Agentic AI Training in District of Columbia

This live training in District of Columbia explores how agentic AI reshapes organizational workflows, talent strategies, and job design. Ideal for intermediate professionals, it covers autonomous decision-making, enterprise readiness, and governance frameworks to ensure responsible, compliant AI adoption.

This instructor-led bootcamp in District of Columbia offers a hands-on journey to build autonomous AI agents in Python. Participants complete five progressive projects covering design patterns, API orchestration, and multi-agent workflows, moving from theory to real-world deployment.

This practical course in District of Columbia guides you through deploying agentic AI on resource-constrained devices. Learn to build, optimize, and manage lightweight agents for local reasoning using Python, TensorFlow Lite, and PyTorch Mobile, enhancing speed, privacy, and reliability.

This instructor-led, live training in District of Columbia introduces agentic AI, covering prompt engineering, design patterns, and responsible use. Participants learn to build simple agent workflows with Python, integrate tools, and implement safety practices for effective, safe, and scalable autonomous systems.

This instructor-led, live training in District of Columbia guides intermediate professionals on designing and scaling agentic AI for business automation. Learn to map workflows, integrate tools, and build practical use cases in customer service, supply chain, and marketing, ensuring measurable impact.

This live training in District of Columbia guides advanced professionals to design and implement multi-agent systems using Python. It covers agent coordination, reinforcement learning, and distributed computing with frameworks like Mesa and Ray, ending in a hands-on capstone project.

This instructor-led training in District of Columbia covers reinforcement learning and decision-making for agentic AI. Learners implement DQN, PPO, and A3C using Python, model environments with OpenAI Gym, and integrate these techniques into robotics and planning systems for real-world adaptive behavior.

This live training in District of Columbia equips advanced professionals with the skills to scale and operationalize agentic AI systems in production. It covers MLOps, observability, and cost control, enabling the design of resilient, observable, and cost-optimized pipelines for large-scale deployments.

This instructor-led training in District of Columbia covers governance, identity management, and red-teaming for agentic AI systems. Advanced practitioners will design secure deployments, implement least-privilege access, and execute adversarial testing to mitigate real-world threats in production environments.

This instructor-led, live training in District of Columbia focuses on practical Python implementation for autonomous AI agents. It covers modern SDKs like LangChain and AutoGen, enabling engineers to build, connect, and orchestrate complex agent workflows with confidence.

This instructor-led live training in District of Columbia covers practical Python techniques for building autonomous agents, including agent loops, tool integrations, memory management, orchestration, and safety controls. Ideal for ML engineers, it focuses on deploying robust, production-ready agents.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at intermediate-level AI developers and automation specialists who wish to integrate agentic capabilities into AI-powered applications.

By the end of this training, participants will be able to:

• Understand the principles of agentic AI and autonomous decision-making.
• Implement goal-driven AI agents with self-optimization techniques.
• Integrate multi-agent collaboration for complex problem-solving.
• Enhance AI-human interaction through adaptive user experiences.
• Deploy agentic AI models in real-world applications.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at advanced-level professionals who wish to develop and optimize multi-agent systems using Agentic AI frameworks.

By the end of this training, participants will be able to:

• Understand the principles of Agentic AI in multi-agent environments.
• Develop AI-driven agents that interact autonomously.
• Implement reinforcement learning for adaptive AI behavior.
• Optimize multi-agent collaboration and competition.
• Apply Agentic AI in robotics, gaming, and enterprise automation.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at advanced-level professionals who wish to leverage Agentic AI for enterprise-scale automation and strategic AI adoption.

By the end of this training, participants will be able to:

• Understand the role of Agentic AI in enterprise applications.
• Integrate autonomous AI agents with enterprise systems.
• Optimize AI-driven workflows for scalability and efficiency.
• Ensure compliance, security, and governance in AI automation.
• Develop AI-driven business strategies for digital transformation.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at advanced-level professionals who wish to leverage Agentic AI for decision-making in complex business and technical scenarios.

By the end of this training, participants will be able to:

• Understand the principles of autonomous decision-making in AI.
• Design and implement AI agents that operate with minimal human intervention.
• Integrate Agentic AI into automation workflows and business systems.
• Optimize AI-driven decision processes for efficiency and scalability.
• Ensure compliance, security, and ethical considerations in AI autonomy.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at intermediate-level AI engineers, ML researchers, and developers who wish to build and implement Agentic AI systems in real-world applications.

By the end of this training, participants will be able to:

• Understand the core principles of Agentic AI systems.
• Implement AI agents capable of autonomous reasoning and action.
• Integrate Agentic AI with APIs and third-party services.
• Optimize multi-agent interactions for complex tasks.
• Address ethical, security, and scalability challenges in Agentic AI.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at beginner-level professionals who wish to understand the fundamental principles of Agentic AI, its capabilities, and its potential impact on industries.

By the end of this training, participants will be able to:

• Understand the core principles of Agentic AI.
• Differentiate between traditional AI and autonomous AI agents.
• Explore real-world applications of Agentic AI in various industries.
• Assess the benefits and challenges of deploying autonomous AI systems.
• Analyze the ethical and security considerations of Agentic AI.

This one-day course provides software engineers, technical leads and development teams with a practical framework for working effectively with AI coding agents in real-world software projects. Rather than focusing on prompting techniques alone, the course explores the complete agent ecosystem, including context engineering, reusable workflows, MCP (Model Context Protocol) integrations, feedback loops, security and organisational adoption.

AI agents are moving from research demos to production systems that act autonomously across text, image, speech, and tool-driven workflows, raising the bar for engineering rigor, adversarial robustness, and regulatory accountability. This instructor-led training takes advanced practitioners through the full agent lifecycle, from architecting single and multi-agent systems to integrating multi-modal perception and orchestrating coordinated agent behavior through modern frameworks. Hands-on labs in Python build progressively toward a production-grade multi-agent system, which participants then stress-test using adversarial techniques and the Adversarial Robustness Toolbox. The course closes with NIST AI RMF, ISO/IEC 42001, EU AI Act, and GDPR alignment, secure deployment, monitoring, and incident response, so participants leave able to ship agents that are capable, defensible, and compliant.

This instructor-led, live training in District of Columbia (online or onsite) is aimed at beginner-level / intermediate-level developers and cloud practitioners who wish to use Alibaba Cloud to build AI agents that can automate tasks, answer questions, and connect with business systems.

By the end of this training, participants will be able to: understand AI agent architecture on Alibaba Cloud, build a simple agent workflow, connect an agent to enterprise knowledge and tools, deploy and monitor an agent in a cloud environment.

This instructor-led, live training in District of Columbia is aimed at intermediate-level professionals who wish to automate business processes and build their first AI agent with Make.

By the end of this training, participants will be able to:

• Design no-code workflows in Make with triggers and webhooks.
• Connect an AI agent to their data within a flow.
• Automate a real process end to end, with error handling and monitoring.