---
title: "AMS Agentic System: AI Framework for Application Management"
description: "A modular-scalable, domain-adaptive, technology-agnostic AI framework for Application Management Services, delivering up to 50% savings in operational costs."
scfAsset:
  maintainers:
    - user: "tobias.mueller"
  managed: true
  marketplaceUrl: "https://marketplace.stackit.cloud"
  category: "service"
  external: true
  tags: ["Agentic-AI", "Multi-Agent", "AMS", "Automation", "Orchestration"]
source_url: "https://framework.stackit.cloud/adoption/assetcontainer/capgemini/capgemini-ams-agentic-system/"
source_file: "docs/adoption/assetcontainer/capgemini/capgemini-ams-agentic-system.mdx"
---

<Aside type="note" title="Asset Profile">
  **Focus:** Agentic AI for Application Managed Services **Model:** Managed Asset (Professional
  Service by Capgemini)
</Aside>

## Overview

**AMS Agentic System** — **A modular-scalable, domain-adaptive, technology-agnostic AI framework for Application Management Services.**

The **Capgemini AMS Agentic System** is a highly flexible enterprise asset that operationalizes agentic AI by orchestrating multi-agent workflows across data, processes, and related applications to accelerate end-to-end execution in Application Managed Services (AMS).

This system is designed to operate like a **Digital Workforce** by continuously sensing signals, understanding context, and autonomously resolving issues across the entire AMS lifecycle.

It enables organizations to industrialize AI-driven AMS operations, delivering up to **30% MTTR reduction** and up to **25% ticket reduction**, which can ultimately lead to **50% savings in operational costs** through intelligent automation, optimization, and continuous service improvement.

By integrating sovereign, secure, and compliant AI capabilities, the platform improves decision-making speed and quality, while ensuring full governance and trust across AI lifecycles. As a result, clients achieve measurable business impact and significantly enhanced scalability of AI-driven operations across industries.

---

## Typical Capabilities

- **Agent Orchestration:** Coordination of multiple AI agents to execute complex, multi-step IT and business processes across systems and domains following a simple execution pattern: Trigger → Intent → Action.
- **Multi-Agent Collaboration:** Specialized agents working together (e.g., planner, executor, validator) to solve even complex tasks efficiently.
- **Autonomous Decision-Making:** Rule-based and model-driven decision logic enabling agents to act independently within defined guardrails.
- **Workflow Automation:** End-to-end automation of IT and business processes, including exception handling and dynamic task adaptation.
- **Tool & API Integration:** Seamless integration with enterprise systems (e.g., ITSM, ERP, CMS) via APIs and connectors.
- **Human-in-the-Loop (HITL):** Built-in escalation and approval workflows allowing human validation for critical or ambiguous decisions.
- **Prompt & Policy Management:** Centralized management of prompts, policies, and governance rules to ensure consistency and compliance.
- **Security & Compliance Controls:** Role-based access, data protection, and adherence to enterprise governance and regulatory requirements.
- **Explainability & Traceability:** Transparent logging of agent actions, reasoning paths, and decisions for audit and compliance purposes.
- **Monitoring & Analytics:** Real-time dashboards and KPIs to track agent performance, usage, and business impact.

---

## Typical Fit Criteria

The asset is particularly suitable if the following conditions are present:

- **High Process Complexity & Autonomy Potential:** The customer operates complex, multi-step business processes (e.g., across functions or systems) that can benefit from autonomous or semi-autonomous AI agents making decisions and taking actions.
- **Fragmented System Landscape:** The IT landscape consists of multiple disconnected systems, APIs, or data sources where orchestration through intelligent agents can reduce manual integration effort.
- **Need for Intelligent Automation beyond RPA:** Traditional automation (e.g., RPA, workflows) has reached its limits, and there is a demand for adaptive, context-aware, and goal-driven automation.
- **Data Availability with Actionable Context:** Sufficient structured and/or unstructured data is available (documents, tickets, conversations, logs) that agents can use for reasoning, planning, and execution.
- **Use Cases Requiring Decision-Making:** Target scenarios involve not just task execution but also **dynamic decision-making**, prioritization, or exception handling (e.g., service operations, customer support, supply chain).
- **Scalability Requirements:** The organization aims to scale AI-driven use cases across multiple business units, requiring a **platform-based approach** rather than isolated pilots.
- **Integration with GenAI Ecosystem:** The customer is already exploring or using GenAI (e.g., copilots, LLMs) and needs a **structured way to operationalize agents** within enterprise workflows.
- **Governance, Security & Compliance Needs:** There is a requirement for **controlled, auditable, and secure deployment** of AI agents, especially in regulated industries.
- **Change Readiness & Operating Model Evolution:** The organization is open to evolving its operating model (e.g., human-agent collaboration, new roles, AI supervision frameworks).
- **Business Value Focus:** Clear KPIs are defined such as cost reduction, cycle-time improvement, service quality, or revenue enablement through AI-driven automation.

---