Agentic AI Engineer | Skills, Tools & Architecture for Building AI Agents

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Издатель Aug 12, 2026

In this video by Uplatz, we explore the role of an Agentic AI Engineer and the skills, tools and architectures required to build AI systems that can reason, plan, use tools and execute multi-step tasks with limited human intervention.

This video explains how Agentic AI differs from traditional Generative AI and how engineers design reliable agent workflows using Large Language Models, memory, retrieval, APIs, tools and orchestration frameworks.

Key topics covered include:

• What is Agentic AI?
• Role of an Agentic AI Engineer
• AI agents versus traditional LLM applications
• Reasoning and planning
• Tool calling and function calling
• Multi-step workflows
• Memory and context management
• Retrieval-Augmented Generation
• Vector databases
• Embeddings and semantic search
• Agent orchestration
• Multi-agent systems
• LangChain and LangGraph
• LlamaIndex
• CrewAI and AutoGen
• Model selection and routing
• Structured outputs
• API and external tool integration
• Human-in-the-loop workflows
• Agent state management
• Long-running workflows
• Guardrails and safety controls
• Agent evaluation and testing
• Observability and tracing
• Failure handling and retries
• Cost and latency optimization
• Security and permissions
• Deployment and scaling
• Enterprise agent architecture
• Skills required for Agentic AI Engineering

An Agentic AI Engineer combines software engineering, AI application development and systems architecture to build agents that can interact with external tools, retrieve information, make decisions and complete complex workflows.

Reliable agent systems require much more than prompting. Engineers need to manage state, control tool permissions, handle failures, evaluate agent behaviour and design workflows that remain predictable even when models produce uncertain outputs.

Multi-agent architectures can also divide complex tasks between specialised agents, but they introduce additional challenges around coordination, communication, cost and observability.

This video is useful for AI Engineers, Machine Learning Engineers, software developers, MLOps professionals, solution architects and anyone interested in building production-ready AI agents and agentic applications.

Subscribe to the Uplatz YouTube channel for more videos on Agentic AI, Large Language Models, Generative AI, AI engineering, MLOps and enterprise technology.

#AgenticAI #AIEngineer #AIAgents #GenerativeAI #LLM #LangGraph #LangChain #MultiAgentSystems #RAG #AIEngineering #EnterpriseAI #Uplatz

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