Learn to build autonomous AI agents

Python, Git, Linux, CLI, networking, HTTP, REST APIs, Docker, SQL, testing, and CI/CD — the engineering base every AI builder needs.

What GenAI is, how LLMs work, tokens, embeddings, prompt engineering, and core concepts — without repeating RAG or tool-calling deep dives.

Neural networks, attention, encoders/decoders, BERT, GPT, KV cache, RoPE, MoE, and quantization — interview-depth ML intuition. Skip if you are focused on building agents, not training models.

Hands-on with OpenAI, Claude, Gemini, Ollama, open-source models, streaming, and multimodal APIs.

Document loaders, chunking, vector DBs, hybrid search, re-ranking, LangChain, ChromaDB, and Streamlit demos.

What agents are, how they work, planning, reasoning, reflection, and building your first agent without frameworks.

Working, short-term, long-term, semantic, and episodic memory — one of the biggest interview topics for production agents.

Function calling, JSON mode, structured outputs, tool registries, permissions, and building a tool-using assistant.

MCP architecture, clients, servers, resources, tools, prompts, transport, and integrating MCP with agents.

LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, AutoGen, PydanticAI, and the emerging framework landscape.

ReAct, Plan & Execute, Reflexion, Tree of Thoughts — learned after building agents, not before.

A2A protocol, supervisor/worker patterns, swarm intelligence, coordination, and multi-agent projects.

LangSmith, Phoenix, OpenTelemetry, trajectory evaluation, hallucination detection, and regression testing.

Prompt injection, jailbreaks, PII detection, content safety, tool restrictions, and human approval flows.

FastAPI, Docker, Kubernetes, async agents, queues, streaming, scaling, monitoring, and cost optimization.

Playwright, browser automation, computer use, form filling, and web navigation agents.

STT, TTS, realtime voice, and agents that understand images, audio, video, PDFs, and screens.

Fine-tuning, LoRA, QLoRA, PEFT, inference optimization, distillation, and reading research papers.

Enterprise RAG, knowledge bases, RBAC, compliance, identity, audit logs, and human approval at scale.

GitHub agents, PR review, bug fix, documentation, CI/CD, and terminal agents.

Production-ready portfolio projects — AI software engineer, research assistant, customer support, and more.

Agent system design, LangGraph coding, MCP design, multi-agent design, memory design, and mock interviews.