Building AI Agents That Build Trust

Dominic Sartorio
Denodo

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption.

What can organizations do to build trustworthy AI agents?

Arlington Research explored the answer to this question by surveying 850 executives and business decision-makers who were responsible for sponsoring, designing, and/or running AI initiatives in organizations with over 1,000 employees.

When it comes to agentic AI, trust has many dimensions, but this research uncovered a powerful overarching theme: The most critical trust gaps relate to data, and these are gaps that can be overcome by adding the right capabilities to any existing data infrastructure; no rip-and-replace required.

In this post, I'll focus on three:

1. The data freshness gap. AI agents need access to live data across myriad data sources. 

2. The data relevance gap. AI agents need access to the right data for the given context, from authoritative sources.

3. The guardrail gap. AI agents need guardrails that effectively limit what they can "see" and do, continually enforced.  

The Data Freshness Gap

AI agents are being enlisted for demanding operational use cases such as dynamically re-routing shipments, mitigating fraud in real time, and balancing the load on smart grids in response to changing conditions. They need access to live data because yesterday's report is already out-of-synch with the current moment, and that's the moment in which agents need to operate.

The research confirms this, reporting that 66% of organizations say that AI data must be real-time or near real-time to be trustworthy. Unfortunately, many organizations are set up for analytics, not operational decision-making; their data management infrastructures are just not built to support the live data requirements of today's agentic AI use cases.

The Data Relevance Gap

More dramatically, AI agents are not trusted when they leverage the wrong data to support a given task. The research indicates that the average company draws data from over 400 sources for AI initiatives, and almost 20% draw from over a thousand. Organizations need to prepare their AI agents so that they can effectively choose the right one for each individual context. However, the research found that 63% of organizations struggle to identify and prepare trustworthy data for AI. This requires a properly configured semantic layer between the AI and the sources, but this hasn't been easy for many organizations to establish.

The Guardrail Gap

Finally, AI agents need to act within clear boundaries that define what data they can see and what actions they are allowed to perform. If organizations can ensure that their AI agents can be trusted to adhere to such boundaries, they can enjoy high adoption rates. Unfortunately, the research shows that 67% of the surveyed organizations struggle with AI data security and access controls, representing a wide trust gap.

It is relatively straightforward to enable strong data governance policies for a single data source, but the complexity rises considerably when the number of data sources increases, and the complexity compounds when the sources are heterogeneous and span on-premises and cloud systems.

Closing the Gaps

Fortunately, each of these trust gaps can be decisively closed by leveraging a logical data management layer alongside any existing data infrastructure. In contrast with traditional data management platforms, logical layers enable:

1. Live access to disparate data, without requiring replication

2. Universal semantics, providing a shared understanding of data across systems

3. Strong data governance and security protocols across diverse sources enforced at the point of access, for reliable agentic AI guardrails 

Dominic Sartorio is VP of Product Marketing at Denodo

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Building AI Agents That Build Trust

Dominic Sartorio
Denodo

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption.

What can organizations do to build trustworthy AI agents?

Arlington Research explored the answer to this question by surveying 850 executives and business decision-makers who were responsible for sponsoring, designing, and/or running AI initiatives in organizations with over 1,000 employees.

When it comes to agentic AI, trust has many dimensions, but this research uncovered a powerful overarching theme: The most critical trust gaps relate to data, and these are gaps that can be overcome by adding the right capabilities to any existing data infrastructure; no rip-and-replace required.

In this post, I'll focus on three:

1. The data freshness gap. AI agents need access to live data across myriad data sources. 

2. The data relevance gap. AI agents need access to the right data for the given context, from authoritative sources.

3. The guardrail gap. AI agents need guardrails that effectively limit what they can "see" and do, continually enforced.  

The Data Freshness Gap

AI agents are being enlisted for demanding operational use cases such as dynamically re-routing shipments, mitigating fraud in real time, and balancing the load on smart grids in response to changing conditions. They need access to live data because yesterday's report is already out-of-synch with the current moment, and that's the moment in which agents need to operate.

The research confirms this, reporting that 66% of organizations say that AI data must be real-time or near real-time to be trustworthy. Unfortunately, many organizations are set up for analytics, not operational decision-making; their data management infrastructures are just not built to support the live data requirements of today's agentic AI use cases.

The Data Relevance Gap

More dramatically, AI agents are not trusted when they leverage the wrong data to support a given task. The research indicates that the average company draws data from over 400 sources for AI initiatives, and almost 20% draw from over a thousand. Organizations need to prepare their AI agents so that they can effectively choose the right one for each individual context. However, the research found that 63% of organizations struggle to identify and prepare trustworthy data for AI. This requires a properly configured semantic layer between the AI and the sources, but this hasn't been easy for many organizations to establish.

The Guardrail Gap

Finally, AI agents need to act within clear boundaries that define what data they can see and what actions they are allowed to perform. If organizations can ensure that their AI agents can be trusted to adhere to such boundaries, they can enjoy high adoption rates. Unfortunately, the research shows that 67% of the surveyed organizations struggle with AI data security and access controls, representing a wide trust gap.

It is relatively straightforward to enable strong data governance policies for a single data source, but the complexity rises considerably when the number of data sources increases, and the complexity compounds when the sources are heterogeneous and span on-premises and cloud systems.

Closing the Gaps

Fortunately, each of these trust gaps can be decisively closed by leveraging a logical data management layer alongside any existing data infrastructure. In contrast with traditional data management platforms, logical layers enable:

1. Live access to disparate data, without requiring replication

2. Universal semantics, providing a shared understanding of data across systems

3. Strong data governance and security protocols across diverse sources enforced at the point of access, for reliable agentic AI guardrails 

Dominic Sartorio is VP of Product Marketing at Denodo

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...