AI Systems And Agents

Expanded Definition

AI systems and agents are not a single product category but an operational surface: models, orchestration layers, applications, and autonomous or semi-autonomous agents that can affect business decisions, execute actions, or request access. For NHI Management Group, the important distinction is between a model that generates output and an agent that can take action with tool access, which creates different governance and security obligations.

Usage in the industry is still evolving. Some teams use “AI system” narrowly to mean a deployed model, while others include surrounding workflows, prompts, retrieval layers, and integrations. “Agent” usually implies persistence, delegated authority, and the ability to act across systems, but definitions vary across vendors and implementation patterns. A useful reference point is the NIST AI Risk Management Framework, which frames AI governance around mapping, measuring, and managing risk across the full lifecycle.

The most common misapplication is treating an agent like a passive chatbot, which occurs when teams overlook tool permissions, memory, and external actions that can change records or trigger downstream workflows.

Examples and Use Cases

Implementing AI systems and agents rigorously often introduces discovery and governance overhead, requiring organisations to weigh automation speed against control, auditability, and access risk.

• An internal support agent that can create tickets, update records, and query knowledge bases, requiring approval boundaries and logging.
• A procurement assistant that drafts purchase requests from user prompts, where the model output is harmless but the connected workflow can create real commitments.
• A security operations agent that triages alerts and enriches incidents, where delegation must be limited to read-only or tightly scoped write actions.
• A customer-facing assistant that uses retrieval and tool calls, where prompt injection and data exposure risks must be assessed as part of the system, not just the model.
• An autonomous code agent that opens pull requests or changes infrastructure configurations, where change control and identity governance become inseparable from AI governance. The OWASP Top 10 for Agentic Applications 2026 is useful here because it highlights the risks that emerge once tool use and autonomy enter the workflow.

These examples show why AI systems and agents must be assessed as an ecosystem, not as isolated prompts or model endpoints. The same model can be low risk in one deployment and high risk in another if it gains access to secrets, APIs, or privileged workflows.

Why It Matters for Security Teams

Security teams need this term because many of the hardest failures come from misplaced trust in AI behaviour, weak scoping of authority, and poor inventory of where AI is actually embedded. Once an agent can touch data, call tools, or influence access, it becomes part of the control environment and should be governed accordingly.

That governance intersects directly with identity and NHI concerns: agents often rely on service accounts, API keys, tokens, and delegated credentials, which means access review, secret handling, and least privilege all apply. It also intersects with adversarial AI threat models, especially where prompt injection, tool abuse, or automated social engineering can turn a useful assistant into an attack path. Resources such as the OWASP Agentic AI Top 10, MITRE ATLAS adversarial AI threat matrix, and CSA MAESTRO agentic AI threat modeling framework help security leaders translate that risk into practical controls.

Organisations typically encounter the real impact only after an agent misuses a tool, overreaches its permissions, or produces an unintended action, at which point AI systems and agents become operationally unavoidable to govern.

Related resources from NHI Mgmt Group

• How should security teams limit the risk from AI agents that have access to production systems?
• How should security teams govern AI agents that can access enterprise systems?
• How should security teams govern ecommerce AI agents that can touch payment systems?
• What breaks when AI agents are given broad access to healthcare systems?