AI Software Development Program with AI Agents

The program features MIT faculty, including Professor Tim Kraska, Professor Armando Solar-Lezama, Professor Paul Liang, and Andrew Wang. Ziniu Wu is also listed among the program faculty. Industry experts provide live mentorship sessions to support practical learning.
Note: Faculty participation is subject to change.

Participants explore three tools in the Claude ecosystem:
● Claude Code: Used for application development, coding workflows, and API integration.
● Claude Chat: Used for model selection and prompt engineering.
● Claude CoWork: Used for agentic workflow design, orchestration, and multistep execution through the Plan → Approve → Execute → Iterate framework.
(Note: The program specifies that Claude API access is not included as part of the enrollment, though participants can explore advanced usage independently.)

The curriculum covers Generative AI and large language models, agentic AI fundamentals, requirements and architecture, testing and quality assurance, ontology-driven development, organizational strategy, AI-enabled software systems, security, compliance, and production deployment. It concludes with an end-to-end capstone project.

Yes. Participants complete practical exercises and projects involving AI-assisted application development, agent design, testing, and system integration. Sample projects include a meeting-notes action agent, specification-driven feature development, support ticket triage, and an AI-enabled internal wiki connected through an MCP server.

The capstone involves designing, building, evaluating, and securing an end-to-end agentic application for a real-world enterprise problem. Participants choose a project direction relevant to their role or industry, such as an invoice processing agent, IT helpdesk agent, patient intake agent, or insurance claim triage agent, and present their work during the final-week showcase.

Yes. Participants study AI-assisted test generation, evaluation metrics, human-in-the-loop review, prompt-injection risks, data leakage, privacy, and compliance. They also explore observability, monitoring, reliability, fallback strategies, and production deployment to help make AI-enabled applications more dependable.

Agentic AI improves enterprise software development by automating multistep tasks, connecting tools and APIs, and assisting with coding, testing, and workflow execution. With appropriate human oversight and guardrails, AI agents can help streamline development workflows, improve consistency, and support the delivery of reliable software. The MIT Professional Education Enterprise Software Development with AI Agents program explores how to apply these capabilities in practice. Through hands-on projects, participants build tool-using agents, translate business requirements into precise specifications, evaluate AI-generated code, and address security, reliability, and deployment challenges.

Program synthesis involves generating software from specifications or defined requirements. In AI-assisted software development, it connects business intent with code generation and verification. The Enterprise Software Development with AI Agents program explores these principles through spec-driven development, structured specifications, and verification of generated implementations. Its faculty includes Professor Armando Solar-Lezama, whose research focuses on program synthesis and neurosymbolic programming.

The program is designed for professionals looking to adopt AI-native software development practices, including:
● Software Developers, Solution Architects, and Technical Product Managers: Those seeking to move beyond basic AI-assisted coding to agent orchestration and system design.
● Functional Product Leaders: Those looking to translate business requirements into precise, machine-executable specifications and collaborate with engineering teams.
● Business Analysts and Systems Analysts: Those seeking to understand AI-driven software development and build systems that meet enterprise and regulatory requirements.

You can apply by completing the online application form. A panel from Great Learning reviews applications to assess each applicant's fit for the program. After the final review, selected applicants receive an offer for a seat in an upcoming cohort.

The program introduces Generative AI, large language models (LLMs), and Agentic AI fundamentals before progressing to advanced applications. Pre-work covers command-line basics and setup of tools such as Python, VS Code, Docker, and Claude Code. Please check the eligibility requirements with the Great Learning Program Advisor.

Please contact your Program Advisor for the current program fee, applicable taxes, and payment options.

No, the program does not include complimentary access or API credits for Claude. Claude API access is not included as part of the program, though participants may choose to explore advanced usage independently.

The program prepares professionals to build reliable enterprise AI applications through hands-on learning in AI agent development, specification-driven design, testing, security, and production deployment. Participants build tool-using agents, evaluate AI-generated code, implement guardrails, and apply observability and reliability practices. It culminates in a capstone project focused on designing, building, evaluating, and securing an end-to-end agentic application.

The best AI agent for software development depends on your programming language, project complexity, development environment, and integration needs. Claude Code can support AI-assisted coding and application development. The right approach depends on whether you need code generation, tool use, workflow orchestration, or end-to-end application development.

Use AI coding agents by first defining clear requirements, breaking work into manageable tasks, and providing precise specifications and relevant project context. Review generated code, run automated tests, evaluate outputs, and apply security checks before deployment. A structured workflow that combines AI generation with human review can help improve code quality, maintainability, and reliability. These practices align with the program's emphasis on specification-driven development, testing, and governance.

AI agent development skills can be relevant to roles involving AI-enabled software engineering, agentic application development, solution architecture, and AI systems integration. The skills developed through the program, including agent design, API integration, evaluation, security, and deployment, can help professionals contribute to enterprise AI projects. Specific job outcomes are not guaranteed by the program.

Learning software development with AI agents can help experienced developers move beyond code generation to system design, workflow orchestration, automated testing, and production readiness. This MIT Professional Education program emphasizes these capabilities alongside specification-driven development and organizational strategy, helping participants understand how to integrate AI agents into enterprise software engineering workflows.