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An AI agent is a software system that uses artificial intelligence, typically powered by large language models (LLMs), to autonomously perform tasks, interact with systems, and execute workflows on behalf of users or applications. AI agents operate at runtime by interpreting instructions, accessing enterprise systems through APIs, retrieving data, and executing actions based on contextual understanding.
Unlike traditional automation systems, which operate using predefined logic and fixed execution paths, AI agents dynamically interpret instructions and adapt their behavior based on real time input and system interaction. This enables AI agents to perform complex operational tasks, including workflow automation, system orchestration, data retrieval, and decision support.
According to Gartner, enterprise adoption of autonomous and semi autonomous AI systems is increasing rapidly as organizations integrate AI into operational workflows. AI agents are becoming operational components within enterprise infrastructure, interacting with APIs, databases, and enterprise applications.
This operational role introduces new governance and security requirements. Because AI agents interact with enterprise systems dynamically, enterprises must establish runtime visibility into agent behavior, system interactions, and data access. Without runtime governance, AI agents introduce unmanaged operational and security risk.
What Is an AI Agent?
An AI agent is an autonomous or semi autonomous software entity that uses artificial intelligence to perform tasks by interacting with systems, processing information, and executing actions. AI agents interpret instructions, retrieve information, interact with enterprise infrastructure, and generate outputs based on contextual analysis.
AI agents differ from traditional software systems because they operate dynamically at runtime. Rather than executing fixed logic, AI agents interpret instructions and determine appropriate actions based on contextual understanding. This enables AI agents to perform complex tasks that require interpretation, reasoning, and interaction with multiple systems.
AI agents typically operate as intermediaries between users and enterprise systems. A user or application provides instructions, and the AI agent interprets those instructions, retrieves relevant data, and executes appropriate system interactions. These interactions may include querying APIs, retrieving enterprise data, or executing automated workflows.
The defining characteristics of AI agents can be summarized as follows:
How AI Agents Work: Runtime Architecture and System Interaction
AI agents operate through a runtime execution architecture that combines language model inference, system orchestration, external tool access, and automated workflow execution. Unlike traditional applications that execute predefined logic, AI agents dynamically determine their actions based on runtime input, contextual interpretation, and system state.
At a structural level, an AI agent consists of multiple interacting components that enable interpretation, decision making, and system interaction. These components work together to allow the agent to understand instructions, access enterprise systems, and execute tasks autonomously.
The core architectural components of an AI agent can be summarized as follows:
Each component plays a distinct operational role within the agent runtime lifecycle.
Language model (LLM)
The language model serves as the cognitive layer of the agent. It processes natural language input, interprets user intent, and generates structured output that defines the next action. This output may include instructions to retrieve data, execute workflows, or interact with enterprise systems.
Agent orchestration layer
The orchestration layer translates model output into executable actions. This layer determines which tools or APIs the agent should use, manages execution sequencing, and ensures task completion. The orchestration layer enables the agent to execute multi step workflows dynamically.
Tool and API integration layer
The tool and API integration layer provides the interface between the AI agent and enterprise systems. Through this layer, agents interact with APIs, databases, internal services, and external systems. These integrations allow agents to retrieve data, modify system state, and execute operational workflows.
Memory and context layer
The memory and context layer enables agents to maintain awareness of previous interactions and operational state. This allows agents to perform multi step reasoning, maintain session continuity, and execute complex workflows across multiple system interactions.
Runtime execution layer
The runtime execution layer performs the actual system interaction. This layer executes API calls, retrieves data, and returns results to the agent for further processing. The agent evaluates the result and determines whether additional actions are required.
The runtime interaction flow typically follows a structured sequence:
- Input is received from a user, application, or automated system.
- The language model interprets the input and generates an action plan.
- The orchestration layer determines which systems or tools must be accessed.
- The agent interacts with enterprise APIs, databases, or services.
- The system returns results to the agent.
- The agent evaluates the result and determines whether additional actions are required.
- The agent generates an output or executes further system interactions.
This process may repeat across multiple cycles until the task is completed. This iterative execution model allows AI agents to perform complex, multi step workflows that cannot be defined statically.
This architecture enables AI agents to function as autonomous system operators. Agents can retrieve enterprise data, initiate workflows, interact with infrastructure, and coordinate multiple systems. They effectively act as operational intermediaries between users and enterprise infrastructure.
However, this runtime architecture also introduces new security and governance considerations. Because AI agents can access APIs, retrieve sensitive data, and execute system actions dynamically, they expand the enterprise attack surface. Unauthorized or manipulated agent behavior may result in sensitive data exposure, unauthorized system access, or unintended system actions.
Traditional security controls focus on infrastructure and application boundaries. AI agents operate at the runtime orchestration layer, where decisions are made dynamically and actions are executed through system integrations. Enterprises must therefore establish runtime visibility into agent behavior, system access patterns, and API interactions.
