There is a pattern that keeps repeating itself across enterprise AI projects. A company invests in a capable model, deploys it with reasonable expectations, and then watches it underperform in production. Not because the model is weak. Because the data feeding it is stale, incomplete, or contextually meaningless to the system trying to use it. Denodo’s new integrations with AWS are a direct response to that pattern, extending its data management platform into the infrastructure layer where agentic AI actually breaks down.
The timing is relevant across more than one industry. Healthcare organizations navigating the same AI scaling challenge are simultaneously contending with a connected device problem that sits right underneath it: IoMT and clinical IoT assets that carry sensitive data, run on unpatched firmware, and sit largely outside traditional security visibility. For CIOs, CISOs, and clinical engineering leaders working through vendor decisions in that space, there is a practical buyer’s guide available that covers how to evaluate IoMT security solutions properly, what questions to ask about agentless detection, and a reusable checklist for RFPs and internal reviews worth looking at before committing to a direction.
Where Agentic AI Actually Fails
The failure mode most enterprises are hitting right now is not what the model brochure prepared them for. AI agents fail in production for three reasons that keep coming up across financial services, healthcare, manufacturing, and retail: they lack real-time awareness of what is actually happening in the business, they work with data that is either wrong or missing critical context, and they operate without meaningful guardrails around compliance and governance boundaries.
None of those are model problems. There are data problems. And they are surprisingly hard to fix from the AI layer down because the issues live in the infrastructure underneath.
Denodo‘s argument is that a logical data foundation, one that sits across on-premises systems, SaaS platforms, and multi-cloud environments and delivers live, governed, business-contextual data to wherever agents need it, is what actually makes agentic AI reliable at scale. The new AWS integrations are built around making that foundation work with the tools enterprises are already using.
Three Integrations, Three Different Problems
Amazon Bedrock AgentCore: Keeping Agents Inside the Lines
The integration with Amazon Bedrock AgentCore addresses the access and control layer. Denodo defines what data is available across the enterprise, enriches it with business context through its semantic layer, and surfaces it via Model Context Protocol in line with established governance policies. Bedrock AgentCore handles the other side of that handshake, managing authentication, routing requests, and enforcing access controls as agents interact with data.
The practical result is that AI agents get consistent, real-time access to the right data without requiring anyone to manually adjudicate every request or loosen governance controls to make things work. For industries where compliance is non-negotiable, that combination matters more than it might in less regulated environments.
Amazon SageMaker: Consistent Meaning Across Disconnected Systems
The SageMaker integration tackles a different problem. Enterprises running data across AWS and non-AWS environments frequently end up with semantically inconsistent information, where the same data element means slightly different things depending on which system you pull it from. For an AI agent trying to make reliable decisions, that inconsistency is enough to produce wrong outputs even when the underlying data is technically accurate.
Denodo connects to Amazon SageMaker Catalog to add business metadata and context directly to the data AI agents consume. It also brings 200-plus native connections to enterprise systems, including SAP, Oracle, and Salesforce, with fine-grained governance controls covering attribute-based access, dynamic data masking, and end-to-end lineage capture for non-AWS sources. The goal is that data accessed anywhere in the environment carries the same semantic meaning, so agents interpret it correctly regardless of where it originated.
Quick: From Insight to Action Without the Wait
The Quick integration is narrower in scope but addresses a friction point that slows down a lot of analytics work. Moving from an insight to an action typically involves some degree of data movement, which introduces delays and creates opportunities for information to go stale in transit.
By combining Quick with Denodo’s live, zero-copy data access, business users can work with current information across distributed environments without waiting for pipelines to complete. AI-driven workflows and conversational interfaces built on this integration are working with trusted, real-time data rather than a snapshot from yesterday’s sync.
What Suresh Chandrasekaran Said and Why the Framing Matters
Suresh Chandrasekaran, Executive Vice President at Denodo, put the core argument plainly: “Agentic AI requires more than powerful models. It requires trusted, real-time, and well-governed data. Our collaboration with AWS focuses on delivering a unified data foundation that enables organizations to scale AI agents with confidence across the entire data landscape.”
The framing is worth noting because it positions the data layer as the enabling condition rather than a supporting component. That is a different conversation than the one most enterprise AI vendors are having, which tends to start with model capability and treat data access as a solvable downstream problem. Denodo’s position is that you cannot retrofit a trustworthy data foundation onto an agentic system that was built without one.
AWS Marketplace and Getting Started
Denodo is available through AWS Marketplace, with free trials, private offers, and flexible options available depending on what fits a given organization’s procurement structure. Purchases can be applied toward AWS Private Pricing Agreements, which simplifies the commercial side for organizations already running significant AWS spend.
For enterprises that have been circling enterprise AI deployment without pulling the trigger, the combination of a logical data layer, live governed access across hybrid environments, and native integration with SageMaker, Bedrock AgentCore, and Quick removes several of the technical objections that have kept production deployments from getting off the ground.
A Note for Healthcare and Clinical Engineering Teams
If your organization sits at the intersection of AI scaling and connected medical device security, the data governance conversation Denodo is having maps directly onto problems that clinical and HTM teams are navigating on the device side. Before committing to a vendor direction on IoMT security, the buyer’s guide mentioned earlier covers device visibility across clinical, IoT, and OT assets, risk scoring that actually reduces time-to-remediate, and the right questions to ask about network-based detection versus endpoint agents for devices that cannot run traditional security software. It includes a vendor evaluation checklist built for RFPs and internal reviews.
Research and Intelligence Sources: denodo, AWS Bedrock, AWS SageMaker, NIST AI RMF, MITRE ATLAS
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