March Wrap-Up – Agentic AI Training at RX-M

At RX-M, we help teams move beyond experimentation and into production-ready AI systems.

As inaugural members of the Cloud Native Computing Foundation and the Agentic AI Foundation, we bring a unique, hands-on perspective to building and scaling agentic systems.

This month, we introduced a new wave of courses and major curriculum improvements – designed to help engineering teams operationalize AI faster and more effectively.


What’s New This Month?

We’ve added a lot of neew offerings, structured into clear learning pathways to reflect how real systems are designed, deployed, and scaled:

1️⃣ Agentic Fundamentals → Real Systems

From prompting to production-ready agents

  • Why prompting isn’t enough
  • Agentic design patterns
  • What breaks in production

Courses:

2️⃣ Context as Infrastructure

The backbone of reliable AI systems

  • Context engineering principles
  • State, memory, and control
  • Model Context Protocol (MCP)

Courses:

3️⃣ Agent Infrastructure & Platforms

Running agents at scale

  • Kubernetes-based orchestration
  • AI gateways and routing
  • Observability for agentic systems

Courses:

4️⃣ Security, Privacy & Governance

Making AI safe, compliant, and production-ready

  • Threat models for agents
  • Governance and safety controls

Courses:

5️⃣ Agentic Software Engineering

Building AI-native applications end-to-end

  • Designing AI-native architectures
  • Integrating agents into real products
  • Developer workflows for agentic systems

Courses:


Why this matters?

AI is about systems and, to deliver real value, teams need to master:

  • Context management
  • Orchestration and infrastructure
  • Observability and security
  • Scalable design patterns

That’s exactly what this new curriculum is built for.

Ready to Get Started? 🎯

Whether you’re just exploring agentic AI or scaling production systems, our modular training approach makes it easy to build the right path for your team.

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