What challenges did Celonis face before AI implementation?

Coordinating appointments between internal resources and external partners required significant manual effort. Fragmented systems and human intermediaries caused scheduling delays in automotive production environments.

What was the impact of the AI solution?

The solution is positioned as a scalable blueprint for autonomous process orchestration. It is designed to reduce manual intervention in order-to-delivery scheduling. Agent logs are written back into Celonis to support continuous process improvement.

Source

Evidence: Low40/100

Celonis uses Amazon Bedrock AgentCore to deploy autonomous AI agents that orchestrate automotive production scheduling

Use case typeWorkflow orchestrationUpdated Jun 13, 2026

Celonis and AWS built an autonomous AI solution for coordinating production schedules across fragmented systems in automotive manufacturing. The agent uses process intelligence plus Amazon Bedrock AgentCore and MCP tools to retrieve order data, check partner availability, apply customizations, and write scheduling outcomes back into Celonis. The observability loop feeds agent logs back into Celonis so teams can refine the process over time.

Organization: Celonis
Industry: Automotive
Location: Germany
Published: June 2026

Reported outcomes

Strategic outcomes

Scale & capacityEnabled scalable autonomous process orchestrationSpeed & agilityReduced manual scheduling interventionBetter decisions & insightImproved process refinement with agent logs

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Celonis
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions Case Study

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Agentic AI
2Process Automation
3Workflow Orchestration

Coordinating appointments between internal resources and external partners required significant manual effort.
Fragmented systems and human intermediaries caused scheduling delays in automotive production environments.

Celonis and AWS co-developed an autonomous AI agent built on Amazon Bedrock AgentCore.
The Celonis MCP Server exposes tools for loading order data, checking partner resources, applying partner customizations, and triggering Celonis action flows.
The agent also connects to third-party calendar APIs to identify optimal time slots and schedules appointments automatically.

Technologies

Amazon Bedrock AgentCore Amazon Bedrock MCP Celonis MCP Server

The solution is positioned as a scalable blueprint for autonomous process orchestration.
It is designed to reduce manual intervention in order-to-delivery scheduling.
Agent logs are written back into Celonis to support continuous process improvement.

Architecture

The solution combines Celonis process intelligence with Amazon Bedrock AgentCore. A Celonis MCP Server exposes tools and action flows to the agent, which also integrates with external partner calendar APIs. AgentCore provides runtime deployment, gateway access to MCP servers, scoped identity/credentials, and observability. The observability loop writes logs back into Celonis for continuous improvement.

Sources & evidence1

Evidence: Low40/100Evidence strength