The complete guide to agentic AI basics

You may have started hearing a new buzzword in AI circles: agents or agentic AI. If you haven’t, it may come as a surprise, because this is quickly becoming the direction the entire field is moving toward.

For years, artificial intelligence has been about assistance. We typed prompts into chatbots, and they typed back answers. Impressive, yes, but still limited. The world is now shifting toward automation, where AI does more than respond. It plans, executes, and delivers outcomes. This shift is powered by agentic systems.

Agentic AI is more than just a passing trend. It is quickly becoming one of the most in-demand skills for developers, product builders, and AI practitioners. Organizations are already adopting it for research, operations, and customer support. Startups are being built around multi-agent systems, and leading enterprises are rethinking entire workflows with agents at the center. Salaries are following the trend, making expertise in agentic systems one of the most lucrative opportunities in tech today.

Learning about agents now is similar to learning about cloud computing a decade ago. The early adopters did not just understand the technology; they reshaped industries with it.

This guide will explain the basics of agentic AI, how it differs from simple and generative AI, the design patterns you should know, and the real-world systems already making an impact. It will also provide a clear learning path that takes you from building your first agent to designing enterprise-ready architectures with the Model Context Protocol.

What is agentic AI?#

Artificial Intelligence has gone through several phases. To understand where agentic AI fits, it helps to look at what came before it.

Simple AI was the starting point. These were rule-based systems: if a condition was met, the machine would follow a predefined rule. Think of an online chess program that follows a set of coded moves or a spam filter that flags emails if certain words appear. Simple AI was powerful in narrow contexts, but rigid. It could not adapt, learn, or reason beyond the rules written into it.

Generative AI took us much further. With large language models, computers began to create new content: writing paragraphs, generating images, composing music, and even writing code. Generative AI is flexible, creative, and context-aware. However, it still operates in a reactive way. You ask a question, and it gives you an answer. You provide a prompt, and it produces an output. The power is undeniable, yet the process stops at generation.

Agentic AI is the next leap. Instead of just waiting for prompts, these systems take initiative. They can decide what steps are needed to reach a goal, plan the process, and even act autonomously in the real-world. An agent can call an external API, fetch information from a database, write and test code, or delegate parts of the task to other agents. It can also remember what it has done before and adapt its strategy when faced with new challenges.

You can think of it this way:

• Simple AI is like a calculator that waits for you to enter numbers.

• Generative AI is like a storyteller who responds when you ask for a story.

• Agentic AI is like a project manager. It not only understands the goal, but also simplifies it into steps, coordinates with others, and makes sure that the job is finished.

This transition moves us from systems that create outputs to systems that deliver outcomes. Instead of just generating an answer, an agentic system can actually get the work done.

Imagine asking a generative model to book you a vacation. It could describe scenic destinations and even draft an itinerary. However, an agentic system could go further: it could search flights, compare hotel prices, reserve the tickets, and add the details to your calendar. That is the difference.

At its core, agentic AI emphasizes collaboration, autonomy, and meaningful impact in real-world settings.

What is an agent?#

At its core, an agent is an AI system designed to act with purpose. Unlike a model that only produces a response, an agent can decide what to do, choose the right tools, and carry out actions to achieve a goal.

A useful way to understand agents is by looking at two broad categories.

• Reactive agents, which simply respond to the environment or a user’s request. They are great for narrow, repetitive tasks.

• Proactive agents, which go a step further by planning ahead, anticipating needs, and taking initiative to achieve outcomes without constant prompting.

Agents usually follow this cycle:

1. Perceive → Receives input from the environment (text, data, images, or APIs).

2. Reason → Interprets the input and decides on a course of action.

3. Act → Uses tools or external resources to perform the next step.

4. Learn → Remembers outcomes and adjusts future behavior. Importantly, this “learning” doesn’t usually mean retraining the model itself. Instead, it happens through updating memory stores, feedback loops, or user corrections that the agent can draw on in future interactions.

This loop is what allows agents to move from simple question answering into task execution, and problem solving.

How agents use tools#

A key feature of modern agents is their ability to use tools. Tools are external functions or systems that extend the agent’s abilities beyond text generation.

Examples include:

• A web search API to gather the latest data.

• A code interpreter to test and run snippets.

• A scheduler to create and update calendar events.

