What Is an Agent Manager? The New Role Every AI-Powered Company Needs in 2026

The software revolution created the product manager. The AI revolution is creating the agent manager. Most companies are already behind.

Harvard Business Review just published a piece arguing exactly this, co-authored by Suraj Srinivasan of Harvard Business School and Vivienne Wei of Salesforce. The comparison to product managers is deliberate: just as software needed someone to bridge engineering and business, AI agents need someone to bridge autonomous systems and business outcomes.

At Beam AI, we have deployed AI agents across dozens of enterprise customers. The companies getting the most value all have one thing in common: someone either internally or externally accountable for how the agents perform. HBR now has a name for that person. From the field, we think they are right.

What Is an Agent Manager?

An agent manager is the person responsible for making sure AI agents deliver actual business outcomes. They are not IT administrators and they are not data scientists. They sit between corporate strategy and the AI systems doing the work.

In practice, the agent manager role includes:

• Performance monitoring: tracking agent quality, speed, escalation rates, and sentiment

• Prompt refinement and workflow optimization: adjusting how agents handle tasks based on real results

• Human-agent handoff coordination: deciding when an agent acts alone and when it escalates to a human

• Root-cause analysis: diagnosing why an agent failed and fixing the underlying issue

• ROI reporting: quantifying the business value agents are delivering

One Salesforce agent manager quoted in the HBR article described it simply: "I start and end my day in dashboards."

That matches what we see. The companies getting real value from AI agents are not the ones with the best models. They are the ones with someone managing, adjusting, and improving those agents over time.

Why the Agent Manager Role Matters: The Data

The HBR article shares data from Salesforce's own deployment that backs up the case for agent managers:

• Their Agentforce platform now resolves roughly 74% of customer support cases autonomously

• Sales development reps went from handling 12 to 15 prospects per day to scheduling 350+ meetings per week

• That translated to a $60 million annualized pipeline and 300+ new clients acquired in four months

• Geographic expansion to the US, Canada, UK, Ireland, Africa, and Japan

These numbers did not come from better AI models alone. They came from better agent orchestration. Someone had to decide which cases the agent handles, which get escalated, how handoffs work, and what the quality bar looks like. That someone is the agent manager.

6 Skills Every Agent Manager Needs

HBR outlines six competencies that define an effective agent manager. We agree with most of them, though a few deserve a reality check from the field.

1. AI Operational Literacy

Agent managers need to understand how agents work, how prompts affect outcomes, and how to diagnose problems. This does not mean they need to write code. The best agent managers we work with understand the logic of what an agent does without building it from scratch. Think of a restaurant manager who can read a recipe but does not need to be a chef.

2. Functional Depth

Domain expertise matters more than AI expertise for agent managers. HBR makes this point well: the best agent managers come from roles where they already understand the business process being automated. If you are automating HR workflows, the agent manager should know HR. If it is finance, they need end-to-end process knowledge.

We see this constantly. Companies that staff agent management with people from the business side get to production faster than those who hand it to IT.

3. Systems Thinking

When you run 80 agents across an organization (which we do with some customers), the agent manager needs to visualize how all those agents interact. One agent's output is another agent's input. If you change how Agent A handles exceptions, Agent B might start receiving data it does not know what to do with.

HBR calls this "multi-agent orchestration." We call it the hardest part of scaling. Understanding agentic workflow patterns is not optional for agent managers working at scale.

4. Change Resilience

HBR describes "weekly test-deploy-learn cycles." In reality, it is daily. Sometimes hourly. The agent managers who succeed are comfortable with constant iteration. They do not wait for perfect conditions before adjusting.

5. Prompt Craftsmanship

Prompt design is a real skill for agent managers. But the HBR article may overstate its difficulty. Most production agents do not need elaborate prompt engineering. They need clear, well-documented process instructions. If you have solid documentation for how a task should be done, the agent can often be live in hours, not weeks.

6. Hybrid Workflow Design

This is the skill HBR nails. The real work of an agent manager is designing when the agent acts alone, when it asks for help, and when it hands off to a human entirely. Get this right and your agents feel reliable. Get it wrong and people lose trust fast.

Our experience confirms this. Companies that map out human-AI handoff points before deploying agents see fewer failures and faster adoption across the organization.

What HBR Gets Right About Agent Managers

The core thesis is correct. AI agents are not set-and-forget tools. They need oversight, adjustment, and someone accountable for their performance. The comparison to product managers is useful because it frames the agent manager as a strategic role, not an operational one. An agent manager does not babysit a bot. They decide what the bot should do, measure whether it is working, and adapt when it is not.

HBR also makes a smart hiring point. The most effective agent managers did not come from AI backgrounds. They came from service delivery, operations, and customer success. The article quotes one agent manager who had a background in audio production and conversational design. What they all shared was judgment and curiosity, not credentials.

What the Agent Manager Role Looks Like in Practice

There are three things the HBR article does not cover that we see in the field every day.

The documentation problem. Before an agent manager can manage anything, the process the agent handles needs to be clearly defined. Many companies skip this. They want to deploy agents against processes that live in people's heads, not on paper. The agent manager's first job is often making sure the process is documented well enough for an agent to follow it. Without this, no amount of agent management will help.

The accuracy conversation. HBR talks about performance monitoring but does not address what "good enough" looks like for agent managers. In practice, 40% of agentic AI projects fail because expectations are misaligned. An agent manager needs to have frank conversations about what accuracy the business requires, what accuracy is realistic today, and where those two numbers need to meet.

The fear factor. The article briefly mentions change management but underestimates how real the resistance is. People do not say "I am afraid of AI." They say "the technology is not good enough yet." Agent managers need to navigate organizational psychology as much as agent performance. The best ones build trust gradually, starting with small wins before expanding scope.

Where Should an Agent Manager Report?

HBR suggests several reporting structures for agent managers: digital customer success teams, sales management with centralized AI alignment, or cross-functional transformation offices.

From our experience, the structure matters less than two things:

1. The agent manager must have direct access to the business process owner

2. They must have authority to change agent behavior without going through a multi-week IT approval process

Companies that bury the agent manager role inside IT get slow results. Companies that embed agent managers inside the business unit move faster and see better outcomes.

How to Get Started with Agent Management

Agent managers are not a temporary role. HBR compares the position to DevOps and site reliability engineering: functions that emerged from a technological shift and became permanent parts of how companies operate.

We agree. The companies deploying dozens of agents today already have people doing agent management work, whether or not they carry the title. The ones that formalize the agent manager role, invest in the right skills, and give these people real authority are the ones pulling ahead.

If you are running AI agents or planning to, the question is not whether you need an agent manager. It is whether you already have one and just have not given them the title yet.