AI Agents Explained Like You’re an Engineer: A Beginner’s Guide to How Modern AI Actually Works

2026-06-23 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, medium

Summary

Modern AI agents function as sophisticated software systems, moving beyond simple "Question -> AI -> Answer" models. They integrate a 15-step process, starting with Goal Understanding and Observation, then leveraging Memory, Chunking, and Embeddings to facilitate Retrieval-Augmented Generation (RAG). The system then engages in Reasoning and Planning to formulate actions, utilizing Tools for external system interaction. Subsequent steps involve Action, Reflection, and Adaptation, all within a continuous "Agentic Loop" that enables Autonomous Decision Making. This cycle, which repeats until a problem is solved or human intervention is required, culminates in a comprehensive Response Generation, distinguishing AI agents from basic Large Language Models by adding context and actions.

Key takeaway

For AI Engineers building autonomous systems, understanding the underlying 15-step agentic architecture is crucial. Focus on designing robust observation, memory, and tool integration components to enable effective reasoning and adaptive planning. Your systems will achieve greater reliability and autonomy by explicitly incorporating reflection and continuous adaptation within the agentic loop, moving beyond simple LLM interactions to solve complex, real-world problems.

Key insights

Modern AI agents operate as complex software systems, integrating multiple steps into a continuous decision-making loop.

Principles

• AI systems require memory for context and history.
• Chunking breaks large documents for efficient retrieval.
• Embeddings convert meaning into comparable numerical formats.

Method

AI agents follow an "Agentic Loop": Observe -> Reason -> Plan -> Act -> Reflect -> Adapt -> Repeat, until a problem is solved or human assistance is needed.

In practice

• Use RAG for context-aware knowledge retrieval.
• Implement tools for external system interaction.
• Design reflection steps to verify actions.

Topics

• AI Agents
• Agentic Architecture
• Retrieval-Augmented Generation
• Embeddings
• Large Language Models
• Autonomous Systems

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.