Agentic AI training teaches teams how to build, configure, and stress-test AI agents before those agents reach production. It covers model selection, system prompts, agent instructions, and MCP server connections, and runs agents against realistic scenarios so teams can see how they behave rather than assume.

An AI agent sandbox is an isolated environment where an agent runs against realistic tasks with no connection to development or production systems. Practitioners work with real AI development tools and API keys that never leave the lab environment.

You run it against a defined scenario and review what it actually did. Agent Tuning produces an execution audit trail for every run covering tool calls, latency, token usage, and exact spend, which teams use to refine the model, prompt, and instructions before running it again. You can also import your own agent skills, as well as export skills built within the sandbox.

Agentic AI security covers the practices that stop autonomous agents becoming an attack path or an attack surface, spanning guardrails, agent identity, and governance. Immersive builds these skills through AI red and blue teaming mapped to MITRE ATLAS, agent identity built on OAuth 2.1, and content aligned to OWASP Agentic Security.

It depends on the model, the prompt, and how many tool calls the agent makes to finish a task, which makes cost hard to estimate up front. Every run logs token usage and exact spend, so teams understand what agentic work costs before it reaches live environments.

No. 16 collections cover roles across the organization, including practitioners who'll never write code. Developer and AppSec teams work in Developer AI Ranges, red teams work in Offensive AI Ranges, and the AI Knowledge Pass builds baseline AI literacy across the wider workforce.