The AI Agent Bootcamp 2026: Complete AI Аgent Course

Explore the core components of AI agents, including the environment with sensors, the interpretation model, decision making logic, actuators, and feedback mechanisms to build practical agent systems.

Explore how ai agents perceive and interact with dynamic environments, from self-driving cars navigating roads and weather to shopping agents blending digital data with weather and local factors.

Sensors empower AI agents to perceive their environment by collecting data from cameras, lidar, GPS, web scrapers, APIs, microphones, and IoT, then processing this input to understand environment and act.

Explore how AI agents use sensor data fed into a model—like a brain—to interpret multimodal inputs (text, images, audio, video) and translate understanding into action.

Explore how a decision making logic layer evaluates model outputs against rules to choose actions, illustrated by trading and spam filtering examples and the role of external validation.

Explore how AI agents perform actions that shape their environment using physical or virtual actuators, from steering a self-driving car and trading stocks to classifying emails as spam.

Observe how agents update their behavior through a continuous feedback loop after acting on the environment, using new sensor data to refine future actions and improve effectiveness.

Explore core AI agent structures, starting with simple reflex agents and advancing to more sophisticated designs, and learn to select and build the right agent for your objectives.

Define five key characteristics of sophisticated AI agents—profile and persona, memory, reasoning, actions, and learning—note that not all types require all five, shaping interaction and environmental change.

Explore the simple reflex agent, a reactive system that acts on perception with no memory or learning. A thermostat example shows it follows hard-coded rules to maintain a fixed temperature.

Explore model-based reflex agents that build an internal world model and map, using it to track cleaned areas and avoid obstacles, illustrating a simple AI with fixed rules.

Explore goal-based agents that search for action sequences and plan to minimize the user's travel time, updating routes when external conditions change.

Explore utility-based agents, which rank higher than goal-based agents by evaluating multiple factors to maximize expected utility. Assess trade-offs across volatility, liquidity, and default risk to support more nuanced decisions.

Learn how learning agents share capabilities with utility-based and goal-based agents, add experiences to their knowledge base, and improve performance in unfamiliar environments through feedback-driven refinement of the internal model.

Explore how AI agents acquire knowledge by connecting to external systems and how human knowledge is incorporated into AI agents, enabling them to learn and adapt.

Explore how AI agents learn from other agents, external sources like web searches and APIs, and human feedback to update knowledge bases and perform agentic reasoning.

Explore how human in the loop guides training, monitoring, and refinement of ai agents, illustrated by a customer support chatbot evolving from development to production through human feedback and metrics.

Explore the distinction between LLMs workflows and AI agents, learn the React and Riwu frameworks, compare single-agent and multi-agent systems, and examine chain of thought reasoning to improve decision making.

Differentiate LLMs, AI workflows, and AI agents by showing how LLMs respond to prompts, AI workflows follow predefined steps, and AI agents act autonomously to achieve goals.

Explore how AI agents reason through information and act to schedule meetings, choose locations, check weather, reserve cafes, and send invitations, demonstrating the react framework of reasoning and acting.

Compare Riwu, a reasoning without observation framework, with React agents by showing upfront planning versus stepwise tool use. Riwu executes a preplanned sequence without adapting to intermediate results.

Explore single agent systems, lean approach where one AI agent expands capabilities with each tool. See a personal assistant handling emails, booking venues, scheduling meetings, and contrast with multi-agent systems.

Explore how teams of specialized ai agents collaborate through orchestration, tool calling, and task division, comparing central manager and decentralized structures to balance complexity and performance.

Explore practical aspects of creating and working with AI agents by selecting models, choosing tools, configuring prompt instructions, and adding guardrails.

Define clear objectives and data needs to implement AI agents in your business, visualize the process, choose an appropriate agent structure, prototype, deploy, and monitor KPIs with oversight.

Choose a model by task needs, balancing accuracy, speed, cost, and scalability; prototype with the most capable model to set a baseline, then test others for simple and complex tasks.

Discover how tools help AI agents interact with software via APIs or human interfaces. Differentiate data tools for information retrieval from action tools that perform tasks like scheduling or ticketing.

Master prompt engineering for AI agents by using clear system prompts, breaking down tasks, defining explicit actions, and capturing edge cases to improve decision making and user experience.

Explore zero shot, one shot, and few shot prompting and contrast with fine tuning to show how training amount shapes AI agent systems and performance.

Within the ai agent engineer course, apply chain of thought reasoning, a prompt engineering technique that reveals intermediate steps. Learn how articulating reasoning improves agent performance, transparency, and error diagnosis.

Explore why multiple guardrails protect AI agents, addressing data privacy, content safety, brand alignment, accuracy, and user experience, and how to continuously refine them after deployment.

Prioritize human intervention and oversight when launching an AI agent, escalating uncertain outputs to humans and catching edge cases and failures automated tests miss.

Evaluate ai agents by balancing accuracy, speed, coherence, cost, and safety for their purpose. Prioritize user experience and real-world feedback to refine tone, memory, and contextual awareness.

Explore n8n, a workflow automation tool that lets you create AI workflows and AI agents with a drag-and-drop builder. Register, start a free trial, and build agents in minutes.

Explore the four n8n node types—triggers, action nodes, logic nodes, and the ai agent node—as arrows connect them into a memory-enabled workflow using a large language model.

Explore the Nar den canvas to build an automated chat-to-email workflow powered by a memory-enabled AI agent that looks up contacts in Google Sheets and sends emails via Gmail.

Define your agent's personality by setting a system prompt that directs the AI to draft work emails using user context, tools for contact data, and a structured format.

Learn to connect your AI agent to a brain model using OpenAI LLMs, create API credentials, and configure a GPT-4 model in Naarden for memory-ready workflows.

Add memory to your agent in n8n with a five-interaction context window, enabling it to recall user details like names.

Connect Google Sheets for contacts and Gmail for sending, enabling the agent to draft and send personalized emails with dynamic recipient, subject, and message.

Configure the final output node to pass structured json from the agent, using manual mapping, surface contacts from Google Sheets, and test in real-time within the chat.

Explore the infrastructure for developing and deploying AI agents, from APIs and cloud services to knowledge integration and deployment, and learn to set up, manage, and scale them.

Compare building in-house versus using an external LLM via APIs. Connect external models to your AI agent to send prompts and receive real-time responses, enabling scalable, cost-conscious decisions.

Cloud solutions provide scalable infrastructure for AI agents, enabling pay-as-you-go experimentation. Amazon Web Services, Microsoft Azure, and Google Cloud Platform offer GPUs, storage, APIs, and tools for AI workloads.

Empower AI agents by connecting them to databases, ERP, and knowledge bases to gain 360-degree visibility of business processes. Enable internet access to gather public information for richer reasoning.

Explore how modern agent projects fuse pre-designed lego bricks to enable memory planning, tool calling, and orchestration, using frameworks like lang chain, autogen, cru ai, adk, and flow wise.

Deploy your AI agent to a user-facing chat interface or a backend service using cloud tools like AWS Lambda, Bedrock, SageMaker, and Lex to ensure scalability and reliability of AI.

Explore how AI agents act as scalable, cost-efficient employees, lowering HR costs, enabling 24/7 support, and delivering real-time insights to decision makers while freeing humans for strategic innovation.

AI agents collaborate with humans across roles, automating routine tasks while humans focus on high-value work, intuition, and creativity. Implement clear escalation paths and ongoing human oversight to guide decision-making.

Explore AI agent architecture and prompting frameworks to build reliable agent systems. Learn agentic workflows, single and multi-agent designs, memory, planning, and patterns like react, review, and Tree of Thought.