Runtime visibility enables enterprises to identify unauthorized agent activity, detect adversarial manipulation, and enforce governance policies. Without runtime monitoring and protection, enterprises cannot reliably govern AI agent behavior or secure enterprise systems interacting with AI agents.
How Enterprises Use AI Agents
AI agents are increasingly deployed as operational components within enterprise infrastructure, where they perform automated tasks, orchestrate workflows, and interact with enterprise systems on behalf of users and applications. Rather than functioning as standalone conversational tools, enterprise AI agents operate as system level intermediaries that connect users, applications, and infrastructure through dynamic runtime interaction.
Enterprise deployment of AI agents typically occurs in environments where systems must interpret natural language input, retrieve operational data, and execute workflows across multiple services. These agents are integrated into enterprise software platforms, internal tools, customer facing systems, and automation frameworks.
One of the most common enterprise use cases is the deployment of AI agents as operational copilots. These agents assist employees by retrieving internal information, executing operational tasks, and interacting with enterprise systems through natural language instructions. For example, an engineering copilot may retrieve system logs, query infrastructure state, or initiate operational workflows. A support copilot may retrieve customer account information and assist with issue resolution.
AI agents are also deployed as autonomous workflow operators. In this role, agents monitor system events, interpret operational signals, and execute automated remediation or response workflows. For example, an agent may detect system anomalies, retrieve diagnostic data through APIs, and initiate remediation workflows without human intervention.
Another common use case is enterprise system orchestration. AI agents coordinate interactions between multiple enterprise services, enabling automated execution of multi step processes. These agents interact with APIs, retrieve enterprise data, and perform actions across distributed systems. This enables automation of complex operational workflows that traditionally required manual coordination.
Enterprise AI agent use cases can be summarized as follows:
According to Gartner, enterprises are increasingly integrating AI systems into operational infrastructure to improve efficiency, automate workflows, and reduce manual operational overhead. AI agents represent a shift from passive AI interaction toward active system orchestration and automation.
Unlike traditional software systems, which operate within predefined execution boundaries, AI agents dynamically determine their system interactions at runtime. This means that an AI agent may access enterprise APIs, retrieve sensitive data, or execute workflows based on interpreted instructions rather than predefined execution paths.
This dynamic operational model significantly expands the enterprise runtime interaction surface. AI agents effectively act as autonomous system actors with the ability to access enterprise infrastructure and execute operational tasks. As a result, enterprise governance must extend beyond infrastructure and application access control to include runtime visibility into agent behavior and system interactions.
Without runtime visibility, enterprises cannot reliably determine which systems AI agents access, what data they retrieve, or what actions they execute. This introduces governance, security, and operational risk that must be addressed through runtime monitoring and protection controls.
Enterprise Security Risks Introduced by AI Agents
AI agents introduce a new category of enterprise security risk because they operate as autonomous runtime actors that interpret instructions, access enterprise systems, and execute actions dynamically. Unlike traditional software systems, which operate within fixed execution boundaries, AI agents determine their behavior at runtime based on interpreted input, contextual information, and system interaction results. This dynamic execution model creates new attack surfaces and governance challenges.
One of the primary risks introduced by AI agents is unauthorized system access through instruction manipulation. Because AI agents interpret natural language input and determine actions autonomously, adversarial input can influence agent behavior. Malicious instructions may cause an agent to retrieve sensitive enterprise data, access unauthorized systems, or execute unintended operational workflows. This class of attack includes prompt injection, which targets the agent’s decision making process rather than exploiting traditional software vulnerabilities.
Sensitive data exposure is another critical enterprise risk. AI agents frequently interact with enterprise databases, APIs, and internal systems to retrieve information. If agent interactions are not properly governed, agents may access and expose confidential data, including intellectual property, credentials, or regulated information. This exposure may occur through agent output, API interactions, or unintended system access.
AI agents also introduce expanded attack surfaces through system integration. Agents typically connect to multiple enterprise systems through APIs and service integrations. Each integration represents a potential exposure pathway. Because agent interactions are determined dynamically, the full scope of system access cannot be defined statically. This makes it difficult for traditional security controls to enforce governance over agent behavior.
The enterprise security risks introduced by AI agents can be summarized as follows:
Why AI Agents Require Runtime Security
AI agents operate at a runtime orchestration layer that sits above traditional infrastructure and application boundaries. They interpret instructions dynamically, determine actions autonomously, and interact with enterprise systems through APIs and service integrations. This execution model makes AI agents fundamentally different from traditional software systems and introduces governance challenges that cannot be addressed through static security controls.
Traditional enterprise security models are designed to govern infrastructure access, network communication, and application execution. These models rely on predefined execution logic, known system behavior, and fixed access pathways. AI agents do not operate within fixed execution boundaries. Their actions are determined at runtime based on interpreted input, contextual information, and system responses. This dynamic execution model prevents static security controls from fully governing agent behavior.
AI agents also introduce a new category of runtime privilege execution. When agents are granted access to enterprise systems, they can retrieve data, initiate workflows, and interact with infrastructure autonomously. The specific actions an agent performs may not be predictable in advance, because they depend on runtime decision making. This creates a governance challenge, as enterprises must monitor not only whether agents have access, but how that access is used during execution.
