Agentic interface for mainframes and COBOL
Hopper — AI Agents for Mainframe Operations - Hypercubic
What is Agentic interface for mainframes and COBOL?
Hopper is an AI interface designed to connect artificial intelligence agents directly to mainframe systems running z/OS. It uses Anthropic's Model Context Protocol to allow teams to interact with mainframes through natural language instead of traditional command-line interfaces or COBOL code. This means developers and operators can describe what they want to accomplish in plain English, and the AI agents handle the underlying mainframe operations. The tool is particularly relevant for organisations with legacy mainframe infrastructure that want to modernise their workflows without replacing existing systems. It includes a development environment for building and customising agentic workflows tailored to your specific mainframe needs.
Key Features
Natural language interface
Issue commands and queries to mainframes using everyday language rather than COBOL or JCL syntax
Model Context Protocol integration
Connects AI agents to z/OS systems for structured communication and task execution
Autonomous workflows
Create and deploy agents that can perform multi-step mainframe operations without manual intervention
Agentic development environment
Build custom agents and workflows specific to your organisation's mainframe requirements
Freemium model
Try the tool free with basic functionality before committing to paid tiers
Pros & Cons
Advantages
- Reduces technical barrier to mainframe operations by allowing natural language interaction instead of requiring COBOL expertise
- Preserves existing mainframe investments whilst adding modern AI capabilities on top
- Automates routine mainframe tasks through autonomous agents, reducing manual effort
- Development environment lets teams build custom solutions tailored to their specific workflows
Limitations
- Limited to z/OS systems; won't work with other mainframe platforms or legacy architectures
- Requires careful security considerations when connecting AI agents to production mainframe systems
- Learning curve for teams unfamiliar with agentic development patterns and Model Context Protocol concepts
Use Cases
Automating routine batch job submissions and monitoring on mainframes
Querying mainframe databases and VSAM files through conversational AI instead of writing SQL or COBOL
Handling after-hours mainframe operations with autonomous agents that require minimal human oversight
Onboarding new team members by providing natural language access to complex mainframe systems
Building custom operational dashboards and alert systems that summarise mainframe status in plain language