Enterprise AI Agent Integration Layer
A production-oriented backend execution layer that lets AI agents call tools, trigger workflows, interact with enterprise APIs, and execute actions safely with logging, retries, audit trails, and monitoring.
Challenge
AI agents can reason about a task, but in most organisations they have no safe, governed way to act on real systems. Direct access is risky, integrations are fragile, and there is rarely an audit trail when an agent triggers a business action.
Solution
We designed a backend execution layer that sits between the AI platform and enterprise systems. Agents call validated, permissioned tools through an MCP/OpenAPI interface; each action is checked, executed asynchronously where needed, retried on failure, and recorded with a full audit trail and telemetry.
Business value
- Agents can act on real systems with controlled, auditable permissions
- Failures are retried and recorded instead of silently lost
- A clear path from AI prototype to a production-grade execution layer
Technologies
- Python
- FastAPI
- MCP
- OpenAPI
- PostgreSQL
- Azure Service Bus
- Databricks Jobs
- Docker
- Terraform
- OpenTelemetry
Relevant roles
- Backend AI Engineer
- AI Integration Engineer
- DevOps / Terraform Engineer
Next step
Discuss a similar project
We can adapt this pattern to your systems and provide the engineers to build it. Reach us at info@inovativi.com.