We’re entering a new phase of AI, not just smarter tools, but more autonomous systems. From copilots to co-workers, the language around AI is evolving fast. This guide is here to anchor you.

Whether you’re a builder, operator, or strategist, the words you use shape how you think and what you ship. So here are 25 terms that matter in the agentic era, with deeper context for each.

Core Concepts

Artificial Intelligence (AI)

Systems that replicate human cognitive functions like perception, reasoning, learning, and decision-making.

Dive Deeper:

AI isn’t a monolith; it’s a layered stack. At the bottom: rule-based systems and supervised learning. At the top: autonomous agents that interact, learn, and adapt in real-world environments. Most enterprise AI still sits in the middle: prediction engines wrapped in dashboards. What distinguishes AI from traditional software is its ability to generalize, or handle variation without explicit programming.

This is essentially the reason why the early use of AI models came in the form of prediction models. Be it Amazon, predicting the demand of certain products in an area, or Uber, determining where to deploy more cabs.

Large Action Models (LAMs)

Models trained not just to understand or generate language, but to predict and execute sequences of actions in dynamic environments.

Dive Deeper:

LAMs are a natural evolution from LLMs. While LLMs complete text, LAMs complete tasks. They’re trained on logs of human-computer interactions, tool usage, workflows, and interface events. Instead of “what’s the next word?”, LAMs ask: “What’s the next move?” In agentic systems, LAMs are the missing piece between planning and doing, bridging goal intention with API-level execution.

Large Action Models are the foundation of AI agents as we know them today.

Here’s a quick primer on the differences between LLMs and LAMs.

Generative AI (GenAI)

AI that creates new content, text, images, and code by learning statistical patterns from massive datasets.

Dive Deeper:

GenAI doesn’t understand what it generates; it samples from probability distributions conditioned on context. But because it’s trained on such vast corpora, the outputs feel intuitive, even creative. The magic comes from its ability to compress and remix the world’s knowledge, enabling rapid ideation, translation, and simulation. It’s best thought of as a collaborator with style and recall, but fragile logic.

Generative AI was a core outcome of Google’s research into transformer models, which became the foundation for companies like OpenAI, DeepMind (now part of Google), and Anthropic.

Source: Exponential View

Agentic AI

AI systems that operate with autonomy, pursuing goals, making decisions, and taking action in dynamic environments.

Dive Deeper:

Agentic AI is a philosophy of interaction, referring to software that’s not just reactive but also proactive. Unlike traditional machine learning (ML) pipelines or even chatbots, agents possess memory, planning capabilities, and the ability to initiate actions. They can coordinate subtasks, adapt strategies, and resolve ambiguity. This makes them ideal for complex domains like security operations or cloud orchestration, where rigid scripts often break down.

Agentic AI enables systems to act independently, pursue broad objectives rather than isolated decisions, and carry out complex tasks that require reasoning elements such as planning and reflection. Source

The hard part? Aligning agents with human intent without hardcoding every rule. Agentic AI is essentially the “third wave of AI” and the actionable evolution from Generative AI. Here’s how it works.

Autonomous Agents

Programs that perceive context, make decisions, and execute actions, often in loops, without ongoing human instruction.

Dive Deeper:

These are the building blocks of agentic AI. A typical autonomous agent includes:

• Observation layer (input or API feedback),

• Planning logic (goal decomposition, task selection),

• Action interface (function calls, API triggers), and

• Memory module (short/long-term state).

What sets them apart is persistence; they don’t just “run once.” They monitor, adjust, and retry. Autonomous agents are goal-seeking entities that repeatedly execute tasks until the goal is achieved and the time horizon or work horizon is exhausted.

For example, a security autonomous AI agent will review all security alerts as they come, update a human about it, and remediate the alert. The agent will keep doing it until all security alerts have been treated in a given time horizon (a day, for example) or a work horizon (security alerts from a particular source become 0).

Multi-Agent Systems (MAS)

Networks of autonomous agents working cooperatively (or competitively) to solve complex, often distributed problems.

Dive Deeper:

MAS design introduces new layers: role definition, communication protocols, conflict resolution, and global coordination. Inspired by biological swarms and distributed systems, these setups thrive in environments where modularity and parallel execution matter.

In practice, they're used in simulations, logistics, decentralized networks, and increasingly, AI ops. But coordination overhead grows quickly: without a “shared mind” or strong interfaces, chaos ensues. This shared mind usually happens in the form of a central or a super agent which “leads the team” of other specific, goal-seeking agents.

