AI agent management: a new profession emerges to orchestrate autonomous systems in companies

As artificial intelligence agents become more widespread in companies, there is a growing need for professionals who can coordinate systems that perform tasks with varying levels of autonomy. Rather than simply using tools to write texts or analyze data, these specialists organize workflows, monitor results, and set limits for automated decisions.

The role still lacks a standardized title in the market, but activities related to orchestrating, supervising, and governing agents are gaining importance. Companies face the challenge of transforming experimental AI projects into reliable operations with measurable results and risk control.

What does an AI agent manager do?

An AI agent is a system that interprets goals, plans steps, and uses tools to perform tasks with minimal human intervention. Unlike a conventional chatbot, which typically responds to requests, an agent can consult databases, trigger other systems, and continue a process.

The AI agent manager coordinates this set of systems. Their responsibilities may include selecting tasks to automate, defining execution criteria, monitoring errors, and determining when a decision should be reviewed by a human.

For example, in human resources, agents can organize resumes and schedule interviews. In customer service, they can consult orders and forward requests. In finance, they can gather information for reports and identify inconsistencies in transactions.

The manager must ensure that these activities comply with company rules and that results are verified. In sensitive processes, such as financial or hiring decisions, human supervision remains essential.

A Deloitte study conducted in April 2026 found that only 21% of 3,235 technology and business leaders interviewed across 24 countries considered AI agent governance mature in their organizations, highlighting a gap between the expansion of these systems and the ability to supervise them.

What skills does this professional need?

The role combines technical knowledge with experience in business processes. While it’s not always necessary to develop AI models from scratch, understanding how they work and their limitations is crucial.

Key skills include designing automated workflows, integrating tools and systems, evaluating response quality, and monitoring performance indicators. Knowledge of APIs, data analysis, and automation platforms may be required for more technical roles.

Understanding security, privacy, and governance is also essential. An agent connected to internal systems can access confidential information or perform improper actions if granted excessive permissions or operating without adequate controls.

The ability to map processes is another key differentiator. Before automating an activity, the professional must identify its steps, exceptions, risks, and success criteria, which requires collaboration with technology teams and those familiar with the operation.

Deloitte’s analysis of AI agents in corporate environments emphasizes the importance of limiting systems’ access to data and tools, monitoring their behavior, and defining human responsibilities.

What is the difference between a prompt engineer and an agent manager?

A prompt engineer primarily focuses on developing and testing instructions to guide AI models, aiming to improve the quality, accuracy, and relevance of responses for specific tasks.

In contrast, an agent manager operates at a broader level. Their work involves coordinating systems that can execute multiple steps, use external tools, and interact with each other to complete a process.

For example, a prompt engineer might develop instructions for an AI to summarize financial documents. An agent manager might organize a workflow where one system collects documents, another extracts data, a third checks for inconsistencies, and a human approves the final report.

The two roles can overlap, especially in small teams. The main difference lies in the scope of responsibility: while prompt engineering focuses on interaction with models, agent management considers the functioning of the entire process.

How much does an AI agent manager earn?

There is currently no standardized salary range for the role of AI agent manager. The title is new, and responsibilities may appear in positions with other titles, such as automation specialist, AI engineer, solution architect, AI product manager, or AI operations professional.

Therefore, comparing salaries requires considering job responsibilities, seniority level, country, and technical complexity. A role focused on coordinating processes and suppliers typically requires different skills than one responsible for developing multi-agent architectures and corporate integrations.

Those seeking opportunities in this field can look for job postings mentioning agentic AI, agent orchestration, intelligent automation, and AI governance. These terms help identify related roles, even when the title does not explicitly mention agent manager.

How to prepare for this role?

Technology professionals can deepen their expertise in system architecture, integrations, monitoring, and security. Business, operations, finance, and human resources professionals can start by mapping processes and identifying tasks that can be automated with low risk.

One way to gain experience is to build a pilot project with a defined workflow, such as classifying internal requests. The test should include quality criteria, access limits, action logging, and a human review step for exceptional cases.

The evaluation should not focus solely on execution speed. Error rate, cost per task, time saved, and quality of results also help determine whether automation delivers real benefits.

The consolidation of this role will depend on how companies integrate agents into their operations. For now, agent management represents a growing set of responsibilities that combines technology, processes, and operational control, rather than a universally established position.