Zach Stauber’s day begins before the first customer support ticket even lands in the queue. Stauber, a support agent manager at Salesforce, a global company that provides businesses with a customer relationship management (CRM) platform, manages a fleet of generative AI support agents across support, sales, and marketing on a platform the company calls Agentforce. Stauber describes his routine this way: “Data, Data, Data. I start and end my day in dashboards, scorecards, and agent observability monitoring.” He focuses on how the AI agents are working, but also how they are learning and adapting—much like how a traditional manager might walk the floor, check in with a struggling employee, or huddle with a team on a tricky case.
Salesforce offers a glimpse into what the future of digital work will look like across industries. The Agentforce platform, which Salesforce is using itself and selling to client companies, now autonomously resolves nearly 74% of the company’s inbound customer support cases. Dozens of digital “employees,” all AI agents, are resolving customer issues, drafting personalized emails, and routing more complex cases to human specialists. Each AI agent operates semi-autonomously, learning from feedback, collaborating with other AI agents, and escalating to human support agents when the task is more complex than they can handle, creating a hybrid workforce.
Overseeing this hybrid workforce are agent managers—people like Stauber, who runs an AI-powered human plus technology team. Agent managers are a new kind of leader responsible for orchestrating how AI agents learn, collaborate, perform, and work with human counterparts. Agent managers supervise AI agents in much the same way traditional managers coach and motivate human employees, though the agent managers do spend more of their time focusing on the safety, accuracy, and business alignment of the agents’ work.
As companies like Salesforce, JPMorgan Chase, and Walmart operationalize autonomous AI across functions from customer service to finance, a profound shift in management is underway. The agents being deployed today aren’t just tools to automate tasks; they’re teammates that require training, governance, and performance management. In this shift, agent managers are emerging as one of the most critical roles in the age of AI.
Just as product managers became indispensable during the software revolution, agent managers are fast becoming the connective tissue between strategic intent and autonomous execution. Their mission: to make AI agents smarter, faster, safer, more impactful, through orchestration across agents and with human counterparts.
The Hybrid Workforce for Sales Development
An immediate illustration of this shift comes from Salesforce’s sales development representatives (SDRs). Previously, the company’s Agentic Transformation and Sales Development team was responsible for following up on leads, typically handling dozens of contacts but only managing to speak with 12 to 15 prospects daily. This disparity meant valuable leads were neglected due to human capacity limits.
Now, an AI agent operates as a part of the hybrid team. The SDR AI agent takes over the initial, high-scale, low-value interactions: personalized outreach, qualification, and continuous follow-up on stale leads. The SDR agent ensures the sales cycle never stops, “while my team is sleeping, our agents are already interacting with customers,” said Vanessa Tabbert, VP of the team.
The integration of agents transformed the team’s capacity, from booking 150 meetings in 30 days to achieving over 350 meetings in a single week after launch with the same lead volume. This led to generating $60 million in annualized pipeline and acquiring over 300 new clients within four months.
Crucially, this AI augmented capability allowed for a rapid geographical rollout managed by a “two-pizza team” (the Amazon lingo for small teams). The expansion occurred across major markets in quick succession; the agent is now live across the U.S., Canada, UK and Ireland, Africa, and Japan, with immediate expansion planned for Australia, New Zealand, South Asia, and more.
The evolution to agentic SDRs transformed the human seller’s role from one focused on low-connect prospecting to one centered on high-value human interaction, empathy, and creative problem-solving. Human SDRs are now freed from filling the broad funnel. Instead, they can focus on human capabilities of persuasion and judgment to accelerate deal progression to close deals. The agent manager’s job is to ensure the autonomous agent workforce is continually adapting, operating safely, and most critically that the AI agent is aligned with the overall sales priorities.
Redefining Ownership
Achieving sustained success with agentic AI requires an attitudinal shift championed by line of business (LOB) owners. Crucially, this shift redefines AI agent ownership. In the pre-agentic world, AI deployment lived within IT or the data science organization. In the agentic era, business units need to take control. Lines of business owners should be responsible for designing, testing, and governing the agents that power their workflows, much as they would for the human workforce.
At Salesforce, for instance, customer success teams define the AI agent’s tone, escalation rules, and success metrics, all under the stewardship of agent managers. If AI agents are performing real work for a business unit, that unit must own their performance. This requires establishing a clear philosophy that AI agents are complementary partners, not competitors, to human talent. For instance, in customer contact centers, AI agents (and human agents) are managed by the call center teams themselves rather than by the IT team. The contact center teams, not the technology organization, are accountable for problem solving and serving customers, whether the service is provided through AI agents or humans.
This approach creates both opportunity and complexity. To be successful, agent managers must blend deep functional expertise with operational AI literacy. They must know not only what the business wants, but how to teach an AI agent to achieve it, safely, consistently, and transparently.
Defining This Pivotal Role
From our interviews with Salesforce’s internal Agentforce group, we found that agent managers typically operate at the crossroads of customer experience, AI operations, and product management. Their mandate: translate functional expertise into measurable AI performance.