LLM Mechanics

What’s under the hood of language-based agents.

Large Language Models (LLMs)

Neural networks trained on massive text datasets to generate human-like responses based on prompts.

Dive Deeper:

LLMs work by predicting the next word in a sequence, not by “understanding” concepts. But scale + structure = magic. With billions of parameters, they encode surprising knowledge about syntax, semantics, and even logic. Their architecture —transformers— lets them model complex relationships in parallel. But they’re also stateless: each interaction is an isolated guess, unless memory or external state is added.

Let’s start off with an example of how LLM products work. like ChatGPT works.

Context Window

The maximum number of tokens (words, characters, symbols) an LLM can “see” in a single prompt.

Dive Deeper:

Think of the context window as a form of short-term memory. GPT-4, for instance, can process up to 128K tokens (~300 pages), but anything beyond that is invisible. Agents with longer contexts can “remember” more, reason across documents, and hold longer conversations. It also shapes how workflows are designed: keep critical info in-window, or use external memory (like RAG).

Source: Generative AI on Medium, context window comparison.

Vector Embeddings

Vector Embeddings transform complex data into meaningful vector representations, enabling powerful applications across various domains.

Dive Deeper:

Vector Embeddings turn language into math. Every word, sentence, or doc becomes a point in space, allowing systems to compute “semantic closeness.” This enables tasks like search, clustering, classification, or memory retrieval. For agents, embeddings help match goals to actions or prompts to their corresponding contexts. And for RAG systems, they’re how you fetch the right knowledge before generating.

Computer vision models, AI chatbots, and other AI systems all utilize vector embeddings to perform complex tasks that mimic human intelligence.

For example, a vector with only two dimensions is unlikely to store all relevant information, leading to simpler but less accurate representations.

Source: Labelbox, What are vector embeddings?

Prompt Engineering

Crafting the inputs to LLMs to guide outputs effectively. A good prompt for an LLM is an equivalent of clean code for building a product — it defines the quality of response the AI model can give.

Dive Deeper:

It’s half UX, half spellcasting. Good prompts strike a balance between specificity and flexibility, providing structure (such as roles, format, and examples) while allowing for creative inference. Prompt engineering is evolving into prompt programming, featuring features such as chaining, conditionals, and even programmatic templating.

In agentic systems, prompts are rarely static; they’re dynamically assembled based on state, memory, and tools available.

Prompt Engineering, when applied correctly, can transform a good model into an amazing one. Consider this the equivalent of code vs. efficient code.

It’s very common for good prompts to be converted into templates that help a team or a company use their AI tools more efficiently to get the desired output. We have also seen a sharp rise in Prompt Engineering job profiles.

Prompt Chaining

Linking multiple LLM calls in sequence, where outputs from one prompt feed into the next. Most advanced prompt engineering workflows involve using a chain of prompts and not just single high-quality prompts.

Dive Deeper:

This is how you get multi-step reasoning: first plan, then act, then reflect. Each chain node refines context or action. It’s fragile without grounding, but powerful with structure.

Many agent frameworks use chaining behind the scenes to simulate deliberation, especially for complex workflows or layered decisions. Think of it as building a thought process, one prompt at a time.

Prompt chaining is beneficial as it helps drive the model on a journey, rather than asking it to go directly from point A to point B.

Source: Orq

Function Calling

A method where LLMs call external tools or APIs during a conversation or task execution. Function calling is also known as tool use.

Dive Deeper:

This is what turns an LLM from a clever talker into a useful agent. Given a schema (what functions exist, what inputs they expect), the LLM learns when and how to call them. Think “search this,” “send that,” “trigger this workflow.”

Function calling bridges generation with execution and is central to orchestrated systems like Mindflow, LangChain, or AutoGen. Mindflow’s AI··Rooms is a great example where you can “talk to your tools” and ask them to do things by simply tagging them in chat.

Source: Mindflow, AI Rooms — users can @ their tools and ask them questions or get them to perform actions using natural language. Real actionability.

We saw the release of protocols like MCP, A2A, and AG-UI, which pushed the frontier of human-AI-tool collaboration further. Here’s a quick snapshot of how these protocols work at a fundamental level.

