What Are AI Agents?

AI agents are autonomous software entities that reason, plan, and take actions to accomplish goals, operating across systems with varying degrees of independence. Unlike static automation or simple chatbots, these agents can interpret instructions, use external tools, and coordinate multi-step workflows without continuous human direction. As organizations deploy AI agents for IT operations, security, certificate management, and infrastructure automation, adoption is accelerating, but so are the governance and identity challenges they introduce. Every agent that accesses organizational systems needs to be identifiable, attributable, and governed. This article explains what AI agents are, how they work, how they differ from traditional automation, and why machine identity is central to deploying them securely at scale.

What Are AI Agents?

An AI agent is a goal-driven software entity that iteratively interacts with large language models (LLMs), external tools, and other systems to accomplish a defined objective. Rather than producing a single output in response to a single prompt, an AI agent interprets a task, determines which resources it needs, and coordinates operations across multiple steps and systems.

As Ellen Boehm, SVP of IoT Strategy at Keyfactor, describes it in the company’s Digital Trust Digest research: “AI agents initiate action. They launch cloud infrastructure, review and modify code, triage security incidents, negotiate APIs, and chain together complex reasoning steps.”

This distinction matters. A traditional LLM interaction is a single exchange, that is, it’s only a question and an answer. An AI agent, by contrast, can interpret a task, decide which systems it needs to interact with, and coordinate the entire operation end-to-end. It behaves less like a script and more like a colleague with specialized expertise and access to the right tools.

AI agents are capable of actions, not just outputs. They can read data, write configurations, call APIs, interact with databases, and trigger downstream processes – all within the scope of a single assigned goal.

Core Characteristics of AI Agents

Several characteristics distinguish AI agents from simpler forms of software automation and basic LLM interactions.

• Iterative Behavior
  AI agents do not complete their work in a single pass. They engage in cycles of reasoning, action, and evaluation. After each step, an agent assesses whether the result moves it closer to its goal and decides what to do next. This iterative loop enables agents to handle tasks that cannot be fully specified in advance.
• Tool and System Access
  AI agents interact with external systems through structured interfaces. A growing standard for this is the Model Context Protocol (MCP), which provides agents with access to external tools and APIs. As JD Kilgallin, Technical Marketing Manager at Keyfactor, explains in the company’s research on securing agentic AI: “MCP is the ‘eyes’ and ‘hands’ of an AI, exposing existing external systems so it can take action to accomplish a goal within its environment.”

MCP servers expose API functions with human-readable documentation that agents can discover and use dynamically. This means agents are not limited to pre-programmed integrations, rather, they can learn to use new tools as they encounter them.

• Goal-Oriented Execution
  Every AI agent operates with a defined objective. Whether the goal is narrow (enroll a certificate) or broad (identify all HTTPS servers on a network and remediate expiring certificates), the agent organizes its actions around achieving that outcome.
• Variable Autonomy
  AI agents can operate with or without direct human involvement. Some require human approval at key decision points. Others execute independently within defined constraints. The degree of autonomy is a design choice, not a fixed property of the technology.

How AI Agents Work

At a high level, AI agents follow a repeating lifecycle of four phases.

• Perception.
  The agent receives a task or detects a condition that triggers action. This could be a natural-language instruction from a user, a scheduled event, or a signal from a monitored system.
• Reasoning.
  The agent interacts with an LLM to interpret the task, break it into subtasks, and determine which tools or systems it needs. This phase may involve consulting knowledge bases, evaluating options, or generating plans.
• Action.
  The agent executes its plan by calling APIs, accessing MCP servers, querying databases, or performing other operations. Each action produces results that the agent can evaluate.
• Feedback .
  The agent assesses the outcome of its actions against its goal. If the task is complete, the agent returns results. If not, it cycles back to reasoning with updated context and continues.

A typical workflow looks like this:

1. A task is defined in natural language;
2. the agent interacts with an LLM to plan its approach;
3. the agent accesses tools via APIs or MCP servers;
4. the agent executes the required operations;
5. finally, the agent evaluates the results, and either returns its output or continues iterating until the goal is met.

Core Capabilities of AI Agents

The following capabilities define what AI agents can do within their operating environment.

Autonomy within constraints. 
Agents make decisions and take actions independently, but within boundaries set by their operators. These boundaries may include which systems an agent can access, what types of changes it can make, and whether it requires human approval for high-risk actions.