Depending on organizational maturity, agent managers may report to:
- Digital customer success (as at Salesforce),
- Sales management (with tight alignment to a centralized AI Operations team),
- Or a cross-functional Digital Transformation Office
But across all settings, this is not likely to be a transient role. It’s a durable operating function, akin to DevOps or Site Reliability Engineering, born of a structural change in how work will be performed in a hybrid digital-human fashion.
Their responsibilities combine business insight, analytical rigor, and hands-on interaction with AI systems, creating a new kind of operational leadership: AI orchestration. The role typically involves:
- Monitoring agent performance: quality, speed, escalation, and customer sentiment
- Refining prompts and workflows to improve accuracy and tone
- Managing handoffs to human agents and ensuring escalation when required
- Conducting root-cause analysis of failed cases to drive continuous improvement
- Quantifying impact through ROI analysis and executive reporting
What Makes an Effective Agent Manager
We noted that the best agent managers resemble early product managers or site reliability engineers. They excel at the intersection of human judgment and machine performance.
Their success depends on six critical capabilities:
- AI operational literacy: They understand how agents operate, how prompts drive outcomes, and how to diagnose system failures.
- Functional depth: They possess deep knowledge of the business process the agent supports, whether customer service, finance, or logistics.
- Systems thinking: They visualize how agents interact across workflows, departments, and even other agents, to achieve “multi-agent orchestration.”
- Change resilience: They adapt quickly to shifting models and business needs, refining agent logic in weekly “test-deploy-learn” cycles.
- Prompt craftsmanship: They excel at designing and refining the language and logic that shape agent behavior, the equivalent of employee training for machines.
- Designing work across machines and humans: They know how to create hybrid workflows, assessing limits of machine capabilities, creating human escalation routines as needed. They’re adept at motivating the human workforce in the context of hybrid AI-human work.
Ultimately, an effective agent manager is fluent in the language of business strategy, the operational workings of AI, and in people management.
Successful management in this hybrid era also requires a shift in how human performance is measured. The focus on activity-based KPIs (for instance, making 60 calls a day) is obsolete. The new model shifts KPIs to focus on outcomes that depend on orchestration and influence—a recognition that performance now depends on how well a person is tuning their agent and running workflows with it. This elevation means management should focus organizational energy on maximizing the efficiency of the entire human-agent system.
Finally, the human workforce must rapidly acquire new, distinct skills to capitalize on this shift. The agent manager facilitates this transformation by enabling employees to move away from low-value, information-retention tasks. The new skill requirements include “managing AI,” i.e., knowing how to command the agent effectively and “managing engagements,” i.e., developing the acumen necessary to engage successfully in the high-value human interactions.
Hiring and Developing Agent Managers
While agent managers can come from a variety of disciplines, the most effective ones emerged from roles already accountable for service quality, customer outcomes, and operational judgment. These individuals brought deep domain expertise and a lived understanding of what “good” looks like in real customer interactions, capabilities that proved more necessary than formal AI credentials.
Zach Stauber’s trajectory illustrates the background that’s desirable for this emerging role. Trained in audio production and shaped by years in service delivery and conversational design, including leading early chatbot teams, Stauber was selected for what he calls “earnest curiosity”: a willingness to experiment, learn quickly, and take ownership as AI reshaped the work.
Early deployments also clarified how agent work should be structured. Agent managers focused on using natural language, shaping intent, judgment, and tone by translating complex business logic into simple, adaptive instructions an AI agent can follow. They worked closely with AI engineers, who often sat within the IT department and focused on deterministic execution: data parsing, system integrations, and the technical steps required when an agent takes action. Internal groups that paired them deliberately scaled faster and with greater trust.
The broader lesson is that agent management is not necessarily a technical role. Organizations that developed the role successfully treated the role as an apprenticeship, immersing managers in live operations, failure reviews, and iterative test–deploy–learn cycles, while clarifying decision rights and escalation paths early. Those that centralized agent management entirely within IT or indexed on AI credentials often saw agent managers function technically while failing strategically. As AI agents take on execution, success will increasingly depend on the quality of managerial judgment that guides them.
The Rapid Growth of a New Discipline
As embedded intelligence spreads across every function, from HR to finance to supply chain, the need for dedicated orchestration will only grow. Without someone accountable for how agents are designed and governed, even the most sophisticated AI initiatives will stall.
Yet identifying and developing this new class of leaders won’t be easy. They will need a blend of business insight, AI fluency, and ethical judgment. Companies will have to invest in new training pathways, integrating business process design, performance analytics, AI expertise, and AI governance into traditional management development programs.
This moment therefore demands a call to action. Within 12 to 18 months, “agent manager” will likely be a standard title in AI-first enterprises, a career path for leaders who know how to scale impact through intelligent automation.
Our observation from our research is that technology alone doesn’t create transformation—leadership does. The agent manager is a crucial part of that leadership, the bridge between corporate intent and autonomous execution, between human judgement and machine precision.
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