Background
Siemens Energy wanted to scale AI agents across the entire organization. The HR department sought a specialized partner and engaged FNTIO directly.
Challenge
- Growing number of departments wanting their own AI agents
- Teams need to create, host, and monitor agents independently
- Integration into existing business applications
- Enterprise requirements: Security, compliance, monitoring
Solution
Self-service agent platform with agent lifecycle management:
- Agent Lifecycle: Build, deploy, monitor in one platform
- LiteLLM: Access to diverse AI models (Claude, GPT, Gemini)
- Business Integration: Agents interact with HR systems (COIN, HR DataHub)
- Forward-Deployed Engineering: FNTIO coaches teams in building their own agents
Agents on the Platform:
| Agent | Function |
|---|---|
| Coco AI (HR) | Explains management processes, knows the organization, integrates HR data |
| AWS Architect Agent | Self-healing: Analyzes CloudWatch errors, posts root-cause analyses in Slack |
| Data Transform Agents | Write Python code for legacy integrations, deploy automatically |
| Voice Agent | Voice-based interaction for HR processes (real-time) |
Why FNTIO?
Siemens Energy had seen with SiemensGPT what FNTIO had built at the parent company. They wanted the same approach: working AI agents in weeks, directly in production.
Results
| Metric | Value |
|---|---|
| AI Agents in Production | 4+ (Coco AI, AWS Architect, Data Transform, Voice) |
| Agent Creation | Self-service for enterprise teams |
| Model Access | Multi-provider via LiteLLM (Claude, GPT, Gemini) |
| Integration | HR systems (COIN, HR DataHub), Slack, CloudWatch |
| Deployment | Fully automated via Fargate + Lambda |
Differentiation
- Not just chatbots: Agents with real system permissions
- Computer Use: Agents write and deploy code
- Voice-first: Real-time Voice Agent as the next interaction level
- Platform, not project: Scalable for the entire organization
Your Starting Point: Self-Service Agent Platform
Your business units want AI agents, but central IT cannot keep up with demand? Here is how we close the gap.
Week 1: Prioritize use cases. Together with your teams, we identify the 3-5 AI agent use cases with the highest impact. Not in a strategy workshop, but through conversations with the people who experience the problem every day.
Week 2: First agent in production.
A concrete agent for the prioritized use case.
Embedded in your systems, with real permissions, under your security framework. Not a chatbot, but an agent with system access.
Week 4: Platform for self-service. The base platform is in place: Your teams can create, deploy, and monitor their own agents. Multi-model access (Claude, GPT, Gemini) without vendor lock-in. Agent lifecycle management integrated.
What Siemens Energy achieved after this point: Self-service agent creation, 4+ productive agents (HR, self-healing, data transform, voice), fully automated deployment.