Task execution. 
Agents carry out multi-step tasks that span multiple systems, translating a single high-level objective into a coordinated sequence of operations.

Tool usage. 
Agents discover, interpret, and use external tools (including APIs, databases, and MCP servers) to accomplish their goals. This tool usage is dynamic, not hard-coded.

Context awareness. 
Agents maintain awareness of their operating context, including the current state of systems they interact with, the progress of their task, and any constraints or conditions that affect their decisions.

Persistence (optional). 
Some agents retain memory across sessions, allowing them to build on previous interactions, track ongoing processes, or learn from past actions. Others are designed to be stateless.

Types of AI Agents

AI agents can be classified along two primary axes: the degree of human involvement and the type of work they perform.

By Human Involvement

Attended agents operate with a human in the loop. They may request approval before taking high-impact actions, present options for human selection, or pause at defined checkpoints. Attended agents are common in environments where oversight is required or where the consequences of incorrect actions are significant.

Autonomous agents operate independently once deployed. They receive a goal, execute their plan, and return results without requiring human intervention during execution. Autonomous agents are appropriate for well-defined tasks in environments with strong guardrails.

By Function

Task-specific agents handle a single, well-defined job, such as enrolling a certificate, generating a report, or processing a support ticket.

Workflow orchestration agents coordinate multi-step processes that span systems. They break complex objectives into subtasks, delegate to other tools or agents, and manage the overall flow of work.

Monitoring and analysis agents observe systems over time, detect anomalies, and trigger responses. They are common in IT operations, security monitoring, and compliance.

Collaborative or multi-agent systems involve multiple agents working together, often with different specializations. One agent might handle data retrieval while another performs analysis and a third generates reports. MCP servers can also be composed in this way. A single natural-language instruction might invoke one MCP server to discover resources and another to act on them.

AI Agents vs. Traditional Automation

AI agents represent a different approach to automation than scripts, robotic process automation (RPA), and rule-based systems. The differences are fundamental, not incremental.

Traditional automation is often reactive, deterministic, and bounded. A script executes a fixed sequence of steps; an RPA bot follows a recorded workflow. A rule-based system applies predefined logic to predefined inputs. These tools are reliable within their designed scope but cannot adapt when conditions change or when the task requires judgment.

AI agents are adaptive, context-sensitive, and capable of reasoning. They interpret instructions rather than following them literally. They make decisions based on current conditions rather than historical rules. They can handle tasks that were not fully anticipated at design time.

Characteristic	Traditional Automation	AI Agents
Decision-making	Follows predefined rules	Reasons about context and adapts
Scope	Single system or workflow	Cross-system, multi-step operations
Handling of exceptions	Fails or escalates	Attempts to resolve and continue
Input format	Structured, predefined	Natural language or unstructured
Adaptability	Requires reprogramming	Adjusts approach dynamically
Human dependency	Needs human design for every path	Operates within constraints, handles novel situations

As Keyfactor research frames it: “Automated systems follow instructions. Agentic systems interpret them.” Automated systems wait for human signals. Agents make their own decisions within the boundaries they have been given.

AI Agents vs. Agentic AI

These terms are related but distinct. An AI agent is an individual software entity that perceives, reasons, and acts to accomplish a goal. Agentic AI, on the other hand, is the system-level approach to designing, deploying, and governing groups of AI agents that work together across an organization.

The relationship is architectural: agents are the components, and agentic AI is the framework. A single AI agent enrolling a certificate is an agent. A platform that orchestrates multiple agents across certificate lifecycle management, monitoring, and compliance is an example of agentic AI.

For a deeper exploration of agentic AI as a system-level concept, see Keyfactor’s coverage of what agentic AI is and why it matters.

Agent Lifespan Models

Not all AI agents are designed to run indefinitely. How long an agent exists — and what it retains between tasks — is a critical design decision.

• Ephemeral Agents
  Ephemeral agents are short-lived. They are created on demand to handle a specific task, and they cease to exist once that task is complete. They are, therefore, on-demand productivity commoditized at the task level: press the button, the task completes, and the agent stops existing.

This model has distinct advantages. Each agent starts from a known baseline, with no accumulated state that could introduce errors or drift. Organizations can launch unlimited ephemeral agents on demand, scaling intelligence to the task rather than maintaining permanent infrastructure. Containerized agents exemplify this approach. They scope intelligence to a task, execute, and terminate.