By combining reasoning with tool use, agents can deliver accurate, contextual, and actionable results. For instance, instead of only writing an itinerary for your trip, an agent can look up flight prices, compare hotels, and then add your bookings to your calendar.

Rather than just telling you what it knows, the agent does the work by orchestrating multiple steps through tools.

To understand how this loop and tool use are possible, we need to look at the main components of an agent: the language model at its core, supported by retrieval, tools, and memory.

Main components of an agent#

When we talk about agents, it is easy to jump straight into features like planning or tool use, but underneath, every capable agent is built on a few essential building blocks. These components define how the agent understands the world, how it reasons, how it acts, and how it remembers what it has done. At the center of all of this sits the language model, which serves as the brain of the system. Around it, retrieval, tools, and memory expand the agent’s abilities, making it practical in real-world contexts. Let’s look at each of these in turn, starting with the LLM.

LLM#

The LLM is the central reasoning and language-understanding engine of the agent. It handles parsing your instructions, interpreting user inputs, generating responses, summarizing, planning, and often decomposing tasks. The LLM provides fluency in language, reasoning ability, creativity, and domain knowledge (from its training).

What matters in the LLM:

• Ability to follow instructions and maintain coherence in multi-turn interactions.

• Size of the context window (how much history or info it can “remember” in one go).

• Fine-tuning or prompt engineering to adapt to specific domains.

• Performance constraints (e.g. latency, compute cost, prompt cost).

Retrieval#

Retrieval is about feeding the agent up-to-date, relevant information that is not already baked into the LLM’s weights. Without retrieval, agents are limited to what the model was trained on. With retrieval, they can access fresh content, factual sources, domain documents, or company data.

Forms of retrieval:

• Document or knowledge-base search (via vector similarity, keyword search, etc.).

• Real-time web search.

• Enterprise data or internal databases.

• Hybrid systems like RAG (retrieval-augmented generation) fetch relevant texts and then pass them to the LLM in context. 

Tools#

Tools are external functions or systems that the agent invokes to perform actions it cannot accomplish by generating text alone. They enable “action” in the “perceive-reason-act-learn” loop.

Example tool types:

• APIs (weather, search, calendars).

• Database queries.

• Code execution or function evaluation.

• File I/O or system commands.

• Web scrapers, email, or scheduling modules.

When tools are used well, agents can solve more than just conversational tasks. They can perform transactions, fetch live data, update resources, etc. 

Memory#

Memory gives the agent continuity and the ability to learn from past interactions. Instead of treating each user request as isolated, memory systems allow agents to store, retrieve, evolve, and reuse what has been learned or experienced before.

Memory involves several key components, as mentioned below.

• Short-term memory: Recent conversation history or intermediate steps needed in an ongoing session.

• Long-term memory: User preferences, past tasks, outcomes, and feedback.

• Memory storage mechanisms: Vector databases, semantic indexes, graph-based memory, and structured metadata.

• Retrieval and relevance: When a new task occurs, memory lets the agent find what’s relevant from the past.

• Updating and pruning: Forgetting or weighing memories so that the system stays efficient and relevant.

Tools and frameworks for making agents#

In the previous section, we explored the major components of an agent: the language model at its core, supported by retrieval, tools, and memory. These elements define what an agent can do. However, to actually bring those components together into a working system, you need the right development frameworks.

Building an agent is about choosing practical tools that help you design, orchestrate, and deploy agents in real environments. Today, there are several frameworks that make this possible, each with its own strengths depending on whether you want simplicity, fine-grained control, or enterprise-scale readiness.

CrewAI#

CrewAI provides a straightforward way to design multi-agent systems by letting you define agents with specific roles, connect them to tools, and then organize them into a “crew.” Instead of manually coding complex coordination logic, you describe what each agent should do and CrewAI takes care of the orchestration.

Highlights:

• Define roles, tasks, and goals for each agent.

• Attach tools like APIs, databases, or code execution environments.

• Orchestrate task handoffs between multiple agents.

• Best for building automation workflows quickly.

LangGraph#

LangGraph, built within the LangChain ecosystem, is a framework that gives you fine-grained control over how agents operate. It uses a graph structure where each node can represent planning, reasoning, memory, or a tool call. This approach makes it easy to build workflows that are reliable, monitorable, and recoverable when something goes wrong.

