Customer Stories
Governing Agentic AI at Enterprise Scale
As AI agents multiplied across the enterprise, governance and control became a growing risk. Xebia implemented a governed Super Agent to orchestrate existing agents through a single, secure interface, enabling AI to scale safely.
Introduction
As enterprises move beyond isolated copilots, a new challenge is emerging. AI agents are multiplying across HR, IT, finance, and operations, each helpful on its own, but increasingly difficult to govern as a whole.
For a global apparel and lifestyle brand operating at enterprise scale, this challenge became apparent early. The organization had already invested in multiple AI-powered agents across critical business systems. Productivity gains were real, but so were the risks: fragmented access, inconsistent behavior, limited observability, and unclear ownership.
Rather than continuing to add more agents, the organization made a strategic decision: AI needed orchestration — not proliferation.
At a Glance
The Challenge
As AI agents spread across enterprise systems, the organization faced growing risks around governance, security, observability, and control.
The Solution
Xebia designed and implemented a GenAI-powered Super Agent that orchestrates multiple specialized agents through a single, governed interface.
The Results
The organization
established a secure, observable, and scalable foundation for agentic AI, turning experimentation into an enterprise-grade capability.
Business Challenge
The organization had already deployed several AI-enabled assistants across domains such as HR, IT services, ERP, and internal knowledge. Each agent solved a local problem, but collectively they introduced new complexity.
Key concerns began to surface:
- Employees needed to remember which agent to use for which task
- Access control and impersonation varied by system
- There was no single point of observability across agent interactions
- Security teams lacked a clear audit trail for AI-driven actions
- Rapid changes in agent frameworks and protocols increased architectural risk
Leadership recognized that without a unifying control layer, scaling AI would introduce operational and compliance exposure rather than competitive advantage.
Solution
Xebia partnered with the organization to design and build a Super Agent: a GenAI-powered orchestration layer that acts as a single, intelligent access point to enterprise agents.
Instead of replacing existing agents, the Super Agent:
- Understands user intent expressed in natural language
- Determines which specialized agent or system should be involved
- Coordinates multi-step workflows across systems
- Enforces identity, authorization, and policy consistently
- Centralizes observability and governance
The result is not “another bot,” but an unified intelligence layer for agentic AI.
Implementation
The Super Agent was implemented as a secure, Azure-native solution, accessible directly through Microsoft Teams.
Key implementation principles included:
- Identity-first design: all actions are performed on behalf of authenticated users, with role-based access control and impersonation enforced end to end
- Governed orchestration: only approved agents can be invoked, avoiding uncontrolled agent sprawl
- Observability by design: telemetry, tracing, and logging were embedded to support auditing and operational insight
- Extensibility: a plugin-based architecture allows new agents to be added without disrupting the system
- Future readiness: the design anticipates evolving agent protocols and frameworks without locking into immature standards. Currently it supports MCP, A2A and some specific proxies created for systems like SAP.
Xebia worked closely with the organization and platform partners to ensure the solution balanced innovation with enterprise-grade security expectations.
The Super Agent established a clear shift from experimentation to control
Results
A single, unified interface for interacting with enterprise agents
Consistent enforcement of identity, permissions, and access policies
Improved transparency into AI-driven actions and decisions
Reduced risk of agent sprawl and inconsistent behavior
A scalable foundation for future agent expansion
Most importantly, the organization gained confidence that AI could scale without compromising governance, security, or trust.
Lessons Learned
Agentic AI amplifies both value and risk without orchestration
Governance is most effective when designed in from the start
Identity, observability, and control are prerequisites for scale
Orchestration enables autonomy, it does not limit it
Future
With a governed orchestration layer in place, the organization is now expanding its AI ambitions with confidence.
Next steps include:
- Integrating additional enterprise agents into the Super Agent
- Further hardening the security guardrails and monitoring for production readiness
- Evolving toward more advanced agent-to-agent collaboration
- Evolving from resolving questions to multiturn and complex agentic orchestration
- Positioning AI as a long-term operating layer rather than a collection of tools
What began as a proof of concept has become a strategic foundation, enabling the organization to scale agentic AI safely, responsibly, and sustainably.
We realized then that we had an opportunity to make a unified intelligence layer across the entire company, making it seamless for our employees. And so today we're working with Xebia and Microsoft to expand our super agent work.
Jason Gowans
CTO and Chief Digital at Levi Strauss
From Case to Capability
Based on experiences like this, Xebia has developed a Super Agent Accelerator to help organizations move from fragmented agent experimentation to enterprise-grade orchestration.
The accelerator is designed to fit into an organization’s existing environment, building on current agents, platforms, and security models rather than replacing them.
A Phased Approach to
Enterprise Agentic AI Orchestration
Phase 1
Assessment & Discovery
The engagement starts with a focused assessment of the current state, including:
◦ Inventory on current AI strategy and running initiatives
◦ Inventory of existing AI agents and AI supported Systems
◦ Review of enterprise systems, identity models, and access patterns
◦ Assessment of security, governance, and observability readiness
◦ Identification of high-value orchestration scenarios
◦ Fit/gap analysis on our accelerator and current state of AI platform
The outcome of this phase is a clear baseline and a prioritized roadmap towards a coherent AI strategy with agentic orchestration as a strategic choice.
2 - 4 weeks
Phase 2
Foundation & Initial Implementation
Based on the assessment, Xebia sets up the core Super Agent foundation:
◦ Make required changes to Super Agent Accelerator to fit the customer environment
◦ Deployment of the Super Agent accelerator into the customer environment
◦ Configuration of infrastructure, identity, and security guardrails
Integration of selected existing agents
◦ Implementation of the first end-to-end scenarios through the Super Agent
◦ Enablement of value tracking and observability
At the end of this phase, organizations have their first agent orchestrations
live via a governed Super Agent
interface, delivering immediate business value.
4 – 6 weeks
Phase 3
Expansion & Optimization
Once the foundation is in place, organizations can expand at their own pace:
◦ Introducing multi-turn, context-aware scenarios
◦ Supporting more complex agentic orchestration across domains
◦ Adding new agents and systems to the orchestration layer
◦ Further tailoring governance, policies, and controls to organizational needs
This phased approach allows organizations to scale agentic AI safely, while continuously adapting the system to real-world usage and evolving requirements.
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