Agent Manager

AI agents are rapidly moving from proofs of concept to production systems that write code, support customers, monitor compliance, and orchestrate operational workflows. As adoption accelerates and the value is projected to exceed $450 billion by 2028, the stakes are rising just as quickly.

With this shift comes a new set of risks. Agents can generate inconsistent outputs, apply logic that doesn’t align with policy, or interact with sensitive data in ways that require oversight. It is no longer enough to deploy agents and assume existing teams can absorb the responsibility. Someone must be accountable for how agents behave, what they generate, and the impact they have across the business.

To fully realize their potential, enterprises need more than technical guardrails. They need dedicated Agent Managers who combine AI fluency, business context, and the operational discipline required to manage human–AI collaboration at scale.

This Q&A explores that shift. Our president and co-founder, Ramesh Maturu, explains why Agent Managers are emerging as a critical layer in enterprise AI, what the role looks like in practice, and how organizations are beginning to embed it into their operating fabric.

1. As AI agents become core to how businesses operate, what do you think is the real urgency behind the role of an Agent Manager?

The urgency is really about risk and scale. 

AI agents are incredibly powerful, but they’re not infallible. They hallucinate, misinterpret prompts, and often rely on external large language models that sit outside an organization’s secure perimeter. That creates a large attack surface for security breaches, misinformation, and compliance violations. Imagine an AI agent confidently referencing a project that never happened. Even if unintentional, that kind of misinformation can trigger legal exposure, reputational damage, and loss of trust.

The Agent Manager provides the governance and control layer that enterprises need as they industrialize AI. They define guardrails, monitor performance, oversee security and compliance, and catch rogue or low-quality outputs before they reach customers, partners, or regulators. We’ve seen that without this role, organizations end up scaling AI agents with no clear owner for risk, quality, or outcomes. That’s not just a technology gap, in many cases, it creates an enterprise risk and value realization gap.

2. Pyramid Consulting has been shaping the tech industry for 30 years. How does this role compare to other groundbreaking IT positions we’ve seen in the past?

Think of Agent Managers as a natural evolution in the lineage of tech disruption roles, similar to how cloud architects or DevSecOps leaders emerged during the cloud boom. Those roles started off niche, but quickly became essential. Now, Agent Managers are doing the same for AI. 

But here’s the difference: instead of focusing on infrastructure or dev workflows, this role brings together AI expertise with governance, compliance, and real-time control. It’s not just about managing code anymore, it’s about coordinating autonomous systems across the business. This is where decades of software experience meet the demands of an AI-native future. 

3. What skill set defines or will define the role of an Agent Manager? Are there any certifications or technical background that makes them stand out? 

Agent Managers are multidisciplinary operators. They combine deep AI fluency with a strong governance and operations mindset. At a core level, they need to be able to: 

• Understand how large language models and agent architectures work.
• Design and refine prompts, workflows, and policies.
• Evaluate outputs for hallucinations, bias, drift, and quality.
• Work hands on with AI observability, monitoring, and governance tools.

Equally important are risk and operations skills. Agent Managers live in dashboards: tracking uptime, latency, usage patterns, agent performance, and compliance issues in real time. A grounding in security protocols, ISO and SOC standards, and enterprise monitoring systems is essential. 

From a platform standpoint, familiarity with Microsoft 365 Copilot, Microsoft Copilot Studio, Power Platform, Azure AI Foundry, and Microsoft Fabric is increasingly valuable as those platforms become foundational to enterprise AI. Experience with AWS SageMaker and Google Cloud Vertex AI is a clear differentiator in multicloud environments. 

Formal “Agent Manager” certifications are only beginning to emerge, so the strongest signal today is proven, hands on experience. People who have worked directly with platforms like AgentOps, Anthropic Claude, and governance tools such as Credo AI and Microsoft Azure AI Governance stand out as ready to own this role. 

4. What kind of work are Agent Managers undertaking today? Are they focused on specific areas like app development, RevOps, or supply chain, or is this more of a centralized role that spans across business functions? 

It’s both specialized and cross-functional.