Highlights:

• Model agent workflows as graphs with clear steps.

• Add error handling, retries, and fallback paths.

• Integrate with LangChain Studio for debugging and observability.

• Best for complex, multi-step workflows that need precision and reliability.

ADKs (Agent Development Kits)#

Agent Development Kits bring structure to agent development by treating agents as full software projects. They provide scaffolding, local development environments, and deployment pathways, making it easier to go from idea to production. Two of the most relevant examples are Google’s ADK and the OpenAI Agents SDK.

Google ADK#

Google’s Agent Development Kit is designed for enterprise and team workflows. It comes with project templates, a local development UI, and built-in evaluation tools. It is model-agnostic, but optimized for Gemini and Vertex AI, making it a strong choice for organizations already using Google Cloud.

Highlights:

• Project scaffolding and standardized workflows.

• Local dev UI for quick iteration.

• Tool registration and orchestration support.

• Observability and evaluation are built in.

• Best for enterprise teams who need structure and scale.

OpenAI Agents SDK#

The OpenAI SDK is a lightweight option that integrates directly with the latest OpenAI APIs. It focuses on providing simple components for creating agents, registering tools, and orchestrating workflows while leveraging the Responses API for function calling and structured outputs.

Highlights:

• Lightweight building blocks for agents, tools, and handoffs.

• Direct integration with OpenAI models.

• Built-in support for function calling and structured outputs.

• Fast path from prototype to working agent.

• Best for developers already building with OpenAI APIs.

Autogen#

Microsoft’s Autogen framework is designed to simplify multi-agent conversations and workflows. It provides a flexible setup where agents can talk to each other, exchange information, and collaborate on tasks with minimal boilerplate code. Autogen is particularly useful for building conversational agents, multi-step reasoning workflows, and scenarios where agents need to negotiate or critique each other’s outputs.

It emphasizes ease of prototyping while still offering the ability to scale into more complex systems. Developers can define agents, customize their personalities or roles, and plug in tools or APIs for task execution.

Highlights:

• Built-in support for multi-agent conversations and collaboration.

• Easy configuration of agent roles, behaviors, and communication flows.

• Strong fit for research agents, evaluators, or critique loops.

• Best for rapid experimentation with conversational or reasoning-driven agents.

To make the choice clearer, here’s a quick comparison of the most popular frameworks and when each one is the right fit:

Want to get hands-on and start building your own multi-agent systems? Begin with our step-by-step course:

Agentic design patterns#

Once you understand the components of agents and the frameworks used to build them, the next step is learning design patterns. These patterns are proven strategies for structuring how agents think, act, and collaborate. Instead of reinventing the wheel for every project, you can apply patterns that help your agents work more reliably and efficiently.

Why patterns matter#

Early attempts at building agents often relied on a monolithic design: a single, all-purpose agent expected to handle perception, reasoning, tool use, and memory by itself. While this approach can work for simple experiments, it quickly malfunctions in real-world use. Monolithic agents are hard to debug, difficult to extend, and unreliable when tasks grow in complexity.

Design patterns solve this problem. Instead of relying on one agent to do everything, patterns provide structured strategies for coordination, specialization, and improvement. They allow you to simplify complex tasks, distribute work, and enforce quality checks. With patterns, agents become modular, scalable, and more resilient, which is exactly what’s needed as we move from demos to production systems.

Common agentic patterns#

Now that we’ve seen why design patterns are essential, let’s look at the ones most often used in practice. These patterns act like a playbook for building agents, each with its own strengths, ideal scenarios, and trade-offs. In many real-world systems, multiple patterns are combined to balance speed, accuracy, and reliability.

Some of the most widely used agentic patterns are as mentioned below.

Prompt chaining#

In this pattern, a large task is divided into smaller steps, and the output of one step becomes the input for the next one. This pattern mirrors how humans often solve problems: by simplifying them into manageable chunks.

Example: Generating a research paper outline → drafting each section → refining into a final polished version.

Routing#

Routing allows agents to decide which specialist or sub-agent should handle a particular query or task. Instead of one agent trying to do everything, the router directs the request to the most capable component.

Example: A customer service agent routes technical issues to a troubleshooting agent and billing questions to a finance agent.

