Introducing the New Agentic Architecture for Snyk Agent Fix: Faster, Smarter, and More Secure

The bottleneck in security has officially moved from “finding issues” to “actually fixing them”. Snyk has led this transformation before, helping organizations embrace a true developer-first approach to security. The latest evolution of Snyk Agent Fix is the first of many enhancements designed to bridge the gap between AI speed and security. By fusing Snyk’s security intelligence with the power of leading frontier models, we are delivering fixes that are both more secure and more functional than any standalone model or previous iterations of Agent Fix could achieve. 

The next evolution of Snyk Agent Fix arrives May 26th, 2026. By moving forward with new models and a new agentic architecture, we’ve moved away from static fine-tuning to dynamic few-shot prompting, a system that can provide models with the most relevant security guidance in real time. This new approach improved performance across every metric and allows us to support all Snyk Code-supported languages, so our customers can harness the power of AI to remediate more issues faster across their entire codebase.

How we measure success: The three pillars of benchmarking

In the world of AI, good enough isn't enough for security. The new model performs significantly better across our rigorous three-tiered benchmarking process, ensuring suggestions are both safe and usable:

• Security integrity: We measure the model's ability to write code that is inherently free of vulnerabilities on the first try (Pass@1) or within five attempts (Pass@5). 

• Functional logic: Using LLM-based evaluation, we ensure the new code maintains functional parity with the original without introducing logical errors. 

• Golden tests: This is our source of truth. We use hundreds of real-world vulnerable snippets paired with unit tests to confirm the vulnerability is gone (FAIL to PASS) while the functional intent remains intact (PASS to PASS). 

From fine-tuning to dynamic few-shot prompting: Guiding commercial models with expertise

As LLM technology evolves, we've identified a new opportunity: moving from static fine-tuning to agentic architecture. The core advantage of Snyk Agent Fix over an out-of-the-box LLM lies in our proprietary security intelligence. Snyk maintains a database of over 35,000 real-world vulnerabilities from open source projects and fixes written by Snyk security experts. During prediction, we don’t just ask the model to guess a solution; we inject the prompt with the most relevant, real-world examples of how that specific CWE was previously resolved. 

This transforms the LLM from a generalist into a domain expert, guided by thousands of human-written data points at the exact moment of generation. By providing these high-quality, real-world examples for the CWE at the time of prompting, we achieved a significant increase in performance over both our previous model and leading commercial models. 

The performance leap

Our Golden Test Benchmarks yielded impressive results. The percentages below represent the pass rate for our suite of ~150 tests. Each evaluation consists of vulnerable code paired with two specific unit tests: one to verify the vulnerability is present and another to ensure the code remains functional. Because models never see these unit tests, they must rely on robust coding practices to satisfy both the security and functional requirements.

Our analysis found that by equipping frontier models from Anthropic with Snyk intelligence, they passed 14.48% more evaluations than the Anthropic or previous Agent Fix model alone. 

*Note that insecure answers are always filtered out and never shown to customers.

The power of agentic retries

One of the most significant hurdles in auto-fixing is LLM’s generating insecure code. Previously, if Agent Fix’s model generated an insecure fix, it was simply filtered out. This is the safest option, but it potentially leaves the developer with no suggestions at all.

Instead of discarding an imperfect output, the system now:

• Extracts the issue: Identifies exactly why the first suggestion failed.

• Feeds it back: Passes the error context back to the agent.

• Adapts the answer: The agent rethinks the problem and generates a corrected version, avoiding its previous mistake.

This loop ensures that, instead of getting “No fix available,” developers receive a high-quality, verified remediation that has already been stress-tested by our engine.

Full language coverage

Finally, this new approach allows us to quickly scale our language support. Previously, adding support for a new language or a complex security rule required thousands of manual training samples and a lengthy fine-tuning process.

By leveraging dynamic few-shot prompting and agentic reasoning, we now offer full rule and language coverage for every language supported by Snyk Code. From Java and Python to Apex and Go, the new Agent Fix is ready to remediate vulnerabilities across your entire stack without waiting for new support to roll out.

Experience the future of remediation

Snyk makes security seamless in the development workflow. Because our architecture is model-agnostic, we constantly evaluate leading models' performance against our benchmarks. This new approach is a giant leap toward a world where security debt is managed automatically and accurately. Stay tuned, we’re just getting started on redefining what it means to be secure at scale. 

Your feedback is critical as we continue to evolve these tools. If you have questions or input on these updates, please reach out to your Snyk account team or Snyk Support.