Source: Google, Intro to function calling

Mixture of Experts (MoE)

The Mixture of Experts framework helps models to be pre-trained with far less compute, which means the model or dataset size can be dramatically scaled up with the same compute budget as a dense model.

Dive Deeper:

MoEs are how models become larger and more accurate without becoming slower. Instead of using every parameter for every task, they route different parts of the input to different “experts.” This allows for specialization, modularity, and faster inference.

In agentic systems, MoEs support dynamic decision-making, activating only the parts of the model most relevant to a given situation. Think of it as “conditional intelligence.” Instead of having a big, bulky model that does everything, we now have smaller models, each with its own specialization, collaborating to achieve a specific goal.

Source: arXiv, Outrageously large neural network

Hypernetworks

Neural networks that generate the weights of other networks.

Dive Deeper:

Hypernetworks add a layer of abstraction to model architecture. Rather than fine-tuning a base model, a hypernetwork can generate new weights based on context or task. This is especially powerful in agentic systems where adaptability is crucial: an agent encountering a novel task can instantly generate a specialized model without requiring retraining. It’s dynamic intelligence-as-a-service.

Agent Design

From stateless bots to goal-driven collaborators

Planning Agents

Agents that first decompose a high-level goal into a sequence of subtasks before taking action.

Dive Deeper:

Planning is what elevates agents from reactive to strategic. Instead of responding to input, they proactively structure their own workflow. Many use tree search, task graphs, or scratchpads to sequence decisions. Planning agents are often paired with LLMs to brainstorm steps, and then with LAMs or tools to execute them.

The tradeoff? More interpretability, but also more surface area for error. Think of it as turning intention into architecture.

Perplexity’s Deep Research mode takes in a prompt and then breaks it down into multiple tasks, defining the sources it will refer to for each task to provide an output. You can read the end result here.

Goal-Oriented Agents

Agents that maintain a persistent objective and work iteratively toward fulfilling it, often across multiple steps or tools.

Dive Deeper:

These agents track progress toward a target state, whether that’s resolving a ticket, synthesizing a report, or triaging alerts. They often rely on feedback loops (e.g. tool success/failure, user approval) to adapt. What makes them powerful is not just execution, but persistence in the face of ambiguity. They don’t just follow instructions, they pursue outcomes.

Meta-Learning

The process by which models learn how to learn involves generalizing learning strategies across tasks.

Dive Deeper:

Also known as “learning to learn,” meta-learning is ideal for agents operating across diverse, changing environments. Rather than relearning from scratch each time, an agent with meta-learning capability can adapt faster with less data by applying abstracted learning strategies. In practice, this unlocks cross-domain agility: one agent, many missions.

Continual Learning

The ability of an agent to accumulate knowledge over time without forgetting previous tasks.

Dive Deeper:

Unlike static models, continual learners evolve with every interaction. This is key for agents in production who must retain what works and adapt to what’s new. The challenge? Avoiding catastrophic forgetting, where learning something new erases prior knowledge. Solving that balance—stability vs plasticity — is one of the core frontiers of persistent, real-world agents.

Agent Memory

A mechanism for storing and retrieving information across sessions, enabling continuity and contextual reasoning.

Memory enhances contextual awareness and helps the agent become more familiar with the work and environments in which it’s working.

Dive Deeper:

Memory turns agents from transactional bots into companions. It can be episodic (what happened in this session), long-term (what’s true across time), or semantic (structured knowledge). Without memory, agents forget everything between steps. With it, they can learn user preferences, revisit past failures, or connect dots across interactions. Architecturally, memory often lives outside the LLM, fetched via embeddings or vector search.

Stateful Interaction

The ability for an agent to maintain and update its internal state as it progresses through a task.

Dive Deeper:

State is the agent’s internal map: where it’s been, what it knows, and what’s pending. It includes flags like “step complete,” “tool failed,” or “waiting for input.” This enables conditional logic, retry mechanisms, and branching workflows. Stateless agents can be impressive in a single turn but without state, they can’t adapt mid-task or recover from errors.

Source: Debmalya Biswas, Stateful and Responsible AI Agents

Reflection Loops

Processes where agents review, critique, or revise their own actions to improve performance over time.

Dive Deeper:

Reflection turns agents into meta-thinkers. After acting, they evaluate: Did that work? Is there a better way? Some use Chain-of-Thought prompting to simulate reflection, while others employ separate critic models. This enables self-correction, hallucination reduction, and more robust execution, particularly in high-stakes environments such as SecOps or code generation.

