Understanding AI Agents and Agentic AI

[FirstSky]

Understanding AI Agents and Agentic AI
Published 10/2026
Created by Skillmint .
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 62 Lectures ( 3h 53m ) | Size: 3.3 GB

Design, evaluate and secure AI agents from primary sources: workflows, tools, MCP, memory, multi-agent and oversight​

What you'll learn
Decide whether a task needs an agent, a fixed workflow or a single LLM call, using one test: who decides the next step
Choose between chaining, routing, parallel, orchestrator-worker and evaluator patterns based on the condition that makes each one work
Design agent tools and MCP servers as contracts, with clear names, actionable errors and the right amount of consolidation
Manage context and memory with just-in-time retrieval, compaction, notes and sub-agents, and treat memory as an attack surface
Judge when a multi-agent system is worth its token cost, and whether a manager or a handoff design fits the job
Build agent evaluations that grade the final state, not the agent's claim, and choose correctly between pass@k and pass^k
Contain prompt injection by design: break one leg of the trifecta, limit agency and sandbox before you filter
Decide which actions need human approval without causing approval fatigue, and run a structured agent design review

Requirements
Basic familiarity with LLM chat tools such as ChatGPT or Claude. No machine learning or maths background is needed
Helpful but optional: comfort reading short code snippets and JSON, to follow the demos
No agent framework or SDK experience required. The course is framework-free by design

Description
Disclaimer: This course contains the use of artificial intelligence

Learn the conditions, not the frameworks. Agent frameworks get rewritten every few months. The design decisions underneath them do not. This course teaches those decisions, taught straight from the primary sources: Anthropic's and OpenAI's engineering guidance, the Model Context Protocol specification, OWASP security guidance, and research such as ReAct, Reflexion, SWE-bench and τ-bench.

Every lesson follows the same shape: two defensible designs, and the condition that decides between them. Should this be an agent or a fixed workflow? Should you consolidate tools or minimise them? Retrieve context up front or just in time? One agent or several? You will leave with a test for each, not a slogan.

What the course covers
What an agent is (and isn't): the loop, who ends it, and three reasons to say no.

Workflow patterns: chains, routes, fan-out, orchestrators, evaluators and ReAct.

Tools and the agent-computer interface: tools as contracts, naming, errors, and code instead of tool calls.

Model Context Protocol: hosts, clients and servers, who controls what, and what the spec actually requires.

Context and memory: context as a finite resource, compaction, notes, sub-agents, and memory poisoning.

Multi-agent systems: what they really buy, manager vs handoffs, and delegation as a trust boundary.

Evaluating agents: grading final state, pass@k vs pass^k, and building an eval set.

Security: prompt injection, the trifecta, excessive agency, and containment.

Humans in the loop and long-running agents: approvals, approval fatigue, stale harnesses and compounding errors.

How you will learn
Short, focused lessons of about four minutes, in-video decision points where you commit to a design before the answer is revealed, live demos, a recap at the end of each module, and six narrated branching scenarios set in a fictional travel company, where each choice changes what breaks next.

The course closes with an agent design review: the conditions from every module, as questions you can ask of any agent design at work. No framework or SDK is required, and nothing here depends on one.

Who this course is for
Software engineers and architects building or reviewing LLM-powered features and AI agents
Tech leads and technical product managers deciding whether a product actually needs an agent
Platform, DevOps and security engineers responsible for running agents safely in production
Developers who have used an agent framework and want the design principles that outlast it

https://rapidgator.net/file/0356e6acce535e228a63ad733242783c/Understanding_AI_Agents_and_Agentic_AI.part4.rar.html
https://rapidgator.net/file/1207187eefcfe448df1e0807b53be48d/Understanding_AI_Agents_and_Agentic_AI.part3.rar.html
https://rapidgator.net/file/41cc1a6d0bb5474b531fcfd7d9fe9f38/Understanding_AI_Agents_and_Agentic_AI.part2.rar.html
https://rapidgator.net/file/b068359e0bb526170955cf2a4daa7df6/Understanding_AI_Agents_and_Agentic_AI.part1.rar.html