From Playbooks to Partnerships: How Agentic AI Redefines the MSSP Model

COMMENTARY: What if your next teammate wasn’t human? With agentic AI taking the world by storm, this is a very real possibility. Many wonder, “Will an AI agent replace me at my job?” Depending on your industry, it is certainly possible. The argument can be made, however, that an AI agent won’t be the one to take your job, but a human who pairs with an agent certainly will.

Now is the time to start thinking about AI agents not just as automation tools but as collaborators who can enhance productivity and address fatigue across various industries. That said, one industry, in particular, is in dire need of the innovation and change that agentic AI brings: Managed security service providers (MSSPs).

Alleviating complexity and risk

Common frustrations that originated with on-premises security—such as overwhelmed SOC teams, dynamic threats, false positives, and alert fatigue—have only intensified and expanded with MSSPs. While risk has been mitigated in one sense by shifting it away from direct organizational oversight, the complexity remains, and the burden now falls on the MSSP. Given all that risk and burden, how can agentic AI assist an MSSP?

First, let’s examine the role of agentic AI within the MSSP context. The agent is goal-driven with autonomous decision-making capabilities. It can take initiative, remember context, and make multi-step decisions at scale. By contrasting this with traditional SOAR playbooks and an LLM chatbot that many MSSPs still use today, you can begin to see how agents can transform an MSSP.

Next, many still think AI equals automation. A common question asked is, “How much of our SOC can we fully automate?” While this is a fair and tempting question, handing off repetitive work to an automation engine without context can create as many problems as it solves. Think about traditional SOAR playbooks. Users follow the rules they outline. They don’t adapt midstream. They don’t ask, “Is this normal for this user on this day?” Plus, they definitely don’t know when not to act. Full automation lacks the nuance experienced analysts or purpose-built AI agents bring – context, escalation judgment, and risk weighting. That’s why the future isn’t about replacing analysts with fully automated environments. Instead, it’s about making analysts that much better by pairing them with agentic AI. The agent shifts the conversation from execution to observation, learning, and collaboration. They become teammates. Not just scripts. This is where the real transformation begins.

Last, but not least, consider an agent as your next teammate. Imagine the concept of a traditional pod when it comes to a SOC. You have a group of people who support a specific customer or environment. Now, imagine that you take the concept of people in a pod and you broaden it to people working with AI agents in a pod. Each pod is outcome-driven, built for speed, context, and trust. Analysts are no longer juggling alerts; they’re managing specialized agents trained for specific tasks: a phishing triage agent, a lateral movement detection agent, or one focused solely on insider threats.

These agents aren’t dumb playbooks; they are learning systems. They remember past decisions, adapt based on new threat intelligence, and recommend actions with explainable logic. The analyst becomes the lead strategist, validating critical steps, mentoring the agent’s decision-making, and refining outcomes through feedback. Over time, the agent becomes not just faster, but better.

Achieving real scalability without burnout

When we shift from viewing AI as automation to AI as a teammate, the operational model changes fast—and for the better. You get real scalability without burnout. One analyst supported by task-specific agents can handle five to 10 times the workload without compromising quality. It’s not about working faster but working smarter and more surgically. Skill amplification becomes the norm. Agents will handle the basic work like enrichment, correlation, and noise reduction. Your human analysts are then freed up to make judgment calls and shape detections to drive more proactive security outcomes.

Designing a human-agent collaboration model in the SOC isn’t just about adding AI and flipping a switch. If anything, it introduces a new layer of complexity. Trust becomes a barrier. Analysts need to understand why an agent took an action. If it contained a host, was it because of behavior analytics, correlation rules, or a flawed trigger? If the agent can’t explain itself, analysts won’t use it, or worse, they will ignore it.

What could the future look like?

Fast forward a few years, and an MSSP starts to look more like a digital operations center. Analysts won’t just work with agents; they will manage portfolios of them. Some call this concept “swarms” or “teams” made up of agents.

You will see new human jobs emerge, like “Agent Operation Lead” or “Threat Response Strategist,” where an analyst is focused on training, tuning, and mentoring agents. Junior analysts might learn the ropes from an AI agent rather than a human being.

Imagine watching an agent work through decision paths, explanations, and playbook executions in real time. We will see analyst-to-agent ratios as a metric, not just ticket volume or adherence to an SLA. Think about the maturity of an MSSP being measured not by how many humans are staffed 24/7, but how autonomously and effectively their AI agents operate. Remember, it isn’t about replacing talent; it is about scaling it.

Agentic AI isn’t just about automation; it is about collaboration, scale, and strategic augmentation. The MSSP space, long burdened with complexity, fatigue, and volume, is primed for this shift. Instead of asking how much can be automated, the better question is: “How can AI agents make analysts more effective?”

Agentic AI brings memory, initiative, and context to decision-making. When paired with human analysts in outcome-driven pods, these agents don’t just replace, they amplify. They take on specialized tasks, learn over time, and evolve from tools to trusted collaborators.

This future isn’t hypothetical. MSSPs will soon manage portfolios of agents the way they manage tickets today. Roles will emerge that focus on mentoring and optimizing agent performance. Junior analysts may even learn from agents first. The MSSPs that lead won’t be the ones with the largest headcount; they will have the smartest agent-analyst teams. It’s not about eliminating people. It’s about scaling talent through intelligent collaboration.

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