Super Agents Are Here 

Over the past two years, enterprises have embraced copilots as a way to accelerate individual productivity. They lowered the barrier to AI adoption, delivered quick wins, and helped teams move faster. Yet as copilots multiplied across functions, a different pattern began to emerge. Every team started launching its own agent, often optimized for a local need or a single workflow. 

At the same time, SaaS providers and off-the-shelf enterprise platforms began embedding their own agents directly into products, each introducing new interfaces, behaviors, and decision logic into the organization. What initially felt like progress gradually turned into a form of agent sprawl, with intelligence distributed across tools, vendors, and teams, but without a unifying structure. 

As this proliferation accelerated, complexity began to grow faster than the value it produced. Ownership became unclear, governance fragmented, and organizations struggled to understand how decisions were made or actions were taken. This is the moment where agentic governance becomes unavoidable. Not as a layer of restriction, but as the operating logic that allows intelligence to scale safely. When governance is designed into the orchestration layer from the start, organizations can move beyond fragmented agents and vendor-driven automation toward a unified intelligence layer that works across teams, systems, and platforms. 

From tools to operating layers 

Enterprises do not operate on isolated tasks. They run on processes that span systems, data, identities, and policies, often crossing multiple departments and platforms. When AI is introduced without coordination, organizations inherit familiar problems in a new form: inconsistent answers, unclear ownership, limited observability, and growing operational risk. 

Traditional automation addressed this through rigid, deterministic workflows. Agentic systems behave differently. They reason, adapt, and make decisions, which is precisely why they require a new kind of control. Orchestration provides that control by enabling systems to understand intent, decompose complex objectives, delegate work to specialized agents or tools, and coordinate multi-step workflows across the enterprise, all while operating under clear governance, security, and observability constraints. 

This shift is not about improving a user interface. It represents a fundamental change in how enterprise systems are designed and operated. 

Agents versus skills 

As organizations begin to build these systems in earnest, an important distinction is becoming clear. Many capabilities that are described as agents are, in reality, skills. They retrieve information, execute a command, or perform a single, well-defined task. Skills are essential, but on their own they do not deliver outcomes. 

Agents operate at a different level. They plan, decide which skills to invoke, determine the order in which work should happen, and adapt their behavior based on context and constraints. Orchestration is what turns collections of skills into enterprise intelligence. Without it, organizations risk repeating the microservices problem of the past, where powerful components exist without effective coordination. With orchestration in place, skills become reusable infrastructure and agents become outcome-driven operators capable of working across systems and domains. 

This is where AI begins to scale meaningfully. 

The most powerful agents work in the background 

Early AI assistants are designed to be visible. They live in chat interfaces, announce themselves clearly, and wait for prompts. As systems mature, that visibility becomes less important than reliability. 

The most valuable agents operate quietly in the background, monitoring signals, routing work, enforcing policy, and resolving issues before a human needs to intervene. Humans are looped in only when judgment, creativity, or explicit approval is required—prompted proactively by the agents themselves, not by someone watching the system run. This is why leading organizations are converging on a single intelligent access layer for employees, supported by a coordinated network of agents operating underneath it. 

In this model, AI stops being a tool that employees use and becomes an operating layer that the organization runs on. 

Why this shift is happening now 

Until recently, this approach was difficult to implement at enterprise scale. That has changed as several capabilities have matured simultaneously. Enterprise AI platforms now support evaluation, versioning, and policy enforcement. Identity-first architectures allow agents to act securely on behalf of real users using least-privilege access. Observability tooling makes it possible to trace and audit complex multi-agent interactions end to end. 

Together, these advances make agentic orchestration not only feasible, but production-ready. 

Governance as an enabler, not a constraint 

Many AI pilots struggle because governance is introduced too late in the process. Identity is added after the fact, guardrails are bolted on in response to incidents, and observability is treated as an afterthought. The builders who succeed take a different approach. 

When identity propagation, access control, data boundaries, evaluation gates, and audit trails are embedded from the start, autonomy becomes safe. Governance does not slow innovation. It enables organizations to scale AI systems with confidence. At enterprise scale, orchestration without governance is not experimentation. It is exposure. 

A public signal from the market 

This shift is already visible in the market. In our engagement with Levi Strauss & Co. we discovered how its AI journey evolved from deploying isolated agents to pursuing a broader ambition. After early success modernizing IT services and expanding agentic workflows across HR, Legal, Investor Relations, and ERP operations, the organization recognized the need for a more cohesive approach. 

As Jason Gowans, CTO & Chief Digital at Levi Strauss & CO. explains at Microsoft Ignite; 

“Once we did that, we realized 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.”

This moment marks a transition from experimentation to enterprise-scale orchestration, and from individual agents to an intelligence layer that spans the organization. 

Orchestration as the new competitive skill 

The advantage in the next phase of enterprise AI will not come from access to models. It will come from the ability to coordinate them effectively. Organizations that lead will design orchestration flows rather than isolated prompts, treat agents as infrastructure rather than applications, and operate AI systems with the same rigor applied to any other critical platform. 

The future of enterprise AI will not be loud. It will be reliable, governed, and largely invisible. And it will run on orchestration. 

Where to go from here

Many customers have built their first agents, but Agentic Orchestration is still very much a new topic. Read ahead to learn how Xebia can help you harness the power of agents in your organization.