AI Agents for Everyone & AI Bootcamp with Hands-on Labs

Explore how agentic ai masterclass distinguishes reasoning from responding, detailing perception, reasoning, and action cycles to achieve accurate, explainable, and reliable outputs.

Plan and decide with a structured loop that defines goals, breaks tasks into steps, executes with clear ownership, evaluates results, and iterates for continuous improvement in AI agents and teams.

Explore memory in artificial intelligence systems, storing and retrieving user preferences and past interactions across short-term and long-term memory to create continuous, personalized, context-aware responses.

Agentic AI agents move from thinking to acting by using tools to connect with external systems, access live data, and execute tasks for real-world outcomes.

Learn to design goal-based prompts, structure instructions, and iterate with feedback to guide AI agents toward clear outcomes, using constraints, guardrails, and defined success criteria.

Design ai agents by integrating the brain (llm), memory, tools, and environment to create a cohesive system that reasons, remembers context, and acts across digital, conversational, and physical settings.

Explore the agent loop, a four-phase cycle of think, plan, act, and observe that enables dynamic decision-making and continuous improvement in AI agents.

Design structured workflows for AI agents by breaking tasks down, ordering steps, and integrating tools to enable reliable, scalable execution and adaptive decision points.

Learn error handling in AI agent systems by detecting issues early, responding logically, learning from failures, and monitoring in real time, with exponential back-off retries, fallbacks, and human handoff.

Explore agentic AI tools and frameworks like LangChain, Crew AI, AutoGPT, and the OpenAI API to connect language models with tools, APIs, and multi-step agent workflows.

Explore no-code, low-code, and full-code approaches for building AI systems, balancing speed, flexibility, and control. Learn how to choose the right path and tools for your goals and timeline.

Choose the right tech stack by starting with the problem and aligning with business goals, then balance scalability, integration, performance, and total cost of ownership for a sustainable AI system.

Define a clear goal for the AI agent and outline its workflows. Test, deploy, and measure outcomes to ensure reliability and business alignment.

Design the workflow to operationalize your AI agent by breaking the goal into discrete, ordered steps, integrating tools and decision points, and deploying for monitoring and improvement.

Build the agent by connecting the large language model, memory, and tools, configure behavior with guardrails, and deploy it into real environments.

Master testing and debugging of AI-driven systems to uncover failures, validate results, and drive continuous improvement through realistic scenarios, monitoring, and iterative refinement.

Optimize AI systems by boosting speed, accuracy, and cost efficiency through caching, shorter prompts, and asynchronous processing, while monitoring latency, cost, and errors for continuous improvement.

Explore multi-agent systems that divide tasks among specialized agents to improve scalability, autonomy, and efficiency. Learn how coordination, collaboration, and role-based agents enable faster, more reliable AI solutions.

Explore role-based agents with manager, worker, and reviewer roles to create a structured, scalable AI system that optimizes collaboration, quality, and efficiency.

Explore how agents communicate and collaborate to form a coordinated multi-agent system, using message passing or shared memory, to solve complex workflows.

Design cohesive multi-agent workflows by defining roles, sequencing tasks, dependencies, and interactions to deliver validated, structured outputs with scalable, reliable ai systems.

Explore real-world applications of multi-agent systems across customer support, sales, marketing, and operations; leverage specialized agents to boost speed, accuracy, and scalability, delivering measurable business value.

Discover how AI agents automate repetitive tasks, qualify leads, generate opportunities, and close deals faster, while integrating with CRM to log activities and boost conversion rates.

Explore how AI agents transform customer support from human-led to autonomous, scaling responses with automation, intelligent routing, and personalized, always-on interactions that improve efficiency and satisfaction.

Automate marketing with AI agents to generate content at scale, run campaigns automatically, and analyze performance in real time for personalized, scalable, data-driven results.

Explore how AI agents transform manual operations into AI-augmented and fully automated workflows, automating data entry, routing, notifications, and reporting while enabling real-time insights and scalable decision support.

Explore how AI agents adapt to industry-specific use cases by tailoring to workflows, business value, and regulatory complexity across healthcare, legal, real estate, e-commerce, and finance.

Identify and manage the risks of agentic AI, emphasizing governance, oversight, and validation to prevent autonomous decisions, data exposure, hallucinations, and operational failures.

Learn how human-in-the-loop safeguards blend human judgment with artificial intelligence speed, applying proactive and reactive controls, audit trails, and escalation pathways to keep AI safe and accountable.

Explore responsible AI by examining ethics, fairness, and accountability, and learn how transparency, bias management, and governance enable compliance and create trustworthy and sustainable AI systems.

Define governance and compliance through policy, oversight, and accountability, align with regulatory requirements and data privacy, and manage risk with ongoing monitoring to ensure ethical, legal, and trustworthy AI deployment.

Learn how to protect data, control access, and defend AI systems against threats. Apply data minimization, purpose limitation, encryption, access logging to build trust and meet GDPR and HIPAA.

Learn the difference between automation and AI agents, and how memory, tools, and brain enable agents, with hands-on ideas for building simple and conversational agents.

Build your first automation by creating a form submission workflow that saves data to Google Sheets and sends a friendly email notification, then publish it to a live production URL.

Learn to build AI agent workflows by creating a workflow with trigger and action nodes, importing templates, and integrating AI nodes, memory options, and tools for automation and analysis.

Design an AI agent to generate and retrieve leads in Google Sheets via a chat workflow, using add and get leads tools, a system prompt, and public deployment.

Automate LinkedIn lead generation by scraping profile data through a structured workflow that formats queries by job title, location, and industry, then stores results in Google Sheets.

Scrape unlimited leads from Google Maps for any niche using the Instant Data Scraper extension, curate in Google Sheets, deduplicate by phone numbers via a simple annotation workflow.