Question 1

What does AI Agent Development training at SkilBrill cover?

Accepted Answer

Twelve weeks and 72 hours across eight modules: what makes something an agent, tool design and function calling, planning and termination, memory, multi-agent orchestration, the Model Context Protocol, reliability and evaluation, and a capstone agent shipped with tracing and an approval gate.

Question 2

Do I need the LLM Application Development course first?

Accepted Answer

It helps but is not required. This course assumes you are comfortable calling a model and reading JSON, and it teaches retrieval where it is needed. If you have never built an LLM feature at all, taking the Generative AI course first is the calmer route.

Question 3

What programming experience do I need?

Accepted Answer

Working Python: functions, classes, dictionaries, JSON, async patterns and exception handling. You will write tool implementations and a reasoning loop yourself, so comfort reading unfamiliar code matters more than any specific framework experience.

Question 4

Why does the course teach the Model Context Protocol?

Accepted Answer

Because tool integration is where agents get expensive to maintain. MCP gives a standard way to expose data and actions, so one server serves several agents instead of a custom integration per assistant. You build a server over a real internal API in the course.

Question 5

How is an agent different from an automation?

Accepted Answer

Automation follows a fixed path. An agent chooses its own next step from the model's reasoning, which is what makes it useful for ambiguous work and what makes it risky. One full module is about recognising when a deterministic workflow would simply be better.

Question 6

Is certification included?

Accepted Answer

The syllabus follows agentic AI developer objectives so it works as preparation for emerging credentials in this area. Vendor exams are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.

Question 7

What do I leave with?

Accepted Answer

A working agent with validated tools, bounded loops, scoped memory, an MCP server over a real API, a trace dashboard, a task-success eval suite and a human approval gate for irreversible actions.