fallback, or human handoff) using the Think → Act → Observe cycle (partially completed)
• By the end of Q2, prepare a conceptual technical document covering the RAG pipeline (data collection, chunking, embedding, retrieval, answer generation) for chatbot and voice AI scenarios
• By the end of Q3, design a complete end-to-end call center AI architecture including:
- agentic orchestration
- RAG
- voice/chat interfaces
- infrastructure
- observability
• By the end of Q4, define a basic observability approach (logging, metrics, latency, fallback rates, AI usage per call)
At this point, I’m looking for guidance on:
- Which courses or learning paths (DeepLearning.AI or others) would best support building production-oriented agentic AI + RAG systems?
- Recommended resources specifically for:
- tool-based agents vs RAG decision design
- evaluation of retrieval quality
- observability in LLM systems
- Any suggestions on how to structure a PoC-level technical paper for this kind of system
I’m not aiming for purely academic work — I want something practical, engineering-focused, and close to real-world production systems.
Thanks in advance for your guidance!