Autonomy Spectrum

The range between fully manual and fully autonomous agent behaviors is often governed by human-in-the-loop controls.

Dive Deeper:

Not all agents should act independently. The autonomy spectrum spans:

• Assistive (offers suggestions),

• Interactive (asks for confirmation),

• Supervised (acts with checkpoints),

• Fully autonomous (executes end-to-end).

The right level depends on trust, risk, and domain complexity. Designing for autonomy isn’t just technical, it’s a product and ethical decision.

Control + Safety

Trust is a feature, not an afterthought.

Guardrails

Constraints and filters that prevent undesirable, unsafe, or unaligned outputs from AI systems.

Dive Deeper:

Guardrails are the bumpers on the bowling lane. They might restrict what topics an agent can engage with, how it handles user data, or which tools it can invoke. Implemented through prompt templates, output filters, or access policies, guardrails ensure that AI doesn’t stray outside its design boundaries. In enterprise settings, they’re often legally or ethically required, not just best practice.

Source: Mindflow, while building an AI··Task or AI··Agent, you can customize which operations your agent can perform and set guardrails around what they can do.

Human-in-the-Loop (HITL)

A setup where humans supervise, approve, or co-pilot agent decisions or actions.

Dive Deeper:

HITL is about augmented autonomy. Agents act, but humans retain the final say. Common modes include:

• Approval gating: “Do you want me to execute this step?”

• Choice offering: “Here are 3 next actions, pick one.”

• Live feedback: Real-time intervention and redirection

Used effectively, HITL enhances trust, reduces errors, and fosters human intuition regarding AI behavior. Used poorly, it becomes a bottleneck.

Reward Models

Scoring functions are used to guide agent behavior, especially in reinforcement learning setups.

Dive Deeper:

Not all goals are hard-coded. Reward models enable agents to learn what constitutes “good” behavior, whether that involves completing a task, pleasing a user, or adhering to a policy. These models can be explicit (set by humans) or emergent (learned from data). In practice, they're hard to tune: reward hacking (gaming the metric) is a classic failure mode.

Tool Use

The agent’s ability to call external APIs, databases, or functions to extend its capabilities.

Dive Deeper:

Agents who only speak aren’t enough. Tool use refers to how agents actually perform tasks—such as querying a database, sending an email, or triggering a workflow. The more tools available, the more capable (and unpredictable) the agent becomes. The challenge is not just access, but also alignment: ensuring that tools are used appropriately, securely, and with context.

Chain-of-Thought (CoT) Reasoning

A prompting strategy that encourages step-by-step thinking in LLMs and agents.

Dive Deeper:

CoT is a cognitive prosthetic. Instead of jumping to conclusions, it walks the model through intermediate reasoning steps. This improves accuracy in math, logic, and planning; especially when tasks are ambiguous. CoT also helps with transparency: you can see how the agent arrived at an answer, not just the result.

Auditability

The ability to trace, inspect, and explain an agent’s decisions and actions over time.

Dive Deeper:

Without logs, agentic systems become black boxes. Auditability means capturing:

• Prompt history

• Tool usage

• Decision paths

• Feedback loops

It’s essential for trust, compliance, and debugging. Especially in regulated or high-risk domains, audit trails are not optional—they're accountability infrastructure.

Explainable AI (XAI)

Techniques and frameworks that make AI decisions interpretable to humans. This approach is also known as the white-box approach.

Dive Deeper:

Agents that act autonomously must also justify their actions, especially in high-risk domains. XAI offers transparency into how decisions are made: why a tool was called, why a path was chosen, and what factors were weighed. This helps with trust, regulation, and debugging. Without explainability, autonomy is a black box.

Source: ScienceDirect, Explainable Artificial Intelligence (XAI)

What’s next: Theory of Mind in AI

The idea is that AI systems can model and reason about the beliefs, desires, and intentions of others.

Agentic AI isn’t just a technical upgrade; it’s a conceptual shift. We’re moving from tools that assist to systems that act. And in that transition, knowledge becomes leverage.

Theory of Mind is the foundation of empathy, negotiation, and social interaction. Bringing it into AI means creating agents that don’t just process inputs, but infer what you’re thinking, what you want, and what others believe. While early, it opens the door to multi-agent collaboration, conflict resolution, and human-like adaptability.

This is where cognitive science meets machine behavior design. Are we there yet?