Build production-ready autonomous AI agents with tool calling, MCP, multi-agent orchestration, safety guardrails, and enterprise deployment strategies.
Last Update: Sep 29, 2024
Build production-ready autonomous AI agents with tool calling, MCP, multi-agent orchestration, safety guardrails, and enterprise deployment strategies.
Lectures :30 lessons
Duration :16:30:00
Enrolled :1 students
Course level :intermediate
Language :English
Price Discount :
Regular Price :$15
Course Status :active
This is an intermediate course. You should have experience working with LLM APIs and Python, but no prior agentic AI experience is required. The course starts with foundational concepts before progressing to advanced topics.
The course primarily uses Python, with TypeScript also covered for MCP server development and tool executor implementation. Most orchestration and agent framework examples are in Python.
MCP is an open protocol that standardizes how AI agents discover and interact with tools and resources. It enables dynamic tool discovery, authentication, and resource subscriptions, making it a key building block for scalable agentic systems.
Yes. An entire section is dedicated to human-in-the-loop patterns, safety guardrails, prompt injection prevention, permission models, auditability, and regulatory compliance frameworks for agentic AI.
Absolutely. The final section covers production deployment including observability with OpenTelemetry, CI/CD pipelines, cost and latency optimization, automated evaluation, and a production checklist for scaling to enterprise workloads.
