Scaling AI Security: How Evo Complements New Agentic Tools

OpenAI’s launch of Aardvark immediately grabbed attention in the AppSec community. Framed as a new “Security Agent,” it aims to automate much of the application security lifecycle, from threat modeling and code analysis to generating pull-request fixes. It’s an ambitious attempt to replicate the work of a full-time security researcher inside the development pipeline.

The headline claim set the tone: a 92 percent success rate when analyzing open source commits. For teams struggling to keep up with rapid releases and expanding codebases, a tool that can surface issues and propose fixes in real time is understandably appealing.

That interest comes with questions. Agentic security introduces systems that not only detect problems but also reason and act, which raises concerns about reliability, workflow integration, and the level of autonomy teams are comfortable embedding into production processes.

Even so, Aardvark signals a change in direction. It shows that major AI players now recognize the need to bake security directly into the software lifecycle, especially as AI-generated code becomes routine. It’s a clear signal that the future of development will demand deeper, more intelligent security automation.

The hype vs. reality of Aardvark

Aardvark has sparked interest, but the reality of running agentic security inside enterprise pipelines is more complex. High accuracy is encouraging, yet even a modest rate of false positives can drain developer time. Teams move fast, and every noisy alert adds friction and cost.

Consistency is another challenge. LLM-based scanning can produce different results on repeated runs, making it challenging to verify whether a fix actually resolves an issue or whether the model simply changes its interpretation. Without stable findings, tracking remediation becomes a matter of guesswork.

Real-world workflows add further pressure. Enterprise pipelines depend on ignores, approvals, and context that automation must respect. A security agent may generate a PR, but teams still need to validate it within existing processes.

Speed also matters. OpenAI didn’t address performance, yet any slow scanning could introduce gaps in coverage, a serious risk when AI systems update rapidly and interact with sensitive data.

And the scope remains limited. Aardvark focuses on open source commits, leaving areas like dynamic testing, dependency analysis, and multi-service architectures as potential partner opportunities. Cost can also be a factor, as some autonomous tools charge by the token, making continuous scanning potentially difficult to sustain at scale.

It’s a promising step, though several gaps remain before agentic security fits enterprise-scale demands. 

Where Snyk provides broad support

Agentic security may be gaining attention, but this is the space Snyk has been preparing for long before Aardvark arrived. Snyk’s advantages become clearer when you look at how it supports developers from the moment code is written. It scales across the complexities of enterprise environments and delivers the governance structure organizations rely on to stay secure at speed.

Strength at the source: Secure code from the start

Where agentic tools analyze code after it’s committed, Snyk shifts protection much earlier in the lifecycle. By scanning code as it’s being written, including AI-generated code, Snyk catches issues before they become rework, pull-request blockers, or production risks. This “secure at inception” approach keeps developers moving and minimizes the friction that often comes with late-stage security reviews.

Evo by Snyk builds on this by delivering consistent, stable results. The same file, model, or agent flow consistently produces the same findings, providing security teams with confidence when validating fixes, tracking progress, and comparing results across branches. Developers receive clear, reproducible feedback, rather than the variability that often accompanies LLM-based scanning.

And because Snyk delivers guardrails directly inside the tools developers already use, security becomes a natural part of the creation process. Instead of interrupting workflows, Snyk reinforces them, making secure coding feel like the default, not an extra step.

Scale and depth built for enterprise reality

AI-native systems introduce requirements that go beyond commit-level scanning. Evo was built for that world. Its multi-layer AI threat detection analyzes models, agents, connectors, and code in real-time, surfacing emerging risks as they appear. With throughput up to a million files per second, teams can monitor fast-changing environments without falling behind.

This foundation is supported by Snyk’s mature testing capabilities. While agentic security tools are still learning to interpret dynamic behavior and dependency chains, Snyk’s DAST and SCA engines have years of production refinement behind them. They can handle the web services, microservices, third-party libraries, and complex dependency graphs that define real enterprise architectures.

And because Snyk is already embedded across more than 4,700 organizations, the platform fits naturally into established CI/CD workflows. It’s already woven into branching strategies, code reviews, approval patterns, and deployment guardrails. That kind of integration maturity can’t be replicated overnight, no matter how capable an agentic tool appears on paper.

Governance, remediation, and total cost confidence

Security doesn’t end when a vulnerability is found. Enterprises still rely on governance: policy controls, auditability, reporting, and human oversight. Snyk provides all of this natively. It provides teams with the structure they need to enforce compliance, manage risk, and ensure that security decisions withstand scrutiny.

