Agentic AI Glossary.
Plain language for
business leaders.

When an AI model generates information that is factually incorrect or entirely fabricated, presented with apparent confidence. Hallucination is a known limitation of large language models. In enterprise settings, hallucination without detection controls represents a material risk, particularly in legal, compliance, financial, and clinical applications where accuracy is non-negotiable.

A sequence of automated steps carried out by one or more AI agents to complete a business process. AI workflows can span multiple systems, data sources, and decision points, operating without continuous human input at each step. Unlike simple automation, AI workflows can adapt to new information and reason through non-standard scenarios.

AI that operates independently to complete tasks without requiring human input at each step. Autonomous AI is capable of managing its own decision-making within defined boundaries. The degree of autonomy ranges from supervised systems that seek approval before acting, to fully autonomous systems that operate independently within established guardrails.

AI systems designed and built to meet the scale, security, compliance, and reliability demands of large organizations. Enterprise AI differs from consumer AI in that it must integrate with existing enterprise systems, operate within regulatory frameworks, produce auditable outputs, and perform consistently at the volume and reliability standards that enterprise operations require. Consumer AI tools built for individuals rarely satisfy these requirements without significant adaptation.

Three categories of financial and operational risk that cost organizations, and governments, significant resources each year. Fraud involves intentional deception for financial gain. Waste refers to the misuse or overuse of resources without resulting benefit. Abuse involves practices that are not necessarily illegal but deviate from accepted standards. Agentic AI can detect patterns across all three categories at a scale and speed that human audit teams cannot match.

A class of AI that can create new content, including text, images, code, and more, based on patterns learned from existing data. Generative AI underlies tools like ChatGPT and Copilot. While powerful for content creation and communication, generative AI is one component of a broader AI capability set. Agentic AI, by contrast, goes further: it uses generative AI capabilities as a reasoning layer while also taking actions, executing workflows, and operating continuously without human prompting.

See AI Guardrails. Boundaries embedded into an AI system to ensure it only acts within approved parameters. In CAMS, guardrails are enforced structurally, through the Agent Registry, which requires every agent to be documented and approved before deployment, and through the AI Agent Control Tower, which monitors behaviour in real time and provides the mechanism to stop any agent that acts outside its defined scope.

A type of AI model trained on vast amounts of text data that can understand, generate, and reason using natural language. LLMs are foundational components of many AI systems, including Agentic AI platforms. However, an LLM alone is not an AI agent. An LLM provides the language understanding and reasoning capability. The agentic layer, the ability to take action, use tools, and complete multi-step tasks is built on top of that foundation. CAMS integrates with leading LLMs and orchestrates them within governed enterprise workflows.

A discipline that ensures an organization has a single, consistent, and accurate version of its critical data, such as customer records, product information, supplier lists, and financial hierarchies. Without MDM, the same entity may appear with different names, IDs, or attributes across different systems, causing inconsistency and errors throughout any process that depends on that data, including AI-driven processes.

A network of AI agents, each with its own role and capabilities, that work together to achieve broader objectives. Multi-agent systems are the architecture underlying enterprise agentic AI deployments. Rather than one general-purpose agent doing everything, a multi-agent system deploys specialised agents that each do one thing well, and the orchestration layer coordinates them into a coherent, governed whole.

A way of interacting with a software system using everyday spoken or written language, rather than technical commands or structured queries. In CAMS, the AI Agent Control Tower uses a natural language interface so that business users and engineers can query agent performance, ask questions about their workflows, and receive clear answers without writing code or learning a new technical system.

A technique used in AI systems where the model retrieves relevant information from an external data source before generating a response. Rather than relying solely on what was baked into the model during training, RAG allows AI to reference live, specific, proprietary data in real time, producing outputs that are grounded in your actual enterprise information. In the context of agentic AI, RAG is a key mechanism for giving agents access to the current state of your organization's knowledge and data.

Data that is organized in a defined format, typically in tables, rows, and columns, as found in spreadsheets, databases, and ERP systems. Structured data is easier for traditional analytics tools to process. However, it represents only a fraction of the data most organizations generate. The rest is unstructured. CAMS's Data Intelligence layer is designed to work with both, ensuring AI agents have access to the full picture.

Data that does not follow a predefined format, such as emails, contracts, audio recordings, images, and documents. Unstructured data accounts for the majority of information generated by most organizations, yet traditional analytics tools struggle to process it. Agentic AI, through CAMS's Data Intelligence layer, can read, interpret, and act on unstructured data the same way a skilled analyst would, making the full breadth of your enterprise information available as a source of intelligence, not just the portion already formatted for a database.

The use of technology to carry out a sequence of business tasks with minimal human involvement. Agentic AI takes workflow automation significantly further, allowing systems to follow predefined steps, reason through decisions, adapt to new information, and complete complex processes end to end. Where traditional workflow automation breaks when it encounters an exception, an agentic AI workflow reasons about the exception and resolves it, escalating to a human only when genuine judgment is required.