Hajra Naz
Artificial intelligence is getting smarter every day. It can write, talk, plan, and even make decisions. But with this progress comes a lot of new words that can be confusing.
This guide will help you understand the most important Agentic AI terms in simple language. You’ll learn how AI thinks, learns, remembers, and acts — just like humans do.
1. LLM (Large Language Model)
An LLM is an AI model trained on massive amounts of text from books, websites, and articles. It learns how to understand and generate language that sounds natural. When you chat with AI tools like ChatGPT, you’re using an LLM.
LLMs can write essays, translate languages, summarize information, and even generate code. Some newer models can also handle images, music, and video — not just words.
2. Reasoning
Reasoning means the AI doesn’t just repeat facts — it thinks about them. It connects ideas, finds logic in patterns, and draws smart conclusions. Reasoning helps AI solve math problems, plan events, or compare choices. It’s what makes AI more human-like in problem-solving.
3. LRM (Large Reasoning Model)
An LRM is like an upgraded LLM with deep reasoning power. It doesn’t just talk — it analyzes complex situations. LRMs can help in scientific research, coding, and business decisions. They’re designed to “think” about logic, planning, and long-term outcomes.
4. Continual Pretraining
AI models can get outdated if they stop learning. Continual pretraining keeps them fresh. It retrains models with new data so they stay current with trends, facts, and news. This helps the AI keep up with real-world changes and improve accuracy over time.
5. Supervised Fine-Tuning
In supervised fine-tuning, humans teach AI with labeled examples. The AI studies these examples and learns how to answer similar questions. It’s like a teacher grading homework — the AI gets better each time. This makes the model more reliable for specific tasks like legal, medical, or academic work.
6. Distillation
Distillation is how big AI models teach smaller ones. The smaller model learns from the bigger one’s knowledge. It becomes faster, lighter, and cheaper to run — perfect for phones or small apps. It’s like having a student learn from a professor and then doing the same job efficiently.
7. MoE (Mixture of Experts)
A Mixture of Experts model uses many small “expert” AIs. Each expert focuses on a single skill, like math, writing, or art. When you ask a question, the right expert responds. This makes the system smart and efficient without wasting computing power.
8. HITL (Human-in-the-Loop)
HITL means humans stay part of the AI’s learning process. People check and correct the AI’s work to improve its accuracy. This approach is important in safety-critical areas like healthcare or finance. It helps balance automation with human judgment.
9. Reinforcement Learning (RL)
Reinforcement learning teaches AI through trial and error. It gets rewards for good actions and penalties for mistakes. This system helps robots learn how to walk or play games like chess. Over time, the AI learns what works best.
10. RLHF (Reinforcement Learning from Human Feedback)
RLHF lets humans guide AI by giving thumbs up or down on its answers. The AI uses this feedback to improve future responses. It’s why chatbots now sound more polite, thoughtful, and human-like.
11. Alignment
Alignment means the AI’s goals match human values. It ensures the AI acts safely and ethically. Researchers work on alignment to prevent harmful or biased results. Aligned AI follows rules, respects privacy, and avoids misinformation.
12. Post-Training
After an AI is trained, it still needs fine-tuning. Post-training improves its tone, removes errors, and polishes responses. It’s the final touch that makes the AI sound natural and well-behaved.
13. Agentic AI
Agentic AI is the next big step in AI evolution. These systems can take action on their own, not just respond to prompts. They can search the web, send emails, or plan projects automatically. Agentic AI combines memory, reasoning, and goals — almost like a digital assistant with initiative.
14. Workflows
Workflows are step-by-step processes that AIs follow to complete tasks. They can involve multiple tools or agents working together. For example, one AI gathers data, another analyzes it, and a third writes a summary. This teamwork saves time and boosts productivity.
15. Agents
An agent is an AI system that can think and act on its own. It can complete tasks like booking appointments or managing schedules. Agents can also interact with each other to share information and make decisions.
16. Multi-Agents
Multi-agent systems involve many AIs working together as a team. Each AI has a role — one plans, one calculates, one communicates. Together, they handle complex goals that one model alone couldn’t do. They’re often used in simulations, logistics, or game environments.
17. Design Patterns
Design patterns are reusable structures for building AI systems. They guide how agents communicate and make decisions. This helps developers build consistent, reliable, and scalable AI workflows.
18. Agent Memory
Agent memory helps AI remember past events or instructions. It allows the agent to make smarter choices based on what happened before. Memory is key for personal assistants that remember your preferences or goals.
19. Short-Term Memory
Short-term memory stores information temporarily. It helps AI remember things during a single chat or session. Once the session ends, the memory is cleared — like a temporary note.
