Agentic Ai Architecture

~60 min · 15 stations

Agentic Ai Architecture is a self-paced learning path in Computer Science & AI, free to read, written at General Public / 9th Grade reading level. Across 15 structured stations, you will work through the core ideas step by step, each with a short quiz to check your understanding. By the end you will be able to distinguish between passive tools and autonomous agents; explain the reasoning capabilities of modern models; trace the development of autonomous software.

Conductor

The Conductor

Welcome aboard the Agentic AI line. We are moving beyond simple chatbots to explore systems that think, plan, and execute tasks on their own. Mind the gap between logic and action.

What you will learn

Complete each station to unlock the next.

FOUNDATION

Establishes the core vocabulary and essential context you need before going further.

Distinguish between passive tools and autonomous agents

Station 01: Defining Agentic Systems

Explain the reasoning capabilities of modern models

Station 02: The Role of LLMs

Trace the development of autonomous software

Station 03: Historical Evolution

CORE CONCEPTS

Unpacks the ideas and principles that the subject is built on.

Analyze how agents interpret their environment

Station 04: Perception Modules

Compare different agent planning methodologies

Station 05: Planning Strategies

Describe short-term versus long-term memory

Station 06: Memory Systems

Identify how agents access external functions

Station 07: Tool Use Capabilities

MECHANICS

Examines how things actually work — the processes, rules, and systems in action.

Diagram the iterative agent execution cycle

Station 08: Control Loops

Implement self-reflection mechanisms for agents

Station 09: Error Correction

Define boundaries for autonomous agent actions

Station 10: Safety Constraints

APPLICATION

Puts knowledge to use through real-world scenarios and practical problems.

Design simple multi-agent collaborative workflows

Station 11: Workflow Automation

Create intuitive agent interaction patterns

Station 12: User Interface Design

Measure agent success using specific metrics

Station 13: Performance Evaluation

SYNTHESIS

Connects everything together and explores broader implications and open questions.

Deploy robust agentic systems in production

Station 14: Scaling Architectures

Predict advancements in autonomous agent capability

Station 15: Future Trends

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General Public / 9th GradeAI Generated · gemini-3.1-flash-lite