The German Federal Employment Agency automates Jira processes with AI agents
Client: Federal Employment Agency (Bundesagentur für Arbeit)
Region: Germany
Industry: Public sector
Building a digital backbone
The Federal Employment Agency (BA) manages a high volume of complex IT changes and user stories. Turning these into structured Jira tickets used to take significant manual effort. Headcount was tight, workloads were rising, and quality and privacy had to stay high. The BA needed a scalable way to process tickets without compromising standards.
The challenge
Manual ticket creation slowed teams and introduced inconsistency. Copy-paste work, reformatting, and summarizing consumed hours that could be better spent on analysis and delivery. At the same time, strict data protection rules limited the use of cloud services, pushing for an on-premises approach.
The solution: a multi-agent AI system with human oversight
The BA and Capgemini co-developed a coordinated system of AI agents that turns Requests for Change (RFCs) and user stories into complete Jira tickets. A reader agent extracts relevant details. A planner agent breaks the work into clear steps. A creator agent drafts the ticket with title, description, category, and metadata. A reviewer agent checks consistency and duplicates before a human approves.
The system was built for scale and maintainability. It handles large RFCs and respects LLM token limits. Training and user enablement were part of the rollout, so teams knew how to work with the new flow and keep quality high.
Technology and security
The solution runs entirely on-premises inside the BA's secure infrastructure. Privacy-compliant models such as Aleph Alpha, LLaMA, and Mistral are orchestrated via CrewAI, an open-source platform for multi-agent coordination. Integration with the local Jira environment required close collaboration across operations, security, and development teams. Data stays inside the agency at all times.
Implementation and results
Following integration, the BA launched a pilot with selected departments. Early outcomes were strong: the multi-agent system now performs most ticket creation steps automatically. Routine tasks like copy-pasting, summarizing, and formatting are handled by the agents. Staff stay in control and focus on oversight, analysis, and decisions.
- Significant reduction in manual effort for ticket creation
- Improved consistency and quality of ticket content
- Fully privacy-compliant, on-premises implementation
- Scalable solution with potential across public administration
- Strengthened innovation capabilities within the organization
Governance built in
A human-in-the-loop step ensures quality and accountability. The reviewer agent flags duplicates and mismatches before final approval. This keeps content accurate while maintaining control, auditability, and trust.
Beyond ticketing: a platform for smarter operations
The BA now has a practical blueprint for deploying and managing multi-agent AI in the public sector. Next use cases include document classification, support for administrative workflows, and citizen-facing communication. The architecture is modular, so new capabilities can be added without disrupting existing services.
What leaders can learn
- Start with a high-volume, rules-based workflow and measure the manual effort you remove.
- Keep a human in the loop to protect quality, privacy, and adoption.
- On-premises AI is viable with the right models and architecture.
- Invest in training and change management to capture value, not just deploy technology.
- Design for scale from day one: document patterns, monitoring, and handoffs between agents.
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"With Capgemini, we found an innovative partner who shares our vision of intelligent, secure, and practical AI integration. This project shows how technology can be used meaningfully to empower our workforce and make public administration future ready." - Uwe Henkel, Head of Automation Center of Excellence, IT System House of the Federal Employment Agency (BA)
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