How Boomi is Advancing AI & ML Platforms for the Agentic Enterprise | ShortList Spotlight

AI agents are changing how enterprises interact with data, applications, and business processes. But as organizations deploy agents more quickly, a critical challenge is emerging: how do enterprises maintain control over agents that can access systems, move data, and take action?

In the latest Constellation ShortList™ Spotlight, R "Ray" Wang examines the AI and Machine Learning Best of Breed Platforms category and explains why governance is becoming a central requirement for enterprise AI.

What are AI and machine learning best-of-breed platforms?

AI and machine learning best-of-breed platforms provide the infrastructure and capabilities organizations need to build intelligent applications using data.

According to Ray, these platforms need to handle complex data ingestion, evolving data, difficult-to-identify signals, model development, interactive models, and improvements in model generation and accuracy. But the requirements are expanding as enterprises move into an agent-driven environment.

The next challenge is enabling AI systems to make context-aware decisions and translate them into action across business processes.

That makes decision velocity a central objective.

Why agent governance matters

AI agents are increasingly capable of interacting with multiple enterprise systems. They can access information, move business data, use tools, and take actions.

That creates a different governance challenge from traditional AI applications.

As Ray puts it, enterprises need governance across three areas:

Data. Tools. Actions.

Controlling only the data an agent can access isn't enough. Organizations also need to understand which tools an agent can use, what actions it can take, and under what conditions.

Without those controls, increased agent autonomy can introduce unnecessary security and operational risk.

What should enterprises look for in agentic AI platforms?

As organizations evaluate AI and machine learning platforms, several capabilities are becoming increasingly important.

• Agent tool discovery: Organizations need visibility into the tools available to their agents and an understanding of what those tools can do.
• AI security policies and guardrails: These establish boundaries around agent behavior, including when an agent can access a tool or take a particular action.
• Data security: Agentic workflows can involve sensitive enterprise information. Data security capabilities can help organizations protect information through controls such as PII redaction and masking.
• Hybrid human and machine workflows: Not every process should be fully autonomous. Platforms should support workflows in which humans and AI systems can work together.
• Agent registry governance: As organizations adopt agents from multiple vendors and platforms, maintaining a centralized view of those agents becomes increasingly important.
• Cross-platform, multi-agent AI: Enterprise agents cannot always be expected to operate within a single technology ecosystem. Cross-platform capabilities allow organizations to coordinate agents and workflows across different environments.
• Agent observability: Organizations also need to know what their agents are doing. Agent observability provides visibility into agent activity and behavior, helping teams understand how AI systems are operating across enterprise workflows.

Boomi's place in the conversation

Ray highlights Boomi as one of the vendors included in Constellation Research's Q3 2026 AI and Machine Learning Best of Breed Platforms ShortList.

The recognition reflects capabilities Ray identifies around agent governance, including tool discovery, security policies, guardrails, data security, hybrid workflows, agent registry governance, cross-platform multi-agent AI, and observability.

The larger point extends beyond any individual vendor.

As enterprises deploy more agents, governance needs to become part of the architecture rather than an afterthought.

The shift from building agents to governing agents

The enterprise AI conversation has moved quickly from whether organizations can build agents to what those agents can actually do. An agent that can reason and recommend is one thing. An agent that can access business systems, move data, and execute actions is something else entirely.

That is why governance must extend beyond model performance.

Enterprises need to understand:

• What agents exist?
• What systems can they access?
• What tools can they use?
• What data can they see?
• What actions can they take?
• When does a human need to intervene?
• How can teams observe and evaluate agent behavior?

The organizations that answer those questions will be better positioned to increase decision velocity without sacrificing control.

The future of enterprise AI isn't just about building smarter agents. It's about giving those agents the right context, access, boundaries, and oversight to act responsibly.

Explore the Q3 2026 Constellation ShortList™ for AI and Machine Learning Best of Breed Platforms.