Parallelization#

This pattern involves running multiple subtasks simultaneously and then merging the results. When tasks are independent, they reduce latency and ensure broader coverage. However, it requires strategies for deduplication, aggregation, and error handling.

Example: A market research agent queries five different sources in parallel, extracts insights, removes duplicates, and synthesizes a combined report.

Evaluator–optimizer loop#

Here, one agent produces an output, and another agent evaluates or critiques it. The first agent then revises the output, and the loop continues until the result meets predefined standards. This mimics human peer review and improves reliability.

Example: One agent drafts an executive summary, another checks for clarity and factual consistency, and the cycle repeats until both quality and accuracy are met.

Orchestrator–worker#

In this design, an orchestrator agent acts like a project manager, delegating specific tasks to worker agents and integrating their results. This allows for modularity and specialization while keeping coordination centralized.
Best for: Large projects, multi-agent research pipelines, and systems where tasks vary in complexity.

Example: An orchestrator agent oversees a team of worker agents. One handles data collection, another analyzes the data, while another formats results into a report.

Here’s a quick reference table to see which pattern is best suited for which type of task:

Putting patterns into action#

Patterns are not isolated shortcuts. In practice, you often combine them. For example, a research assistant might use parallelization to gather information quickly, routing to direct queries to the right specialists, and an evaluator–optimizer loop to refine the results before delivering them.

Patterns are where theory becomes practice. To expand your knowledge and implement these strategies in real projects, explore our course:

Designing full agentic systems#

So far, we’ve examined components (LLM, tools, retrieval, memory), the frameworks that let you build with them, and the patterns that help agents collaborate. The next step is to combine all of these pieces into full systems that can run safely, reliably, and at scale.

Why System Design matters#

A single agent or pattern can handle simple tasks, but real-world applications demand more. Think of enterprise workflows, customer support platforms, or research assistants. These require multiple agents, guardrails, monitoring, and smooth handoffs. System Design turns building blocks into production-ready systems.

Core principles of agentic System Design#

Let’s study the principles that guide us in the process of designing full agentic systems.

Orchestration#

Orchestration ensures that multiple agents and components work together toward a common goal. This is where the design patterns you’ve already seen come into play. Patterns such as chaining, routing, parallelization, and orchestrator–worker structures provide strategies for coordinating tasks, distributing work, and combining outputs. In System Design, orchestration is the glue that connects everything into a smooth, end-to-end workflow.

Safety and guardrails#

As agents gain autonomy, they also need boundaries to prevent errors, misuse of tools, or unintended behaviors. Safety and guardrails ensure that every step of the process remains predictable and aligned with user or organizational goals. This can involve enforcing structured outputs, validating inputs and results, restricting which tools agents can access, and embedding policy checks into workflows. In a well-designed system, safety is not an afterthought, but the very foundation on which the system is operationalized.

Human-in-the-loop#

No matter how advanced an agentic system becomes, there will always be moments where human judgment is critical. Human-in-the-loop design builds in opportunities for people to review, approve, or override an agent’s decisions, especially in high-stakes scenarios. It also creates pathways for humans to provide feedback, which in turn, improves the system over time. This principle ensures that automation enhances human expertise rather than replacing it outright.

Scalability#

Designing for scale means building systems that work just as well in production as they do in demos. An agentic system must be able to handle growing workloads without losing performance or reliability. Scalability involves strategies like distributing tasks, optimizing for speed, ensuring resilience against failures, and managing resources efficiently. By designing with scalability in mind from the start, systems are prepared to handle real-world demands.

Observability and feedback#

For an agentic system to be trustworthy, it must also be transparent. Observability means that every decision, action, and outcome can be traced and understood. Feedback loops, whether from metrics, logs, evaluations, or user input, turn raw system behavior into actionable insights. This principle allows developers to monitor performance, debug failures, and continuously refine the system. Without observability, it’s impossible to know whether an agent is truly working as intended.

If you want to move from experimenting with agents to designing full systems, this is your next step. In the course “Agentic AI Systems,” you’ll not only learn the principles of orchestration, safety, scalability, and observability, but also study real-world case studies like MACRS (multi-agent collaborative reasoning) and Eureka (NVIDIA’s breakthrough in autonomous reward design). These examples show how the concepts you’ve learned are already shaping advanced AI systems in practice.

