For years, SOAR (Security Orchestration, Automation, and Response) platforms have promised faster investigations, reduced alert fatigue, and streamlined response.
And to an extent, that has been true. But today, as the world is moving faster and faster, they have hit their ceiling.
Any security leader building for the future is quietly asking the same question:
Is SOAR still worth the effort?
If you’ve ever had to maintain brittle playbooks, deal with broken integrations, or manage a SOAR platform that feels more like a side project than a force multiplier, you already know the answer. The truth is: SOAR was a step forward, but it’s no longer enough.
A new operational model is emerging – autonomous AI agents. These agents go beyond task automation. They think, adapt, collaborate, and learn. And they’re reshaping what modern security operations look like.
SOAR Automates Tasks. AI Agents Perform Jobs.
At its core, SOAR is a playbook engine. It takes predefined steps and executes them across tools. But those steps must be perfectly defined, constantly maintained, and monitored for drift. It’s automation, but brittle.
AI agents, on the other hand, are goal-driven entities. You assign them outcomes: “Triage this alert.” “Hunt for related indicators.” “Generate a report.” And they figure out how to get there using reasoning, procedure libraries, shared memory, and other agents. It’s the difference between a python script and a junior analyst.
| SOAR Platforms | AI Agents in Bricklayer |
| Rigid, static playbooks | Adaptive, goal-driven workflows |
| High maintenance | Self-improving, autonomous |
| Workflow automation only | Collaborative intelligence |
Static Playbooks Can’t Keep Up With Dynamic Threats
SOAR platforms struggle when things don’t go according to plan.
- A new field in a JSON?
- A changed endpoint?
- A slightly different attack pattern?
That’s enough to break a playbook, or worse, cause silent failures.
AI agents don’t follow a script – they follow intent. They adapt in real time, evaluate conditions, pivot as needed, and even ask for help when uncertain. They behave more like teammates than tools.
Speed and Scale Without Human Bottlenecks
AI agents work 24/7, in parallel, across thousands of alerts. They don’t get tired. They don’t wait for a human to check in. And they get smarter over time by learning from past outcomes.
For SOCs under pressure to do more with fewer analysts, this isn’t a nice-to-have. It’s a strategic necessity.
Built for Collaboration, Not Isolation
Unlike SOAR, which often feels like a separate workflow engine, AI agents are designed to collaborate with both humans and other agents. They share context, document their reasoning, and provide natural language explanations.
This means analysts can supervise, redirect, or learn from their AI teammates, without needing to write a single line of Python.
From Product to Platform: A New Operating System for the SOC
The real promise of AI agents isn’t just speed or scale. It’s a new model for how security gets done. And that requires more than agents alone.
At Bricklayer, we’ve built a full agentic platform—a shared environment where AI agents live, think, coordinate, and execute. It includes memory, governance, task routing, and explainability by default. It’s not another product to bolt on. It’s a foundation to build the modern SOC on top of.
SOAR Is the Past. AI Agents Are the Future.
Every generation of SOC tooling has had its moment. SIEMs gave way to XDR. SOAR automated the repetitive. But as adversaries evolve and alert volumes explode, we need systems that think, not just do.
By 2027, we believe the majority of SOCs will operate with AI-first workflows. Not because it’s trendy, but because it’s cheaper, faster, and better. SOAR will be remembered as a necessary, but limited, phase in the evolution of cybersecurity operations.
Want to see autonomous AI agents in action?
Request your Bricklayer demo today and experience the future of security operations firsthand.
