What is AI agent management?

The concept has gained attention as artificial intelligence systems have become more autonomous and integrated into enterprise operations. Early AI systems often performed narrow tasks with direct human oversight. Modern AI agents can complete multi-step workflows, interact with external systems and make decisions with limited intervention.

As organizations deploy larger numbers of agents across departments and platforms, they increasingly need centralized ways to manage agent security and operational control. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.¹

AI agent management usually combines technical controls with operational policies. Technical controls might include monitoring tools, permission systems and activity logs. Operational policies often define who can deploy agents, what tasks agents can perform and how human review should work. Together, these controls help organizations manage risk while still allowing agents to operate efficiently.

Organizations often approach AI agent management as a layer between AI models and business operations. This management layer can coordinate multiple agents, route tasks and track performance over time. It can also help organizations standardize how agents access internal systems or external applications.

Many organizations now connect this layer to the broader idea of an agent control plane, which provides centralized oversight for agent orchestration, permissions, identity management and lifecycle operations. This structure supports scaling agent deployments with greater visibility and operational consistency.

The field also reflects a broader change in how businesses think about AI adoption. Traditional software usually follows fixed rules that developers define in advance. AI agents can behave less predictably because they respond dynamically to prompts, data and changing environments. Many of these systems are built on large language model (LLM) and generative AI technologies that can adapt responses at run time. That flexibility creates new opportunities, but it also introduces AI agent governance challenges that many existing IT management practices were not designed to address.

AI agent management is becoming essential for scaling agentic AI deployments. Without coordinated oversight, deployments can become fragmented, inconsistent with governance policies and limited visibility into systems’ activity. This problem is sometimes called AI agent sprawl, where large numbers of disconnected agents become difficult to monitor, maintain or govern effectively. In response, vendors are introducing dedicated platforms that bring orchestration, governance and observability into a unified management layer.

AI agents are changing how organizations operate. Salesforce customers report that AI agents and assistants are delivering strong ROI in IT, sales and customer service.² As agents take on broader responsibilities, organizations need clearer structures for coordinating their activity and managing how they interact with employees and systems.

The move toward agentic AI also changes how businesses think about operational oversight. Traditional software systems are predictable and tightly scoped. AI agents operate with more flexibility because they can interpret goals, make recommendations and interact with multiple tools in real time. This flexibility improves efficiency while introducing complexity. Organizations need centralized ways to monitor behavior, manage permissions and maintain visibility across large networks of agents.

More businesses are moving toward multi-agent systems. Different departments might deploy specialized agents for finance, customer support, security or data analysis. Over time, these systems can become fragmented if each team manages agents independently. A management layer helps organizations create consistent governance models, shared operational standards and centralized oversight across departments and platforms.

Multi-agent environments are leading to new responsibilities for IT, security, compliance and platform teams. In some organizations, AI governance groups are expanding to include oversight of agent orchestration, identity management and lifecycle operations. As adoption grows, AI agent management is becoming part of the broader infrastructure that supports enterprise operations and long-term AI initiatives.

The importance of AI agent management will likely increase as organizations integrate agents more deeply into core business processes. AI agent management will help organizations maintain control over increasingly complex agent ecosystems while supporting broader efforts of business transformation.

Several factors are driving the adoption of AI agent management:

Concerns about fragmented deployments: Without centralized oversight, organizations can struggle with disconnected agents, inconsistent governance and limited accountability across teams.

Demand for greater visibility: Businesses increasingly need insight into how agents behave, which systems they access and how decisions are made at run time.

Expansion of multi-agent environments: Many businesses are deploying specialized agents for different functions or departments. Management platforms help coordinate these agents within a shared operational framework.

Growing operational complexity: AI agents can interact with various systems, tools and workflows at the same time. Organizations need centralized coordination as these environments become more interconnected.

Integration with enterprise systems: AI agents are becoming more connected to cloud platforms, application programming interfaces (APIs) and internal business applications. This integration increases the need for operational management layers.

Need for centralized governance: Organizations want more consistent policies around permissions, approvals and operational oversight as agent deployments expand.

Shift from experimentation to operational scale: Many enterprises are moving beyond isolated AI pilots and building long-term strategies for managing agent ecosystems. The growing availability of AI tools, no-code platforms and reusable templates is also making AI agent deployment more accessible across business teams.

AI management use cases vary by industry and business size. Most organizations adopt AI agent management when agents begin interacting with multiple systems, workflows or teams. Real-world AI agent use cases include:

AI agents are increasingly used in BPA to coordinate workflows across finance, procurement and operations teams. Some organizations are also extending these workflows into supply chain environments where AI agents help track inventory activity and flag disruptions. For instance, an AI agent might process expense submissions, detect unusual activity and route high-value expense requests to receive further review.

