Over the last two years, AI agents have exploded across the enterprise landscape. Marketing teams prototype assistants for campaign analytics. Operations teams build copilots for scheduling and logistics. Finance experiments with reconciliation agents. Product teams embed autonomous workflows into customer-facing features.
This surge signals a new phase of digital transformation. AI is no longer confined to data science teams; it is becoming part of everyday work. But the rapid proliferation of agents often outpaces the structure required to govern them.
The result is a modern AI Wild West, defined by creativity and rapid exploration, but also confusion, duplication, and uneven risk management. In our latest “Global AI Confessions Report” surveying 800+ data leaders, we found that although 86% say their organizations rely on agents, 75% have trust concerns in agent deployment. While leaders can see flashes of value, they struggle to scale that value across teams. Instead, many organizations now face scattered agents, inconsistent development practices, and limited visibility into what exists.
Forward-thinking organizations are now shifting from scattered innovation to strategic, scalable, and governed agent ecosystems. They are building an AI Main Street, a more coordinated and governable environment where agents are discoverable, trusted, reusable, and aligned with business priorities. Reaching that point requires an enterprise-wide approach to standardization, visibility, and lifecycle management.
Why Early AI Momentum Often Turns Into Fragmentation
Every enterprise is racing to implement agents, but teams are innovating in isolation. As a result, there is low visibility into which agents exist or how they work, business teams unknowingly tackling the same problem in different ways, IT chasing down shadow AI, and no way to scale successful agents.
Without a standardized, governing structure to support controlled innovation at scale, even the most promising agent initiatives struggle to mitigate risks and deliver long-term impact.
The Shift Toward AI Main Street
AI Main Street reflects a more mature and sustainable model for enterprise AI. Instead of isolated efforts, organizations build a centralized, connected ecosystem where agents can be discovered, evaluated, reused and monitored with confidence. This creates a pathway that balances innovation with control, letting businesses expand agents safely and consistently.
A shared foundation does not limit creativity. Instead, it gives teams the confidence to build more ambitious workflows, knowing they are working within a framework designed for scale and reliability.
The Benefits of a Connected Agent Ecosystem
A structured approach to AI agents brings several essential capabilities that help organizations move beyond experimentation. At Dataiku, we recently launched Agent Hub, a collaborative workspace within The Universal AI Platform™, to bridge the gap between isolated experimentation and scalable innovation.
Whether you’re building simple task-focused agents or complex multi-agent workflows, Dataiku enables both code and no-code development, powered by your enterprise data and deeply embedded across your business systems. Agent Hub centralizes all agent activity, so every employee can easily discover, use, and create approved AI agents, while IT retains complete control of the entire agent lifecycle. As a result, agents evolve into mission-critical enterprise assets: connected to business data, orchestrated across teams, and built for controlled innovation at scale.
Clear Standards That Support Innovation
Organizations benefit when development, review, and data access practices follow a consistent model. As part of Dataiku, Agent Hub extends the same foundations that enterprises already rely on for analytics and ML to agents.
- Flexibility: Connect to any cloud, LLM, data source, and business system with the Dataiku LLM Mesh.
- Oversight: Deploy and monitor all agents alongside existing data, analytics, and ML workflows.
- Governance: Enforce consistent policies for all AI governance in one place, with built-in sign-off, explainability, and risk management at every level.
These shared standards make it easier for teams to build responsibly while maintaining compliance, quality, and cost.
Shared Components and Reusable Patterns
Common templates, connectors and evaluation methods reduce the cost and complexity of development. In Agent Hub, business users can:
- Create an agent in seconds using natural language on a single page.
- Jump start building with 20+ prompt templates for common business use cases.
- Leverage approved LLMs, data, tools, and agents already governed by IT.
Instead of waiting in IT backlogs or starting from scratch each time, teams are empowered to build their own solutions in a controlled environment, fostering innovation, faster rollouts, and higher-quality outcomes.
Visibility Into the Full Agent Lifecycle
AI agents evolve as data shifts and business needs change. A unified view of agents helps teams identify what is being used, what is duplicated, and where performance gaps exist. Ongoing monitoring, version tracking, and refinement ensure that agents remain accurate, compliant and effective over time.
Agent Hub centralizes all agent activity in a single place. Once a user creates an agent, it automatically becomes a Dataiku-managed asset, alongside Enterprise Agents, letting IT:
- See and monitor all agent activity.
- Support any trouble shooting.
- Share it enterprise-wide to scale business impact.
With this full view, leaders gain clarity about the value delivered and can make informed decisions about where to invest or consolidate.
Better Discovery for Employees
When people across the business can easily find, understand, and use approved agents, adoption grows naturally. With the Agents Library, every employee can access approved, trusted agents all in one place. All access rights are enforced by IT, so employees can get started faster without putting data, compliance, or budgets at risk.
Task Coordination and Orchestration
In order to successfully complete tasks with multiple steps across multiple systems, agents need to be able to navigate across a mix of modern, legacy, and third party systems. They need to understand how to connect to APIs, handle varied authentication schemes, and translate data between systems. This requires robust orchestration to ensure agents can interact without errors, maintain context, and respect compliance or data residency rules.
Agent Hub’s orchestration layer transforms agents from standalone utilities into a dynamic, business-ready taskforce. They allow agents to operate together efficiently, safely, and at scale. Users can simply:
- Ask any question.
- Get it routed to the right agent or coordinated across multiple agents for complex tasks.
- Automatically create dynamic presentations, directly from the agent’s outputs, using Dataiku Stories.
Why This Approach Matters
A connected, discoverable and well-governed agent ecosystem helps enterprises scale AI in a way that is sustainable and aligned with business goals. Instead of dozens of unrelated experiments, organizations gain a coordinated framework that supports innovation, reduces risk, and improves operational clarity.