IntegraAI: An Event-Driven Framework for Intelligent Process Automation

Modern enterprises run on fragmented systems — ERP, CRM, mailboxes, calendars, cloud storage, databases — each holding context the others need. Connecting them traditionally means brittle point-to-point integrations that grow harder to maintain with every new requirement. Together with ADIVEX, we built a different approach: a decentralized, event-driven framework where autonomous AI agents handle coordination and automation across all of them.

Agents that react instead of orchestrate

The architecture is built on a Kafka event bus. Independent agents subscribe to the events they understand and publish new events in response — no central orchestrator, no static pipeline. A user request enters through a router agent that recognizes intent, extracts entities, and routes downstream: to a workflow agent for multi-step processes, a data agent for retrieval, or directly to a connector for a simple action. Workflows emerge dynamically from chains of events rather than from top-down instructions.

This loose coupling means any component can be added, replaced, or scaled independently. The LLM sits at the reasoning layer, not at the coordination layer — it emits structured, schema-validated commands that the framework validates and routes. The model never talks to external systems directly.

Connecting the systems you already use

Connectors bridge the framework to existing infrastructure: ERP and CRM systems via APIs and database queries, email via IMAP/SMTP, calendars (Google, Office 365), cloud storage and file shares, Microsoft Teams, WhatsApp, code repositories, and more. Specialized services handle document processing — OCR, invoice generation including ZUGFeRD/XRechnung, text-to-speech and speech-to-text, and image classification.

Each connector exposes its capabilities as typed Kafka events, defined once in a shared schema. An Event SDK generates DTOs, serializers, and Kafka producers/consumers from that schema for different programming languages, making it straightforward to extend the system with additional connectors or services.

Secure retrieval with RAG

All connected data sources contribute to a shared vector knowledge base. When an agent needs context, retrieval is always security-trimmed — filtered by tenant, organizational area, role, and user — before anything reaches the LLM prompt. The model only ever sees what the requesting user is permitted to access. This is what makes it safe to operate a shared AI assistant across organizational boundaries without risking cross-tenant data leaks.

Observability and compliance built in

Auxiliary services cover the operational baseline from day one: an audit logger records every data access and tool call with full correlation context, a telemetry logger aggregates per-tenant metrics for Grafana/Prometheus dashboards and SLO alerts, and a policy gatekeeper validates all prompts and tool execution requests before they are processed further. Every action is traceable and attributable. Component health is tracked continuously via a component registry that rejects requests to offline components early — no silent timeouts.

Available as a managed service

The framework was developed together with ADIVEX, combining their expertise in enterprise integration with our experience in scalable software architecture. It is production-ready and deploys as cloud microservices on Kubernetes, or in a hybrid setup with local agents running on user machines for scenarios where data must not leave the premises.

If you are looking to automate business processes, connect existing systems with AI, or build a context-aware assistant for your organization — we offer the framework as a managed service and can adapt it to your infrastructure and requirements. Get in touch.