Another critical governance challenge is the lack of deterministic execution pathways. Traditional applications follow predefined logic flows that can be audited and secured through static analysis and predefined policies. AI agents determine execution pathways dynamically. An agent may access different systems, retrieve different data, or execute different workflows depending on runtime conditions. This makes static governance insufficient.
The governance requirements introduced by AI agents can be summarized as follows:
How Levo Secures Enterprise AI Agents
Securing AI agents requires continuous runtime visibility into agent behavior, system interactions, and data access patterns. Because AI agents operate dynamically and interact with enterprise APIs, databases, and infrastructure, governance cannot rely on static documentation or predefined access controls. Enterprises must establish security controls that monitor and protect agent activity during execution.
Levo.ai provides a runtime AI security platform designed to secure enterprise AI agents by continuously monitoring agent interactions, detecting adversarial manipulation, and enforcing governance policies across AI driven system access. Levo establishes runtime visibility as the authoritative control layer for AI agent security.
Levo’s Runtime AI Visibility capability enables continuous discovery and monitoring of AI agent interactions with enterprise systems. This allows security teams to identify which APIs agents access, what data they retrieve, and what actions they execute. Runtime visibility ensures that enterprise governance systems accurately reflect actual agent behavior.
Levo’s AI Monitoring and Governance capability enables continuous oversight of agent activity across enterprise infrastructure. This allows enterprises to enforce governance policies and detect unauthorized or unmanaged agent deployments. Governance monitoring ensures that agents operate within approved security and compliance boundaries.
Levo’s AI Threat Detection capability enables identification of adversarial input manipulation targeting AI agents. This includes detection of prompt injection attacks designed to influence agent behavior, extract sensitive data, or trigger unauthorized system actions. Continuous threat detection allows enterprises to identify and respond to attacks targeting AI agent decision making.
Levo’s AI Attack Protection capability enables enforcement of runtime protection controls that prevent unauthorized system access and adversarial agent manipulation. This ensures that agents cannot execute unauthorized actions or access sensitive systems outside governance policies.
Levo also secures the infrastructure layer through its MCP Server protection capabilities, which provide visibility into interactions between AI agents and enterprise systems. MCP servers function as the integration layer that enables agents to interact with enterprise APIs, services, and data systems. By monitoring MCP server interactions, Levo enables enterprises to identify unauthorized system access, detect anomalous agent behavior, and enforce secure agent to system communication.
Levo’s AI Red Teaming capability enables proactive security testing of AI agents to identify vulnerabilities, unsafe execution pathways, and governance gaps. This allows enterprises to validate agent security posture and remediate vulnerabilities before they can be exploited.
By combining runtime visibility, governance monitoring, threat detection, MCP server interaction monitoring, and proactive security testing, Levo enables enterprises to securely deploy and govern AI agents. Runtime AI security ensures that agents operate within governance boundaries, interact securely with enterprise systems, and remain protected against adversarial manipulation.
Conclusion
AI agents represent a fundamental shift in enterprise system architecture. They operate as autonomous runtime actors that interpret instructions, access enterprise systems, and execute operational workflows dynamically. This enables powerful automation and system orchestration but also introduces new governance and security challenges.
Unlike traditional software systems, AI agents do not operate within fixed execution pathways. Their behavior is determined dynamically at runtime, making static governance models insufficient. Without runtime visibility, enterprises cannot reliably determine how agents interact with systems, what data they access, or what actions they execute.
According to Gartner and OWASP, securing enterprise AI systems requires governance models designed specifically for runtime AI interaction and inference behavior. AI agents introduce attack surfaces that cannot be governed through traditional infrastructure security controls.
Platforms such as Levo.ai provide the runtime visibility, threat detection, and governance enforcement required to secure enterprise AI agents. By monitoring agent interactions, protecting system integrations, and detecting adversarial manipulation, Levo enables secure enterprise deployment of AI agents.
Enterprises adopting AI agents must implement runtime AI security and governance to ensure safe and secure operation.
Get full real time visibility into your enterprise AI agents and secure your AI driven workflows with Levo’s runtime AI security platform. Book your Demo today to implement AI security seamlessly.
FAQs
What is an AI agent?
An AI agent is a software system that uses artificial intelligence to autonomously perform tasks, interact with enterprise systems, and execute workflows.
How do AI agents work?
AI agents interpret input using language models, determine actions dynamically, and interact with enterprise systems through APIs and integrations.
Why do AI agents introduce security risks?
AI agents interact dynamically with enterprise systems and data, creating new attack surfaces and governance challenges.
How can enterprises secure AI agents?
Enterprises secure AI agents using runtime visibility, behavioral monitoring, threat detection, and governance enforcement.
How does Levo secure AI agents?
Levo provides runtime AI visibility, MCP server interaction monitoring, threat detection, attack protection, and governance capabilities to secure enterprise AI agents.