• Persistent Agents
  Persistent agents are long-running. They maintain state, build context over time, and handle ongoing responsibilities such as continuous monitoring, recurring compliance checks, or long-term process management.

Persistent agents are appropriate when the value of accumulated context outweighs the risks of state accumulation. A monitoring agent that understands normal network behavior over weeks is more effective than one that starts fresh each time.

When evaluating agent architecture, organizations should classify agents by their access privileges, decision authority, risk exposure, and lifespan. In other words, they should distinguishing persistent services from ephemeral workers.

Common Use Cases for AI Agents

AI agents are being deployed across enterprise functions wherever multi-step, cross-system tasks create operational friction.

IT operations. 
Agents monitor infrastructure, detect anomalies, triage incidents, and execute remediation steps, reducing mean time to resolution and freeing operations teams for higher-level work.

Customer support. 
Agents handle initial triage, gather context from multiple systems (CRM, ticketing, knowledge base), and either resolve issues directly or route them with full context to human agents.

Data analysis and reporting. 
Agents query data sources, perform analysis, generate visualizations, and compile reports, turning what was a multi-hour manual process into a single instruction.

Business process automation. 
Agents orchestrate workflows that span procurement, finance, HR, and compliance systems, handling approvals, data validation, and cross-system coordination.

Certificate and credential management. 
Agents can enroll certificates, monitor expiration dates, trigger renewals, and coordinate across discovery and enrollment systems. These are tasks that previously required manual effort or fragile scripted integrations.

Why AI Agents Are Gaining Adoption

Several business drivers are accelerating the adoption of AI agents across enterprises.

Efficiency at scale. 
Organizations face growing volumes of operational tasks that exceed what human teams can handle manually. AI agents extend operational capacity without proportional headcount increases.

Speed of execution. 
Agents operate continuously and execute tasks in seconds or minutes that would take humans hours. In time-sensitive operations (e.g. in an incident response, compliance remediation, or certificate renewal) speed is a material advantage.

Reduction of manual, repetitive work. 
Tasks that are routine but cross-system (e.g. gathering data from five sources to compile a report, checking configurations across hundreds of servers) are current candidates for agent automation.

Consistency and reliability. 
Agents produce more consistent outcomes. They do not skip steps, forget context between tasks, or apply rules inconsistently across different instances of the same process.

Natural-language accessibility. 
The ability to instruct agents in natural language lowers the barrier to automation. Teams that previously depended on engineering resources to build integrations can now express their needs directly.

Challenges Introduced by AI Agents

As AI agents become more capable and more widely deployed, they introduce new operational considerations that organizations must address.

Oversight and accountability. 
When an agent takes an action (e.g. when it modifies a configuration, approves a request, or transmits data) the organization needs to know which agent acted, what authority it had, and whether its action was appropriate. Maintaining clear oversight becomes more difficult as the number of agents grows.

Predictability. 
Because AI agents reason about their tasks rather than following fixed scripts, their behavior is not fully deterministic. Two agents given the same task in slightly different contexts may take different approaches. This flexibility is a strength, but it also means that organizations cannot predict every action an agent will take.

Coordination at scale. 
As organizations deploy dozens or hundreds of agents, coordination becomes a challenge. Agents may have overlapping responsibilities, conflicting objectives, or dependencies on shared resources. Managing agent-to-agent interactions (especially in multi-agent systems where agents can create other agents) requires governance frameworks that many organizations have not yet built.

According to Keyfactor’s 2025 Digital Trust Digest survey, 50% of organizations have not fully implemented governance frameworks for AI agents, and 69% believe AI-based vulnerabilities will pose a greater threat than human misuse. These numbers reflect a technology that is advancing faster than the governance structures designed to manage it.

Why AI Agents Matter

AI agents matter because they change the interface between people and complex systems. Instead of navigating multiple tools, dashboards, and workflows manually, a user can express an objective in natural language and let an agent coordinate the underlying operations.

Consider certificate management as an example. A single natural-language instruction (e,g, “Enroll a certificate with this subject”) triggers an agent to interact with a certificate authority, configure the certificate, and return the result. A more complex instruction (e.g. “Identify all HTTPS servers in my network and reenroll any certificates due for renewal”) requires the agent to compose multiple systems: one for discovery, another for enrollment, coordinating across both in a single workflow.

This is the shift that AI agents enable: from multi-system workflows managed by specialists to single-interface operations managed by agents. The complexity does not disappear, it is absorbed by the agent, which handles the coordination, error handling, and cross-system communication that previously required manual effort.