👉 Ready to see how the theory translates into real-world impact? Enroll in “Agentic AI Systems” and start building systems that deliver outcomes, not just outputs:

Agentic protocols#

After understanding agents’ components, frameworks, and System Design, it’s time to look at agentic protocols: the standards that enable interoperability, modularity, and secure connections between agents, tools, and data. The Model Context Protocol (MCP) is leading the way, alongside others like A2A (Agent-to-Agent), ACP (Agent Communication Protocol), which address different use cases in agent ecosystems.

A2A Protocol#

A2A (Agent-to-Agent) Protocol is an emerging open standard that focuses on enabling direct communication between agents. Its vision is to ensure that agents built on different platforms, using different frameworks, or designed for different tasks, can still interact, collaborate, and share information seamlessly. By giving agents a common “language,” A2A helps move beyond isolated systems toward true multi-agent ecosystems.

Some of A2A’s distinguishing features include:

• Standardized message formats that allow agents to exchange not only text, but also structured data, metadata, and potentially multimodal content.

• Collaboration support, including the ability to delegate tasks, share intermediate results, and coordinate roles such as planners, evaluators, and workers.

• Capability discovery and negotiation, so agents can identify each other’s strengths and agree on how to divide work.

• Peer-to-peer and decentralized design, reducing reliance on a single orchestrator and enabling more resilient, scalable networks of agents.

Agent Communication Protocol#

ACP (Agent Communication Protocol) is an open standard governed by the Linux Foundation designed to enable interoperability among agents, tools, and applications. The idea is to address fragmentation, journeying past isolated implementations so agents built with different frameworks or by different teams can still understand each other and communicate seamlessly.

Some of ACP’s distinguishing features include:

• Standard message formats with support for multimodal content (text, images, etc.) so agents can exchange richer data.

• REST-based, HTTP-integrated architecture, making it easy to plug into existing web services.

• Open governance via Linux Foundation ensures neutrality, broad participation, and versioning transparency. 

• Designed for production environments, with attention to observability, scalability, and security. 

ACP is particularly useful when your system involves multiple agents needing to exchange tasks, results, or context among each other, especially across different platforms or frameworks.

Model Context Protocol#

As agentic systems become more complex, one of the biggest challenges is how agents connect to tools, data, and prompts reliably. Without a standard, each new integration becomes a patchwork solution, hard to maintain and hard to scale. This is where the Model Context Protocol (MCP) comes in.

MCP is an open protocol that defines a clear, and modular way for language models to interact with external resources. Instead of relying on brittle custom APIs, MCP provides a common language for agents, tools, and servers to communicate.

Core components of MCP#

Let’s discuss the core components of MCP, starting with its client–server architecture.

Client–server architecture#

At the heart of MCP is a client–server relationship that brings structure to agentic systems. The client, usually a language model or agent, makes requests to the server, which in turn provides access to resources, tools, or prompts. This separation of responsibilities makes systems easier to design and maintain because the model does not need to know the internals of every service it interacts with; it simply communicates through the standardized MCP interface.

Resources, tools, and prompts#

MCP extends beyond simple text exchanges by defining three core components: resources, tools, and prompts. Resources represent external data sources such as databases, knowledge repositories, or APIs that supply information. Tools represent actions that an agent can perform, from executing a piece of code to calling an external service. Prompts are reusable templates that capture instructions or workflows in a consistent format, ensuring that agents can use them reliably across tasks. Together, these components give MCP the flexibility to support both knowledge retrieval and action execution.

Context management#

One of MCP’s most powerful contributions is how it handles context. Instead of treating every request in isolation, the protocol allows agents to draw in relevant information, maintain it across steps, and reuse it as workflows evolve. This richer, persistent context makes agents more capable, as they can build upon what they have already learned or retrieved, rather than starting from scratch with every request.

Multi-server architectures#

MCP also supports multi-server designs, where several servers can work in tandem to support a single system. For example, one server might specialize in exposing a knowledge base, another could provide access to specific tools, and a third could manage observability or policy enforcement. By chaining these servers together, developers can build modular and composable architectures, where each server can evolve independently without breaking the overall system.