20. Long-Term Memory
Long-term memory lets AI remember facts across multiple interactions. It can recall your name, preferences, or past conversations. This makes future interactions more personal and context-aware.
21. Procedural Memory
Procedural memory teaches AI how to do tasks. It remembers steps, sequences, or instructions — like a recipe or routine. For example, it helps a robot repeat the same motion correctly every time.
22. Cognitive Architecture
This is the blueprint of how AI “thinks.” It defines how the model stores information, solves problems, and reacts. It’s like the mental framework inside a human brain, but for machines.
23. RAG (Retrieval-Augmented Generation)
RAG helps AI find real data before answering. It searches documents, websites, or databases to make responses more accurate. This keeps the AI’s answers factual instead of relying on memory alone.
24. Agentic RAG
Agentic RAG gives AI freedom to choose what to search and how. It acts like a researcher — gathering sources, verifying facts, and summarizing results. This makes it smarter, faster, and more reliable for knowledge-heavy tasks.
25. Tools
AI can use tools like calculators, browsers, or APIs. These extend its capabilities beyond simple chat. For example, an AI can use a map API to find routes or a database to fetch company info.
26. Function Calling
Function calling allows AI to trigger specific actions through code. When you ask for a weather update, the AI runs a “getWeather()” function. This makes it interactive and capable of connecting with apps or services.
27. MCP (Model Context Protocol)
MCP helps AI talk to different tools and data systems smoothly. It ensures all parts of the system share the same context. This prevents confusion and improves collaboration between AIs.
28. Structured Outputs
AI doesn’t always reply with paragraphs. Structured outputs let it return clean data like lists, charts, or tables. This is useful for reports, databases, or automation systems.
29. Test-Time Scaling
When AI faces a hard problem, it can use test-time scaling. This means giving it extra computing power or reasoning steps at runtime. It helps improve the quality of complex answers.
30. ReAct (Reason + Act)
ReAct combines thinking and doing. First, the AI reasons through a problem. Then it takes the right action. This method improves accuracy and reduces errors.
31. Reflection
Reflection allows AI to review its own answers. It checks for mistakes and improves the next response. This helps the model grow smarter with experience.
32. Self-Healing
A self-healing AI can detect and fix its own problems. If something breaks, it adjusts automatically. This makes the system more reliable and less dependent on human repair.
33. LLM Judge
An LLM Judge is an AI that grades other AIs. It reviews responses for accuracy, tone, and relevance. This speeds up quality checks and reduces the need for human evaluation.
34. Hybrid Models
Hybrid models mix different types of AI. They can combine language understanding with image or sound recognition. This makes them versatile — perfect for tasks like visual storytelling or data visualization.
35. Chaining
Chaining connects multiple AIs so one’s output feeds into another’s input. For example, one AI summarizes an article, and another turns it into a social post. It creates smooth, automated workflows.
36. Routing
Routing helps decide which AI or tool should handle a specific task. If the question is about math, it goes to a math expert model. This ensures the right system handles the right job.
37. Orchestrator
The orchestrator is like a project manager for AI systems. It coordinates multiple agents, manages timing, and ensures everything works together. It’s essential for large-scale AI operations.
38. Vertical Agents
Vertical agents are specialized AIs made for specific industries. You’ll find them in healthcare, education, finance, or marketing. They understand industry jargon and deliver expert-level results.
39. Overthinking
Overthinking happens when AI spends too long analyzing before acting. It might take too many steps and slow down the results. Researchers are working to balance deep reasoning with quick decision-making.
Why These Terms Matter
All these terms describe how AI is becoming more human-like — learning, remembering, and reasoning on its own. Agentic AI is the future: systems that can plan, act, and think with purpose.
They’ll help businesses, researchers, and individuals save time while staying creative and informed.
Understanding these terms helps you see how AI is evolving — and how it’s shaping the tools we’ll all use in the next decade.
FAQs
1. What does Agentic AI mean?
Agentic AI is a type of AI that can act independently, plan goals, and make decisions without human control.
2. Why is reasoning important in AI?
Reasoning helps AI think through problems, not just repeat data. It makes responses logical and trustworthy.
3. What’s the difference between LLM and LRM?
LLMs focus on language generation, while LRMs specialize in reasoning and complex problem-solving.
4. How does AI remember things?
AI uses short-term memory for active chats and long-term memory to recall older information across sessions.
5. What’s next for Agentic AI?
Future systems will combine reasoning, reflection, and memory to act more like real assistants — capable, aware, and helpful.