Remediation is another critical advantage. While Aardvark can propose patches, Snyk delivers validated, packaged fix recommendations that developers trust. These engines are built to support real workflows, ensure compatibility, and match organizational patterns, not just generate a code snippet and hope it fits.

And unlike token-based pricing models that can make continuous scanning prohibitively expensive, Snyk’s approach avoids unpredictable costs. Teams can scale protection without worrying that each additional scan or commit will inflate the bill.

The advantage of maturity and specialization

Snyk offers a platform with both technical depth and the maturity needed for large organizations. Aardvark moves agentic AppSec forward, but its current capabilities don’t meet the governance, consistency, and operational rigor required in enterprise environments. Snyk continues to lead by strengthening code as it’s written, scaling across complex environments, and providing the governance and consistency teams rely on to move quickly and stay in control.

Aardvark signals significant demand for securing AI-native software

Aardvark reflects the growing interest in more automated security workflows, demonstrating that major players now recognize the value in tools that reason, act, and help teams bridge the gap between detection and remediation. For many organizations, that shift indicates that AppSec is entering a new phase.

For Snyk, what appears to be a disruption is a confirmation. The core ingredients of agentic security already exist across the platform: real-time analysis, consistent findings, and workflow-native guardrails built around how developers ship software. Moving toward more autonomous, collaborative models is a natural extension of what Snyk delivers today.

Aardvark also highlights the challenges these agents must still overcome: oversight, trust, reproducibility, and integration with broader governance programs. As promising as this new category is, organizations will still need the stability and structure that mature platforms provide.

Aardvark reinforces the direction Snyk has been moving for years, combining intelligent automation with the control, scale, and reliability enterprises depend on.

Looking ahead at collaboration, not competition

Agentic security is gaining interest, and this shift often leads teams to directly compare tools. However, the more productive view is one of collaboration. Each tool addresses different parts of the AppSec lifecycle, and together they hint at a future where automation and governance reinforce one another rather than compete for the same ground.

Consider a workflow where Evo acts as the intelligence layer, continuously uncovering vulnerabilities, classifying them by risk, and enforcing policy across code, models, agents, and pipelines. Within the same lifecycle, Aardvark becomes the action layer, generating patches or pull requests as soon as an issue is identified. From there, Evo steps back in to confirm that fixes align with organizational policy, verify compliance, track exceptions, and maintain the governance structure enterprises rely on.

Together, they create a workflow that’s fast and reliable. Developers gain the benefit of automated remediation without losing the stability and oversight that come from mature, enterprise-ready workflows. Agentic tools handle the repetitive work. Snyk ensures the outcomes are consistent, traceable, and aligned with real-world constraints.

Over time, these systems could even strengthen one another. Feedback loops between Evo’s policy intelligence and an agent’s remediation output can make both smarter, more precise, and better attuned to how teams actually build software. It’s not a future defined by replacement. It’s one shaped by cooperation, where automation accelerates the work and platforms like Snyk keep everything grounded in trust and control.

Evolving security through collaboration

Agentic tools like Aardvark represent a meaningful step forward, but they’re not a replacement for the structure and stability that enterprise AppSec demands. Discovery, governance, validation, and deep pipeline integration remain the foundation of any effective security program, and those are areas where Snyk continues to lead. Evo ensures teams always understand their risk, receive consistent findings, and stay in control across code, models, agents, and workflows.

While Aardvark signals the growing focus on AI-driven, commit-level security, Evo leverages Snyk’s decades of research and enterprise-tested engines to provide a broad, platform-wide view, covering code, models, agents, and workflows. Over time, the two approaches can complement each other: Aardvark accelerating localized remediation, and Evo ensuring consistent, policy-driven governance and real-time protection across the full software lifecycle. Together, they form a more comprehensive lifecycle, one where AI amplifies developers rather than replacing them, and where automation seamlessly integrates into the processes that organizations already trust.

As agentic capabilities evolve, success will depend on ecosystem integration, developer adoption, and rigorous validation. Snyk’s maturity, real-time protection, and policy-driven intelligence provide the stability needed to support that next wave of innovation, creating a future where security moves faster, works smarter, and stays aligned with how modern software is built.

Want to learn more? Explore how Evo by strengthens security for AI-native Applications and Tools.