Many businesses use AI agents to handle support requests, route tickets and retrieve information from internal systems. AI agent management helps organizations monitor how agents interact with customers and apply rules around approvals or escalation. These systems are increasingly used to improve digital customer experiences while still maintaining human oversight for sensitive interactions. For example, a support agent might handle routine billing questions on its own but transfer more sensitive issues to a human representative.

Organizations often deploy agents that search internal documents, summarize information and support employee research workflows. AI agent management helps control access to sensitive information and track how agents retrieve or share data. For instance, a legal research agent can access approved document repositories while remaining restricted from confidential HR records.

AI agents can automate infrastructure monitoring, incident response and system maintenance tasks. In these environments, management layers help organizations track agent actions and maintain operational visibility across cloud platforms or internal systems. For example, an AI agent might identify unusual server activity and automatically create an incident ticket while alerting security teams.

Some organizations deploy multiple specialized agents that collaborate within larger workflows. AI agent management helps coordinate responsibilities between these agents while maintaining visibility into the overall process. For example, one agent can collect customer data, another can analyze account history and a third can generate recommendations for a sales team.

Industries such as healthcare, financial services and insurance often require stronger governance and auditability around AI systems. AI agent management helps organizations document agent activity and apply operational controls that support compliance requirements. For instance, a healthcare organization can use management tools to track which systems a clinical support agent accessed during a patient workflow.

Security teams can use AI agents to analyze alerts, investigate anomalies or assist with threat detection. Because these agents often interact with sensitive systems, organizations typically apply stricter monitoring and governance controls. For example, a security agent might gather information from multiple monitoring tools before recommending a response for human approval.

Development teams are increasingly using AI agents to assist with coding, testing and documentation tasks. Management platforms help coordinate how these agents access repositories, interact with development tools and follow organizational policies. For instance, an organization might allow coding agents to suggest changes while restricting direct production deployments without human review.

AI agent management is more complex than traditional AI operations because AI agents are more dynamic than earlier AI systems. Traditional AI systems are designed for narrow tasks such as classification, prediction or content generation. The workflow around those systems often remains largely fixed.

AI agents operate differently because they can interpret goals, generate decisions and interact with external tools during execution. That shift creates new operational requirements that standard AI management practices were not built to handle.

Some of the most important differences include:

• Deployment structure: Traditional AI deployments are often isolated to single applications or departments. AI agents are increasingly deployed across interconnected multi-agent environments.
  
  
• Governance scope: Conventional AI governance mainly addresses model risk and data handling. Agent management introduces more concerns around autonomy and policy enforcement.
  
  
• Management focus: Traditional AI operations typically focus on models and data pipelines. AI agent management focuses on coordinating agent behavior across workflows and connected systems.
  
  
• Monitoring requirements: Traditional monitoring often measures model accuracy or uptime. AI agent management also requires visibility into actions, permissions and decision-making paths.
  
  
• Operational flexibility: Earlier AI systems usually operate within predefined boundaries. AI agents can adapt their actions based on context or changing inputs. In many environments, agents are designed for greater context awareness. This ability allows them to respond dynamically as workflows and data flow patterns change during execution.

AI agent management includes the operational and governance capabilities organizations use to deploy, coordinate and oversee AI agents at scale. While platforms might differ in their approach, most enterprise strategies focus on a similar set of core components. These include:

Governance capabilities define the rules and boundaries that guide agent behavior. These controls can include approval workflows, usage policies, operational restrictions and compliance requirements that apply across the organization.

Many organizations include human review mechanisms within agent workflows, especially for high-risk or business-critical tasks. Human oversight can involve approvals, escalation processes or manual intervention when agents encounter uncertainty or unexpected outcomes.

AI agents often interact with enterprise systems, APIs and sensitive data sources. Identity and access management helps organizations control agent access, actions and permission management over time.

AI agents often depend on external tools, enterprise software and cloud services to complete tasks. Integration management helps coordinate these connections while maintaining operational consistency across platforms and environments. Many organizations also rely on an AI gateway, API management tools and standardized connector frameworks to manage interactions between AI agents and external providers.

AI agent management also includes processes for deploying, updating, testing and retiring agents. Lifecycle management helps organizations maintain consistency across environments while supporting version control and operational stability. Some organizations align these activities with broader agent development lifecycle (ADLC) practices to manage AI systems throughout their operational lifecycle.

Observability provides visibility into how agents behave at run time. Organizations use monitoring systems to track actions, review decision paths, identify failures and analyze how agents interact with users or connected systems. Operational dashboards and performance metrics help teams monitor agent activity over time.

