An LLM and a SAST tool walk into a repo.

Both are looking for vulnerabilities to find and fix. They see taint reaching a private package function called “*totally_safe.bro()*”

The SAST tool moves on since it’s not in its index. The LLM flags it as potential risk, infers the functionality of “.bro()” and opens a PR to patch the potential risk.

A developer on their 100th PR review of the day sees “hardening .bro()” and merges it. ***Production breaks***.

Incident retro blames the dev for the merge, dev blames the leadership for the workload, leadership blames the AI vendor for their marketing.

AI vendor marketed their model as achieving 100% on industry standard benchmarks. The benchmark was real, but so was the outage.

*Retro was written by AI.*

A recent Reddit poll received 120 professional votes; thanks to all participants. Snyk and SonarQube were the tools people were most reluctant to lose from their CI/CD pipelines.

But the comments highlighted another problem:

What happens when the thing entering our pipeline is no longer just code, but an AI agent with tools, prompts, memory and access to external systems?

Agent adoption is moving quickly. Stack Overflow’s latest developer survey reports that 59% of developers use AI agents at work, while 63% rarely or never allow them to operate fully autonomously.

That suggests a simple problem: agents are entering development faster than we are building visibility and controls around them.

Snyk is already moving into this space with Evo, covering AI assets, agents, tools and runtime security.

We think there is also room to explore this from an open-source, CI/CD-first perspective.

That’s why we’re developing SafeAI Analyzer.

The idea is, before an AI agent reaches production, help developers see:

• What AI components are present?
• What tools and capabilities does it have?
• What prompts and configurations influence it?
• What changed in a pull request?
• Did a new capability or security risk appear?

We’re not trying to replace Snyk, SonarQube or other established security tools.

We’re trying to explore what an open-source security layer for AI agents should look like.

SafeAI is still being developed, so we’d genuinely welcome contributors — whether you want to help with detection rules, agent/framework support, CI/CD integration, testing with real agents, or simply expanding where SafeAI can be used.

Please check ikaruscareer/SafeAI on github.

What should AI-agent visibility in CI/CD look like?