As AI capabilities evolve, the concepts of non-human identity (NHI) and AI agent are showing up increasingly in our daily work, especially in engineering, product, and system design contexts. You will see them in architecture diagrams, GitHub discussions, dev standups, or even baked into feature specs. An LLM-powered service might be labeled an “agent,” while a persistent user-facing system gets assigned an “identity.” The problem? These terms are being used interchangeably when they describe fundamentally different things.
AI agents are AI/LLM-driven software systems. They can be powerful and autonomous. While they are tools, they are often not treated as identities, but they should be. They require security, oversight, and accountability, but not the projection of intent. NHIs, on the other hand, are machine or workload identities assigned to systems that persist, adapt, and present themselves in ways that resemble their identity. Treating them as agents risks ignoring their growing complexity and presence.
In this blog, we will clarify what these terms really mean, how they differ, and why the distinction matters, especially for teams building and integrating intelligent systems.
What is an AI agent?
AI agents are systems powered by large language models (LLMs) that make decisions, manage tasks, and adapt dynamically to real-time inputs. These systems go beyond passive tools; they can adapt autonomously and in real time.
AI agents don’t just respond; they take action. Unlike traditional systems that wait for user input, AI agents can initiate workflows, orchestrate across APIs, update databases, manage schedules, and even control physical devices. They are increasingly embedded into everything from developer tools and customer support systems to smart home platforms and product backends. For example, an AI agent might automatically escalate a support ticket based on sentiment analysis, trigger a deployment pipeline after code review, or adjust IoT device settings based on real-time sensor data.
Most AI agents typically share these three core traits:
- Autonomy – they can operate without continuous human input
- Goal-directed behavior – they pursue defined objectives or tasks
- Environmental awareness – they process inputs and adjust behavior based on changing context
These agents are powerful and can appear intelligent, but they are still software systems that are designed artifacts. They do not have identity, intent, or continuity of self. They may behave in ways that feel human-like, especially when interacting through natural language, but they remain fundamentally task-driven with programs built to serve specific functions. Understanding this distinction is critical.
What is a non-human identity (NHI)?
Non-human identities (NHIs) are machine or workload identities used by software and systems to access resources. They are assigned to entities like APIs, service accounts, containers, workloads, and IoT devices. The purpose of NHIs is to allow automated systems and services to securely interact with other components in a distributed environment without human intervention. They enable tasks like data transfer, API calls, code deployment, workload orchestration, and service-to-service communication. NHIs are foundational to daily work operations, enabling infrastructure to scale, adapt, and function autonomously across dynamic, multi-cloud environments.
For example, an NHI might allow a CI/CD pipeline to push code to production, a Kubernetes pod to pull secrets from a vault, or an IoT sensor to report health metrics to a cloud dashboard.
At a high level, NHIs typically share three core characteristics:
- Automation-first – they are designed to operate without manual intervention
- System-integrated – they are tightly embedded into apps, infrastructure, and platforms
- High-volume and short-lived – they are often created and destroyed programmatically at high speed and scale
Despite their importance, NHIs are often overlooked in traditional identity systems, which were built for managing human users. They usually lack proper visibility, governance, or controls, making them a growing security risk. NHIs do not have intent or awareness; they are not intelligent. But they do hold privileges and access that, if compromised, can lead to major security risks. As NHIs continue to evolve, it is essential to understand their role and secure them effectively to maintain security and operational resilience.
Key differences between non-human identities and AI agents
AI agents are not NHIs, and they should not be treated as such. Grouping AI agents under the umbrella of NHIs is not only inaccurate; it can also create security risks.
NHIs like service accounts and tokens are predictable by design. They are static tools built to execute specific, predefined functions. Their behavior doesn’t change, and they never act without instruction. Because of this predictability, they can be modeled, monitored, and managed within traditional identity frameworks.
AI agents are fundamentally different. They are autonomous. They interpret intent, reason independently, and make decisions that evolve in real-time. Their actions are not scripted; they are autonomous. Unlike NHIs, AI agents can surprise you, and that’s not a side-effect—it’s a feature.
Treating AI agents as just another type of machine identity ignores this profound shift. It risks applying the wrong controls, overlooking new risk vectors, and ultimately undermining trust in systems designed to be intelligent. We need to stop forcing these entities into outdated identity approaches.
AI agents are a new identity type. They demand a new approach for lifecycle governance, behavioral monitoring, and real-time intervention. Recognizing this isn’t just a matter of knowledge, it’s a matter of security.
Here is a breakdown of the key differences:
| NHIs | AI Agents | |
| What they are | Digital credentials for systems or services | Task-driven intelligent systems powered by AI |
| Primary purpose | Enable machines or workloads to authenticate and access resources | Make decisions, act on data, and perform workflows |
| Security focus | Credential management, access controls, lifecycle | Behavior monitoring, permissions, and context limits |
| Identity lifecycle | Configured like a user account: created, rotated, expired | Not a standalone identity; built on top of NHIs |
| Risks | Exposed API keys, unused service accounts | Autonomous overreach, overprivileged, prompt injection, and misuse |
| Governance needs | Least privilege, credential hygiene, and rotation | Guardrails, explainability, and intent restriction |
| Identity security alignment | Enforce authentication, authorization, and visibility | Enforce action scope, verification, and observability |
Why knowing the difference matters
Understanding the difference between NHIs and AI agents is critical for securing your environment and users. If you treat both the same, you risk securing one layer while leaving the other wide open. You might lock down credentials but fail to monitor what the agent is doing with them. Or you might constrain AI behavior, but overlook that it is using a long-lived, over-permissioned NHI.
These are two distinct threat surfaces, and if you do not address them as distinct entities in your identity security strategy, you are leaving critical vulnerabilities and security risks unchecked. Now is the time to clearly understand the differences between NHIs and AI agents, because only with that visibility can you apply the right controls, close the right gaps, and stay ahead of the risks that are already here.