The intelligence layer for AI agents
The AI Data Engine gives AI agents the data, context, and reasoning they need to understand their environment, make informed decisions, and act within defined boundaries.
Intelligence for AI agents operating in the real world
AI agents are moving into complex business environments and operational systems, where they work across enterprise data, applications, people, and business processes.
The AI Data Engine provides a governed, connected foundation for agents to work with enterprise data, relationships, context, reasoning, business rules, models, and workflows. This enables agents to interpret their environment, work across systems, and operate within defined business requirements.
Visibility
See the context behind every agent decision
AI agents operate across data, models, tools, workflows, and business processes. Understanding their behavior requires visibility into the information and reasoning that influence each decision.
The AI Data Engine connects agent activity to the data, relationships, and reasoning behind it. Decisions can be traced from the information available to an agent through the reasoning that shaped its response and into the resulting action.
This makes it possible to evaluate outcomes, identify patterns, investigate unexpected behavior, and improve agent performance.
- 01Available informationData & relationships
- 02Decision contextRelevant information & conditions
- 03Resulting actionAgent activity & outcome
Collaboration
Connect agents, people, and systems through shared context
AI agents work alongside people, applications, and other agents across business processes. Effective collaboration depends on shared access to the information, context, and processes involved.
The AI Data Engine provides a common foundation for enterprise data, applications, agents, models, and workflows. Information and intelligence can be accessed across the systems where work happens.
Agents can participate in existing business processes while people and applications access the same underlying resources. APIs extend these capabilities into operational systems, allowing agents to work within existing workflows rather than operating in isolated AI environments.
Control
Give autonomous agents clear context and boundaries
As agents move from understanding and collaboration toward autonomous action, they need clear boundaries for what they can access and do.
The AI Data Engine applies permissions, policies, constraints, and business rules across the data and reasoning process. You can define which information is available to an agent, which conditions influence its decisions, and where it is permitted to act.
These controls provide a consistent framework for managing access and actions as agents operate across applications, workflows, and environments.
Action
Bring contextual intelligence closer to where decisions are made
With the right context and boundaries in place, AI agents can move from understanding and decision-making to action.
AI agents are moving into devices, machines, vehicles, industrial environments, and other operational systems where decisions may need to happen close to the point of action.
Q-Sensei extends the AI Data Engine into these environments, giving agents access to relevant operational data and enabling decisions closer to where actions take place.
Deployment can be configured around the requirements of each environment, including latency, connectivity, privacy, data residency, and operational autonomy.
Centralized and distributed agents can use Q-Sensei models, third-party models, or models developed by internal teams.
Build AI that understands before it acts
Give your agents the data, context, reasoning, and control they need to move from generating responses to taking informed action.
- Context
- Reasoning
- Action