For organizations managing thousands of certificates, credentials, and machine identities across hybrid environments, this shift has direct operational impact. Tasks that previously required dedicated teams and custom scripting become accessible through natural-language interaction with capable, tool-equipped agents.

How AI Agents Connect to Identity and Trust

AI agents act on behalf of organizations. They access systems, modify data, and make decisions that have real operational consequences. Every action an agent takes needs to be attributable, Organizations must know which agent did what, when, and with what authority.

This is fundamentally a question of identity. When systems begin to act independently, risk becomes a question of how they behave — and managing that behavior requires governance rooted in verifiable identity.

As long as an agent is continuously sensing, judging, and acting, it should have its own unique identity, not a shared credential or a static token, but a distinct, verifiable identity that reflects its current authorization and scope.

According to Keyfactor’s 2025 Digital Trust Digest survey, 86% of organizations agree that AI agents cannot be trusted without unique, dynamic digital identities. Yet only 28% report confidence in their ability to prevent a rogue AI agent from causing harm before it acts.

The gap between recognition and readiness is significant. As agents multiply – ephemeral and persistent, attended and autonomous, operating across internal and external systems – the need for scalable, automated identity management becomes foundational. The topics of AI agent security, AI agent identity, and agent governance build directly on the concepts covered here.

How To Prepare for AI Agents in Your Organization

Deploying AI agents effectively requires more than selecting the right technology. Organizations that move early to establish governance, identity infrastructure, and operational guardrails will be better positioned to scale agent adoption securely. The following steps provide a practical framework.

• Inventory and Classify Your Agent Landscape
  Start by mapping which agents are already operating in your environment, whether deployed intentionally or embedded within third-party tools. Classify each agent by its access privileges, decision authority, risk exposure, and lifespan (ephemeral vs. persistent). This inventory forms the foundation for every governance decision that follows.
• Establish Identity-First Governance
  Every AI agent that accesses organizational systems should have a unique, verifiable digital identity. This could be either certificate, a cryptographic key, or an equivalent machine credential. Shared credentials and static API tokens are insufficient for environments where agents act autonomously. Build agent identity into your existing PKI and machine identity management infrastructure rather than treating it as a separate concern.
• Define Authorization Boundaries
  For each class of agent, define explicit boundaries: which systems it can access, what actions it can take, and what triggers human review. Attended agents should have clear escalation points. Autonomous agents should operate within well-defined constraints that reflect the organization’s risk tolerance. Document these boundaries as policy, not just as configuration.
• Integrate Agent Oversight Into Existing Security Operations
  AI agent activity should feed into the same monitoring, logging, and audit workflows that cover human users and traditional automation. Security leaders and CISOs should ensure that agent actions are attributable, auditable, and included in compliance reporting. This is particularly important for organizations subject to regulatory frameworks that require demonstrable control over system access and data handling.
• Plan for Scale
  Agent adoption tends to accelerate once initial deployments demonstrate value. Plan your identity infrastructure, governance frameworks, and operational tooling to handle hundreds or thousands of agents — not just the initial handful. Ephemeral agents, in particular, can scale rapidly, and each one requires its own identity lifecycle management. Organizations that build scalable foundations now avoid costly retrofits later.
• Assess and Iterate
  Agent governance is not a one-time exercise. As agent capabilities evolve and deployment patterns change, revisit your classification, authorization policies, and identity infrastructure on a regular cadence. Benchmark your maturity against industry data and peer organizations to identify gaps before they become liabilities.

Why Keyfactor Is Closely Involved in the AI Agent Ecosystem

Keyfactor has spent years solving the challenge of non-human identity, by securing the machines, workloads, and autonomous systems that organizations depend on. AI agents amplify this challenge. They are software entities that authenticate, communicate, and act across systems at a scale and speed that exceeds anything traditional machine identity management was designed to handle.

Keyfactor’s platform gives organizations the ability to govern cryptographic assets at scale, by maintaining full visibility into their certificate landscape while automating the issuance, renewal, and revocation of machine identities. As AI agents become a standard part of enterprise infrastructure, the ability to issue, manage, and revoke machine identities dynamically, across ephemeral and persistent agents alike, becomes a foundational requirement.

This is not a new problem for Keyfactor. It is an evolution of the same problem the company has been solving since its founding: ensuring that every entity acting within an organization’s systems can be identified, authenticated, and trusted.