Security and observability#

MCP is built with production needs in mind, so security and observability are not optional add-ons, but fundamental features. Access controls ensure that agents can only call tools and resources that they are authorized to use. At the same time, tracing and logging provide a clear record of what actions were taken, when, and why. This transparency makes debugging easier, helps teams monitor performance and safety, and provides accountability for systems operating at scale.

Why MCP matters#

MCP represents a shift from ad-hoc integrations to standardized, modular systems. It gives developers a way to build agents that are easier to extend, safer to run, and more transparent to debug. In other words, MCP is about making agents production-ready.

MCP represents a major step forward in how we design agentic systems. If you want to move beyond standalone agents and start building systems that are interoperable, observable, and scalable, this is the protocol to master.

Important note: With MCP handling how agents interact with tools and data, and A2A handling how they interact with each other, developers now have a clearer path to building robust ecosystems. Together, they reduce fragmentation, create interoperability across vendors, and pave the way for large-scale, cooperative agent networks that can tackle more complex, distributed problems.

Learn how MCP powers advanced agentic systems and explore its full potential in our course:

Careers in agentic AI#

As we’ve seen with frameworks, System Design principles, and protocols like MCP, the agentic AI ecosystem is evolving quickly into a real production environment. With that shift comes a surge in career opportunities. Organizations are no longer just experimenting with agents; they are actively hiring people who can design architectures, implement patterns, integrate protocols, and ensure safety at scale.

Emerging opportunities#

• Agentic system architect/designer: Defining high-level system architecture, selecting patterns, and ensuring safety, observability, and memory design.

• Protocol engineer: Implementing standards like MCP or ACP, setting up client/server frameworks, and securing agent communication.

• Research and safety roles: Testing for reliability, bias, and hallucination while building guardrails and human-in-the-loop checkpoints.

Why this is a future-proof skillset#

Agentic AI requires broad-spectrum expertise across LLMs, memory, tools, frameworks, and protocols. That combination is rare, which makes it highly valued. Opportunities are opening across industries like tech, finance, healthcare, and customer service, wherever complex workflows can benefit from automation. As companies want production-grade systems, not just prototypes, engineers and designers who understand observability, scalability, and safety are especially in demand.

Become an agentic AI expert#

If you want to turn this knowledge into a career path, we’ve built a structured roadmap that takes you from fundamentals to advanced System Design and protocols. The “Become an Agentic AI Expert” path walks you through each stage: from core concepts and design patterns to full System Design and MCP implementation, and ties it together with projects that showcase your skills.

Ethics and agent responsibility#

As agents become more autonomous, questions of responsibility, transparency, and trust become just as important as technical design. A system that can plan, act, and learn is powerful, but without careful boundaries, it can also cause harm. This can be the case whether through biased outputs, unsafe tool use, or decisions that impact people’s lives.

Designing ethical agents means more than compliance; it requires embedding responsibility into every layer of the system. Safety guardrails, human-in-the-loop oversight, and transparent logging all help ensure that agents behave predictably. Memory design must respect privacy, especially when handling sensitive data. Tool access should follow the principle of least privilege, so agents cannot misuse resources. Moreover, evaluation processes should continually test for fairness, bias, and reliability.

Ultimately, responsibility is shared: developers, organizations, and users all play a role in how agentic systems are applied. Just as we design for scalability and observability, we must also design for accountability. The more powerful agents become, the more critical it is that we align them with human values and societal norms.

Concluding thoughts#

Agentic AI represents the next major leap in the evolution of artificial intelligence. We’ve moved from simple rule-based systems to generative models that can create, and now to agents that can plan, act, and deliver outcomes. Along the way, we’ve explored what agents are, the components that make them work, the frameworks that bring them to life, the patterns that give them structure, and the System Design principles that make them reliable at scale. Finally, we looked at the protocols like MCP that are setting the stage for interoperability, and the growing career opportunities in this field.

The common thread is clear: we are entering a new era where AI is not just reactive but proactive, collaborative, and deeply integrated into real-world workflows. For learners, this means a chance to position themselves at the forefront, master concepts, build projects, and prepare for roles that will define the future of work.

The journey from understanding simple agents to designing full agentic systems is challenging, but it is also rewarding. With the right learning path, you can move from experimenting with LLMs to becoming a professional who builds scalable, safe, and production-ready systems.