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Glossary / Agentic AI
Agentic AI
What is agentic AI?
In modern enterprises, AI is shifting from a passive tool to an autonomous actor. Instead of waiting for instructions, agentic AI systems can decide what to do next, coordinate multiple steps, and interact with other systems on their own.
These agents might research information, process transactions, update records, or even call other AI services, all without a human in the loop.
Like non-human identities, agentic AI operates in the background with credentials and permissions that can rival or exceed those of human accounts. If left unmanaged, autonomous agents can create both incredible efficiencies and serious security risks.
This guide explains what agentic AI is, why it matters, and how to keep it secure in 2025.
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Defining agentic AI
Agentic AI describes a type of artificial intelligence that doesn’t have to wait for instructions and is built to act on its own. Instead of answering a single prompt and stopping there, these systems can figure out what needs to be done, break the work into smaller pieces, and choose how to tackle each step.
This autonomy is made possible by integrating large language models (LLMs) or other reasoning engines with tools, APIs, and workflows.
The result is a system that can:
- Plan multi-step actions toward a goal.
- Choose which resources or services to call.
- Adapt to new information in real time.
Agentic AI is already showing up in areas like customer service automation, security operations, and software development pipelines — and, increasingly, in enterprise identity ecosystems
Learn more and get the identity playbook for agentic AI.
Why agentic AI matters
Autonomy comes with good news and bad news: speed and scale for good actors and speed and scale for bad ones.
An agent that can decide its own actions can solve problems in minutes or even seconds that might take humans hours or longer. But if an attacker compromises that agent’s identity, they gain the same autonomous power, so they can scan systems, extract data, or disrupt operations, without waiting for instructions.
The risks are real. As AI agents gain system-level access, they inherit all the identity, authentication, and authorization concerns of any non-human identity. Without protections, they can take actions that violate policy, leak sensitive data, or cause compliance failures.
That’s why agent identity orchestration is becoming so critical.
Key components of agentic AI security
Securing agentic AI is about controlling autonomy without losing its advantages. Enterprises need a framework that defines identity, enforces boundaries, and ensures accountability for every action an agent takes.
- Identity assignment: Every agent gets a unique, verifiable identity so its actions can be traced.
- Authentication: Use secure, modern methods like OAuth with PKCE or mutual TLS to prove that the agent is who it claims to be.
- Authorization boundaries: Explicitly limit the systems, APIs, and data each agent can access.
- Activity monitoring: Track and log every action in real time to catch misuse early.
- Lifecycle control: Create, rotate, and retire agent credentials automatically as their roles change or end.
Best practices for managing agentic AI
Agentic AI is powerful, but like any tool with sharp edges, it needs to be handled carefully. Best practices differentiate between secure automation and a wide-open door for attackers.
- Apply least privilege. Limit each agent’s access to only what’s needed for its current tasks.
- Use strong authentication . Avoid static secrets; use short-lived tokens or one-time proofs.
- Monitor continuously . Flag and investigate unusual patterns of behavior.
- Automate revocation . Be ready to cut off an agent’s access instantly if compromised.
- Integrate with IAM policies . Treat agentic AI as a first-class identity in your access strategy.
Agentic AI vs. traditional AI
| Feature | Traditional AI | Agentic AI |
|---|---|---|
| Initiative | Responds only to user prompts | Can set and pursue its own goals |
| Workflow | Single-step interactions | Multi-step planning and execution |
| Adaptability | Limited to input context | Adjusts actions based on new data |
| Integration | Standalone or minimal tool use | Directly uses APIs, tools, and services |
| Identity needs | Often runs under a shared service account | Requires unique, managed identity with full lifecycle controls |
| Security risk | Misuse limited to single queries | Potential for wide-reaching impact if compromised |
Agentic AI is changing how work gets done and how identity security needs to operate. Organizations can harness their autonomy without losing control by applying the same rigor to agents as they would to human and other non-human identities.