Build an AI Agent and Transform Your Organization Without Writing Code
Columbia Engineering’s Agentic AI Program for Business helps executives understand how agentic AI systems are transforming enterprise operations, workflow automation, and strategic decision-making. This executive education program teaches leaders how to evaluate, deploy, and manage agentic AI for business applications without requiring coding experience.
AI agents are reasoning, planning systems that can orchestrate complex workflows, coordinate teams of specialized AI agents, and act autonomously to achieve long-horizon goals. For business leaders, that represents both a profound opportunity and a governance challenge. Most executive AI certificate programs tell you what these systems can do. This program shows you how agentic AI works, in addition to how to use agentic AI.
Developed by faculty at Columbia Engineering, Agentic AI for Business is an intensive workshop that gives decision-makers - regardless of technical background - genuine technical intuition about how agentic AI systems are built, how they reason, and what can go wrong. Through a sequence of hands-on labs, participants create an AI agent themselves, building the kind of first-hand understanding that no lecture or case study can replace.
Each day builds on the last - from foundational understanding to hands-on construction to strategic deployment. Lectures, case studies, and industry panels are integrated with labs so that every concept is immediately applied.
What You Will Learn
- Develop a framework for identifying high-value AI automation opportunities within your organization
- Translate business processes into AI workflow specifications that your technical teams can execute
- Build genuine technical intuition about how agentic AI systems are designed and how they reason - without coding
- Build functional AI agents that can integrate with company goals and perform strategic decision-making
- Build data infrastructure to customize agents to the company’s unique needs and constraints
- Learn to evaluate AI vendor proposals, model outputs, and system architectures with an informed eye
- Navigate the ROI, risk, governance, and ethics dimensions of deploying agentic AI at scale
- Build a peer network of senior leaders navigating the same transformation across industries
The program schedule and faculty are subject to minor changes.
Who Should Attend
This program is designed for leaders responsible for, or significantly involved in, AI adoption decisions at their organizations, without a deep technical background.
- C-suite and VP-level executives (CEO, COO, CPO, CMO, CRO)
- Founders, entrepreneurs, and small business owners
- Division heads and senior business unit leaders
- Strategy, operations, and transformation leads
- Senior managers overseeing data, product, or technology teams
- Consultants advising organizations on AI adoption
- No coding or programming experience required
- No prior AI or data science background required
- Familiarity with business operations and decision-making
Program Schedule
Lecture Topics
- Demystifying Generative and Agentic AI: developing intuition for neural networks, LLMs, and agents
- AI Tools and Workflows: teaching AI to act; transforming processes into workflows; monitoring automations
- Data Infrastructure: pipelines, integrations, APIs, cloud tools, and enterprise constraints
- Enterprise AI Systems: architecture, reliability, and production considerations
- Industry Panel: Lessons from enterprises - ROI, build versus buy, risk management
- Harness Engineering: guardrails, governance, and security
- The Future of AI
Lab Sessions
Intensive labs led by faculty to guide you through building your own AI agents. Topics include:
- Prompt engineering, summarization, and text generation via a customized generative model
- Build an Agent - configure an agent and integrate it with an LLM
- Integrate your data - prepare data, build an MCP system, and integrate with an LLM
- Data-driven agentic AI
- Guardrails and Tracing
- Deployment
AI Project
In a capstone project, you will apply what you built in the labs to solve your own business challenge. Using the framework and agent-building skills from the workshop, you’ll identify a high-value AI opportunity in your organization, translate the relevant business process into an AI workflow, and design a solution suited to your company’s data, goals, and constraints. You’ll leave with a plan you can take straight back to your team, along with the judgment to evaluate ROI, risk, and governance before you commit resources.
Faculty & Program Leadership
Hardeep Johar is a Teaching Professor of Industrial Engineering and Operations Research at Columbia Engineering. He holds a Ph.D. in Information Systems from NYU Stern and serves as Program Coordinator for the MS in Business Analytics and MS in Management Science at Columbia Engineering. He brings extensive industry experience as a quantitative proprietary trader at Deutsche Bank, Credit Suisse, and Morgan Stanley, and has advised and served on the management teams of multiple technology startups. His teaching and research center on the practical applications of AI and machine learning in business, and he has taught widely in executive programs and participated in AI-focused industry panels.
Tony Dear is a Senior Lecturer in Computer Science at Columbia Engineering, where he teaches courses in artificial intelligence, mathematics, and robotics. He is the Faculty Director of Columbia Engineering's Online Artificial Intelligence Executive Education certificate program and the creator of a graduate course in Data-Driven Decision Modeling. His research focuses on the intersection of robotics and reinforcement learning, and he holds a Ph.D. in Robotics from Carnegie Mellon University.
Yi Zhang is a Senior Lecturer in the Industrial Engineering and Operations Research Department at Columbia Engineering, where he teaches data science, simulation, and business analytics. His research focuses on the economic impact of digital platform adoption, and he is passionate about incorporating digital technology into active learning environments. Prior to joining Columbia, he taught Econometrics and Advanced Business Analytics at Carnegie Mellon University's Heinz College, where he received his Ph.D. in Information Systems and Management.
Contact Us
Columbia Engineering Executive Education
[email protected]
Interested in a master's degree in artificial intelligence? Learn more about our online, part-time MS in AI.
Customize This Program For Your Organization
If you would like an exclusive offering of Agentic AI for Business for your organization's employees, our Executive Education team can work with you to modify this program to fit your needs.
Email [email protected] to learn more.
FAQ
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can independently plan, reason, and take actions to achieve a goal with limited human intervention. Agentic AI systems can make decisions, execute multi-step tasks, adapt to changing inputs, and coordinate workflows autonomously.
In business environments, agentic AI is increasingly used to automate complex processes such as customer support, research, operations management, software development, data analysis, and enterprise workflow orchestration. These AI agents can interact with tools, retrieve information, analyze data, and continuously refine outputs to improve efficiency and decision-making.
As organizations adopt more advanced AI capabilities, understanding how to evaluate, deploy, and govern agentic AI for business applications has become an important leadership skill.
How is agentic AI different from generative AI?
Generative AI focuses on creating content such as text, images, or code based on user prompts. Tools like large language models generate outputs in response to instructions but typically require ongoing human guidance for each task.
Unlike generative AI tools that respond only to prompts, AI agents are reasoning, planning systems that can orchestrate complex workflows, coordinate teams of specialized AI agents, and act autonomously to achieve long-horizon goals. They can perform multi-step workflows, make decisions, use external tools, retrieve information, and adapt dynamically based on outcomes.
What do executives need to know about AI agents?
AI agents are transforming how organizations automate workflows, analyze information, and support decision-making across business functions. Unlike traditional software tools, AI agents can independently complete multi-step tasks, interact with systems and data sources, and adapt dynamically to changing objectives with minimal human oversight.
Executives must understand the strategic implications of agentic AI as organizations explore opportunities to improve efficiency, productivity, and customer experience. As agentic AI capabilities continue to evolve, executives who understand how to responsibly deploy and manage AI agents will be better positioned to lead organizational transformation and maintain competitive advantage in an increasingly AI-driven business environment.