Agentic AI in 2026 is not just “prompt engineering.” It is the engineering discipline of building AI systems that can reason over a goal, call tools, retrieve context, coordinate workflows, recover from failures, involve humans when needed, and produce auditable outcomes. The practical stack now includes agent runtimes, tool protocols, observability, evaluation, safety, and deployment infrastructure. OpenAI Agents SDK supports tracing across LLM calls, tool calls, handoffs, guardrails, and custom events; LangGraph focuses on durable execution, streaming, persistence, and human-in-the-loop control; MCP standardizes how AI applications connect to external tools and data sources; Google ADK supports prompt/tool agents, multi-agent orchestration, graph workflows, evaluation, and deployment.
↳ A single-agent assistant with tools
↳ A RAG-powered agent with citations
↳ A multi-agent workflow with a planner, executor, verifier, and human review
↳ A production-grade agent with tracing, evaluation, retries, guardrails, and cost monitoring
↳ A portfolio-ready Agentic AI system you can explain in interviews
