Build an AI Agent and Transform Your Work Without Writing a Single Line of 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 a three-day 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)
- 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
- Completion of a short pre-program reflection exercise
Program Schedule
Day 1
From Hype to Hands-On: Understanding Generative and Agentic AI
Day 1 demystifies generative and agentic AI for executive decision-makers - developing intuition for how these systems actually work without requiring a technical background. Participants leave with practical skills in steering AI tools and a clear picture of how to translate business processes into AI workflows.
Topics and Labs:
- Demystifying Generative and Agentic AI: developing intuition for neural networks, LLMs, and agents
- Lab 1: Prompt engineering, summarization, and text generation via a customized generative model
- AI Tools and Workflows: teaching AI to act; transforming processes into workflows; monitoring automations
- Lab 2: Build an Agent - configure an agent and integrate it with an LLM
Day 2
From Model to System: Data Pipelines and Enterprise Integration
Day 2 moves from individual models to enterprise systems. Participants explore the data infrastructure that powers real-world AI deployments - pipelines, APIs, cloud tools, and enterprise constraints - and build a working data-driven agentic system in the lab.
Topics and Labs:
- Data Infrastructure: pipelines, integrations, APIs, cloud tools, and enterprise constraints
- Lab 3: Integrate your data - prepare data, build an MCP system, and integrate with an LLM
- Enterprise AI Systems: architecture, reliability, and production considerations
- Lab 4: Putting it all together - data-driven agentic AI
Day 3
From Agent to Strategy: Deploying AI in Your Organization
Day 3 bridges technical understanding with organizational realities. Through a case study, an industry panel, and a hands-on workshop, participants develop the judgment and frameworks needed to lead AI deployment decisions with confidence.
Topics and Labs:
- Prescriptive Analytics: From Prediction to Action - case study
- Industry Panel: Lessons from enterprises - ROI, build versus buy, risk management
- Ethics and Reliability: hallucinations, bias, and ethical constraints - workshop
- Change management and organizational readiness for AI at scale
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.
Ali Hirsa is a Professor at Columbia Engineering and Director of both the Center for Artificial Intelligence in Business Analytics and Financial Technology and the Financial Engineering Program. He also serves as Chief Scientific Officer at ASK2.ai and Managing Partner at Sauma Capital, a New York hedge fund, bringing an active practitioner's perspective to his teaching and research. He is the author of 'Computational Methods in Finance' and holds a Ph.D. in Applied Mathematics from the University of Maryland.
Ansaf Salleb-Aouissi is a Senior Lecturer in Computer Science at Columbia Engineering, where she teaches AI and discrete mathematics and leads the PRAISE Lab (Practice and Research in AI for Science and Education). Her research applies AI to medicine, science, and education, and she is the founder of Aiphabet Inc., a nonprofit dedicated to bringing free, high-quality AI education to teens and pre-college students. She is the author of more than 40 peer-reviewed publications on AI and its applications. She earned her Ph.D. in Computer Science and Artificial Intelligence from the University of Orléans in France.
Kostis Kaffes is an Assistant Professor of Computer Science at Columbia University and a founding member of the Data, Agents, and Processes Lab (DAPLab), where he is building the foundations for AI agents to safely and reliably automate complex work. His research focuses on the systems and data scaffolding necessary for the reliable, safe, and efficient operation of LLM agents in real-world enterprise environments, including how AI agents can automate performance engineering for cloud workloads. He holds a Ph.D. in Electrical Engineering from Stanford University.
Devon Peticolas is an Adjunct Assistant Professor in Industrial Engineering and Operations Research at Columbia Engineering and a Staff Agent Engineer at Scale AI, building agentic AI solutions for enterprise customers. Previously, he was a Principal Engineer on the Data Science team at Oden Technologies, where he led machine learning deployment and data science infrastructure. His work focuses on getting agentic AI out of demos and into production enterprise workflows, with a current emphasis on turning data platforms into agentic context engines by integrating databases and filesystem sandboxes to create robust, enterprise-ready retrieval systems.
Contact Us
Columbia Engineering Executive Education
[email protected]
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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.