Orchestration coordinates how agents run tasks, interact with systems and collaborate with other agents. In multi-agent environments, orchestration layers can route requests, assign responsibilities and manage workflows across different tools or apps.

As organizations deploy larger numbers of AI agents, many are adopting agent control planes to provide centralized coordination and operational oversight. An agent control plane acts as the management layer that connects orchestration, governance and monitoring across agent ecosystems. Rather than managing agents individually, organizations can use a control plane to supervise how agents operate across workflows and systems.

Agent control planes typically support several key functions:

• Centralized orchestration: Control planes coordinate how agents run tasks, share information and interact with other systems or agents.
  
  
• Governance and policy enforcement: Organizations can apply operational policies, access restrictions and approval requirements across multiple agents from a centralized layer.
  
  
• Observability and monitoring: Control planes provide visibility into runtime behavior, system interactions and agent activity across environments.
  
  
• Identity and permissions management: Many control planes help organizations manage authentication, system access and role-based permissions for agents.
  
  
• Lifecycle coordination: Some platforms also support deployment management, version tracking and operational maintenance across agent ecosystems.

The growing focus on agent control planes reflects the increasing complexity of enterprise AI environments. As organizations move toward interconnected multi-agent systems, centralized management layers are becoming more important for maintaining visibility, coordination and operational consistency.

AI agent management helps organizations coordinate, govern and scale AI agents more effectively across enterprise environments. Specific benefits include:

• Better coordination across workflows: Orchestration capabilities support collaboration between agents, enterprise systems and business processes. This coordination can help streamline operations, optimize resource usage and create greater business value from AI deployments.
  
  
• Greater accountability: Audit logs, approval systems and runtime monitoring provide clearer records of how agents make decisions and perform actions.
  
  
• Improved operational visibility: Monitoring and observability tools help organizations track agent behavior, system interactions and workflow activity across environments.
  
  
• More scalable deployments: Standardized management frameworks make it easier for organizations to expand agent usage across departments without creating fragmented environments. These frameworks also help organizations manage growing agent workloads while supporting long-term scalability across enterprise operations.
  
  
• Reduced operational fragmentation: A unified management layer can help organizations avoid disconnected deployments and inconsistent governance practices.
  
  
• Simplified lifecycle management: Centralized deployment and maintenance processes help organizations manage updates, testing and version control more efficiently.
  
  
• Stronger governance: Centralized policies and oversight structures help organizations apply consistent operational standards across multiple agents and teams.

Although AI agent management offers important operational advantages, organizations also face significant challenges as they deploy agents at scale. Many risks stem from the dynamic, autonomous nature of agentic AI systems.

• Agent sprawl: As more teams begin deploying agents independently, organizations can quickly lose visibility into how many agents are operating across the business or what systems those agents can access. Many organizations address this challenge by introducing centralized orchestration layers and shared governance standards that create a more unified operational view.
  
  
• Compliance and accountability concerns: Organizations can face difficulties documenting how agents reached decisions or performed actions, especially in regulated industries. Limited documentation and weak auditability can create challenges for compliance teams and internal oversight efforts. Audit trails, reporting systems and centralized governance policies can help support stronger accountability across agent operations.
  
  
• Inconsistent governance: AI agents are often introduced at different speeds across different business units. Without centralized oversight, teams can follow different approval processes, security practices or operational policies. These inconsistencies can create confusion and compliance concerns. Standardized governance frameworks can help organizations apply more consistent controls across deployments.
  
  
• Limited observability: Many organizations still lack clear visibility into how agents behave at run time. When agents interact with multiple systems or make autonomous decisions, it can become difficult to trace actions or understand why certain outcomes occurred. Runtime monitoring tools, activity logging and audit capabilities are becoming increasingly important for improving operational transparency.
  
  
• Operational unpredictability: Unlike traditional software systems, AI agents respond dynamically to changing inputs, prompts and runtime conditions. This flexibility can improve adaptability, but it also makes behavior less predictable in some situations. Many organizations reduce this risk by incorporating testing processes, human review mechanisms and operational guardrails into agent workflows.
  
  
• Security and access risks: AI agents often interact with sensitive systems and data sources, which can create new challenges around permissions and identity management. Organizations are increasingly using identity management frameworks, role-based access controls and approval workflows to reduce these risks.
  
  
• Workflow complexity: Multi-agent environments can become difficult to coordinate as agents interact across platforms, applications and external services. A simple deployment can evolve into highly interconnected environments. As complexity increases, orchestration platforms and standardized integration practices can help organizations maintain more consistent coordination. As deployments scale, organizations might also need strong oversight of infrastructure usage and model pricing.