Introduction: The Need for Collaborative Intelligence in AppSec
According to an October 2024 report from Gartner, 33% of enterprise software applications will incorporate agentic AI by 2028—up from less than 1% in 2024. This rapid growth underscores the transformative potential of agentic AI to streamline inefficiencies, automate repetitive tasks, and enhance operations across the enterprise. When applied through a multi-agent approach to application security—particularly across the software development life cycle (SDLC)—the value these agents can deliver is substantial.
As application complexity grows—driven by widespread adoption of open-source libraries, microservices, serverless architectures, containerization, and now LLMs as a core building block—engineering teams and AppSec leaders face mounting challenges in securing scalable pipelines that protect the business.
The key insight behind the need for a multi-agent network approach is that developers, security teams, and executives are all part of the same workflow—and share the same challenges, though each is impacted differently when issues arise. Specifically, developers focus on identifying and remediating security issues early in the SDLC, ideally within their IDEs, or as part of their pull-request (PR) process. AppSec and security analysts are responsible for orchestrating processes and enforcing policies from the point that code is committed through runtime in the cloud. Meanwhile, CISOs and executive leaders are primarily concerned with overall security posture, application-level insights, and efficient risk management. Security can no longer slow down the business or be a burden to development. Business velocity is a key element in driving innovation especially in the era of AI.
This reality calls for a modern approach—one where multiple AI agents not only automate tasks independently but also collaborate, with humans and with each other, to drive application security efficiency.
What is a Multi-Agent Network in Application Security?
Before diving into more details of the different types of AppSec agents that are required for an efficient cross engineering and security teams’ success, let’s first define what AI agents are, how they differ from traditional GenAI solutions, and how multiple AI agents can work together as a powerful network of agents.
Now that we have defined what AI agents can do, as opposed to GenAI systems, it is important to understand that behind each AI agent there is a well-trained and sophisticated large language model (LLM) that serves as the agent’s brain, if you will. The LLM handles reasoning, decision-making activities, and more. Each agent workflow has its unique LLM requirements (tasks, memory, etc.) which means, if we attach this to the above different personas, each LLM needs to serve its AI agent with the requisite capabilities to autonomously perform its tasks (Dev, AppSec, CISO).
When a set of AI agents is connected into a collaborative network with clear objectives, multiple stakeholders across the organization can benefit.
Real-World Impact: Why This Matters
According to the 2025 DevOps Evolution Report, over 50% of developers spend more than 21 hours per week on security tasks. Meanwhile, Statista reports that critical vulnerabilities are, on average, 214 days old at discovery, and high-severity flaws persist for nearly 190 days—leaving organizations and their customers exposed to potential breaches. These challenges highlight a critical gap: current AppSec approaches are not keeping pace with the demands of fast, secure software delivery. To truly scale and streamline application security, organizations must rethink their approach. By empowering developers with a high-trust, autonomous security platform, enterprises can unlock significant benefits, including increased velocity, greater innovation, improved developer productivity, and lower overall application development costs.
Let’s break down each of the benefits and understand how a network of AppSec AI agents can help engineering and security teams:
MTTR Reduction and Improved DORA Metrics
Many organizations today rely on Google’s DORA metrics to benchmark developer experience, productivity, and delivery speed. From an AppSec perspective, two key DORA indicators stand out: Lead Time for Changes (a proxy for mean time to resolution) and Change Failure Rate. These two indicators track how quickly developers fix critical issues like security vulnerabilities and how often code changes cause failures that require rollbacks or security patches.
Agentic AI autonomously identifies and remediates security vulnerabilities in pre-release code—improving fix times and reducing failed releases. This minimizes security fire drills, costly post-production patches, and business disruption.
Making Security a Shared, Streamlined Responsibility
An agentic AI network strengthens AppSec as a shared responsibility by promoting goal-driven collaboration across teams. It embeds continuous security testing throughout the SDLC, aligning developers, security, and CISOs around a unified, proactive security culture. By leveraging multiple agents to deliver tailored insights to different practitioners based on their roles and outcomes, it creates a more resilient and adaptive security program, as well as a stronger app compliance with ongoing executive visibility.
Lower Engineering Costs Through Smarter Automation
As in any automated process, there are quite a few cost-related benefits. To list a few – time saving on the developer front that can be shifted to other high priority tasks. Fewer security-fix cycles within the pipeline thanks to an autonomous remediation process that results in lower engineering costs as well.
Multi-agent intelligent automation delivers significant cost and efficiency benefits to application security. By autonomously handling detection, prioritization, remediation, and visibility, it reduces the need for manual intervention and shortens fix cycles. Developers save time and can focus on higher-impact tasks, while fewer back-and-forths in the pipeline lead to lower engineering effort and costs. Ultimately, it strengthens security posture while optimizing resource allocation across the SDLC.
To summarize the above, embedding intelligent AI agents directly into the development workflow supports the objectives of reducing noise, cutting manual effort, and enabling real-time, context-aware security actions.
Conclusion: It’s Not Just AI, It’s a Team of AI Agents
As described above, employing a multi-agent network represents a shift from AI assisting to AI collaborating. Organizations that are at high scale from an application and pipeline perspective should consider such a shift as a strategic move towards a more consistent, streamlined application security program. When done right, the adoption of a multi-agent network transforms AppSec into a connected, intelligent ecosystem—built for developers, backed by security, and aligned with the business.
As outlined above, adopting a multi-agent network marks a shift from AI simply assisting to AI actively and autonomously collaborating across the SDLC. For organizations operating at scale, with complex applications and fast-moving pipelines, this evolution is a strategic step toward a more consistent, efficient, and scalable AppSec program.
When implemented effectively, a multi-agent architecture transforms application security into a connected, intelligent ecosystem—developer-friendly, security-driven, and fully aligned with business goals.