Agent Managers today are deployed across RevOps, compliance, customer support, and even software engineering, basically everywhere AI agents are embedded in workflows. For instance, in regulated industries, agents monitor evolving laws across geographies and instantly update documentation and alerts. In software delivery, agents are layered between front-end apps and back-end services to accelerate coding, testing, and deployment.

The Agent Manager ensures consistency, quality, and strategic oversight across all these areas. It’s a glue role that ensures agents don’t go rogue, or worse, silently fail. 

5. Which industries are seeing or will see the impact of agent managers most evidently? Who has been an early adopter?

The software industry is clearly out in front. Engineering teams are embedding agents into development environments to accelerate code generation, automate testing, manage pull requests, and streamline deployments. In that context, Agent Managers are already acting as supervisors, overseeing agent behavior, measuring performance, and enforcing enterprise standards. 

But the impact is quickly broadening into other sectors where regulation, complexity, and customer trust are non-negotiable. For example: 

• Financial services using agents for risk analysis, regulatory monitoring, KYC / AML workflows, and customer engagement. 
• Healthcare and life sciences supporting clinical documentation, coding, research synthesis, and patient communication under tight oversight. 
• Retail and e commerce powering personalization, product recommendations, merchandising decisions, and content generation at scale. 
• Manufacturing and supply chain enabling demand forecasting, exception handling, inventory optimization, and logistics decision support. 

In all of these industries, the pattern is the same: as soon as agents touch regulated data, high value decisions, or customer experience, the need for a dedicated Agent Manager becomes obvious. Software is the proving ground, but regulated and customer-centric industries are where the role becomes highly visible, very quickly. 

6. Where should agent managers sit in the organization chart? Will it change the accountability for revenue, risk, and customer experience? 

Agent Managers sit at the intersection of technology, operations, and risk.

They don’t replace existing leaders; instead, they create a horizontal control plane across AI initiatives, similar to how a CISO or Head of DevOps operates today. In most enterprises, we expect Agent Managers or “Agent Operations” teams to sit within the CIO, CTO, or Chief Digital / Transformation Officer organization, with strong alignment to the COO and risk / compliance leadership. 

As AI agents begin to drive real impact on customer experience, revenue, and operational efficiency, this role naturally becomes a central point of accountability. They will: 

• Influence how value is delivered through AI‑driven workflows 
• Govern how and where data is accessed, transformed, and exposed 
• Shape how customers and employees experience AI‑generated interactions 

So, while the Agent Manager may not directly own P&L, they will materially influence revenue realization, risk posture, and experience quality. In our view, they’re not just managing tools, they’re managing the outcomes of an AI‑enabled operating model. 

7. Why are organizations hesitant in hiring for this role? Does hiring an agent manager mean replacing someone else? 

Change is hard, especially when it’s unclear what success looks like. Many leaders are overwhelmed by the pace of AI evolution and uncertain about ROI. Others fear workforce disruption. Tasks once handled by developers, analysts, or support teams are now being automated, which naturally raises concerns about job security. 
 
Ironically, Agent Managers are here to manage that transition. They don’t replace people, they manage the shift. They identify which tasks can be handed off to agents, monitor for quality and risk, and ensure humans stay in the loop where needed. In that sense, the role isn’t about removing people, it’s about making sure the right people are doing the right work alongside AI. 

8. How do agent managers collaborate with human teams? Where do you think it’s crucial to keep a human in the loop? 

Agent Managers are the bridge between people and AI. They make sure AI agents stay aligned with business goals, keep an eye on performance, flag anything unusual, and step in to fine-tune behavior when needed. Think of them as the orchestrators of human-AI collaboration. They’re especially important when agents run into exceptions, edge cases, or anything that feels ethically gray.

For example, a support agent might handle 90% of inquiries on its own, but that remaining 10%? The tricky, sensitive cases? Those get passed to a human. And it’s not just about jumping in when something goes wrong. Agent Managers are also shifting toward a “human-above-the-loop” model: setting up smart policies, monitoring systems at scale, and managing risk from a higher level without needing to intervene in every decision. They strike the right balance between speed and safety, automation and accountability.