AI tool comparison
ElevenAgents Guardrails 2.0 vs Shannon
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Safety & Governance
ElevenAgents Guardrails 2.0
Real-time safety controls for voice agents — stop drift, injection, and off-brand behavior
75%
Panel ship
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Community
Free
Entry
ElevenAgents Guardrails 2.0 is a safety layer built on top of ElevenLabs' voice agent platform, designed for enterprises deploying customer-facing AI voice agents at scale. The core problem it solves: voice agents in production tend to drift, get manipulated through prompt injection, or go off-brand in ways that only surface after something embarrassing happens. Version 2.0 adds three main capabilities: real-time policy enforcement that monitors agent behavior as it happens, prompt injection protection against users trying to manipulate the agent's instructions, and configurable custom rules that enterprises can tailor to their specific compliance or brand requirements. Unlike static guardrails baked into the system prompt, these operate as a live enforcement layer during conversations. The timing matters. As more enterprises put voice agents on their phone lines and websites, the "what could go wrong" list has gotten longer — agents giving wrong pricing, going off-script with sensitive customers, or being jailbroken into saying things they shouldn't. Guardrails 2.0 positions ElevenLabs not just as a voice synthesis platform but as an enterprise-safe agent runtime.
AI Security
Shannon
Autonomous AI pentester that proves exploits, not just finds them
75%
Panel ship
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Community
Paid
Entry
Shannon is an autonomous AI security testing agent that does what most scanners can't: it actually proves vulnerabilities are real before reporting them. Built by Keygraph, it analyzes your source code and API endpoints, identifies attack surfaces, and then autonomously executes live exploits — SQL injection, XSS, SSRF, authentication bypasses, and more. The key differentiator is evidence-first reporting: Shannon won't flag a potential SQL injection unless it can demonstrate the exploit working in your environment. Under the hood, Shannon uses Claude to reason about code structure and attack chains, combining static analysis with dynamic exploitation in a feedback loop. It maps the application graph, selects attack strategies based on code patterns, attempts the exploit, and reports only confirmed vulnerabilities with full reproduction steps. It runs locally and can be pointed at any web app or API. The timing is pointed: AI coding assistants are shipping code faster than teams can review it for security. Shannon was born from that gap — an AI to check the work of other AIs. At ~$40-55 in API credits per full scan, it's priced for startups who can't afford a dedicated security team but can't afford a breach either. The AGPL open-source release makes it accessible to indie developers and security researchers.
Reviewer scorecard
“Static system prompt guardrails are a band-aid. Having a live enforcement layer that can catch drift and injection attempts as they happen is the right architecture for anything customer-facing. This is the kind of tooling that makes it reasonable to deploy voice agents in sensitive contexts like healthcare or finance.”
“This solves a real problem I face constantly: AI-generated code shipping faster than security reviews can keep up. Shannon catches what static linters miss because it actually runs the exploit — that's a fundamentally different class of tool. At ~$50 per scan it's cheaper than one hour of a security consultant's time.”
“Guardrails as a paid add-on to your voice agent platform is a strange model — safety shouldn't be upsold. Also, ElevenLabs controlling both the voice synthesis and the safety layer means there's no independent verification that the guardrails are actually working. That's a dangerous single point of trust for enterprise compliance purposes.”
“Every 'autonomous pentester' of the past decade has promised to replace human red teamers and delivered glorified CVE scanners. The AGPL license is also a poison pill for enterprise teams who need commercial contracts before running anything against production. Wait for a version with a proper SaaS tier and audit trail.”
“Voice agents are the new customer service reps, and companies are learning the hard way that they need guardrails. This is the beginning of a whole category: real-time behavioral safety systems for AI agents. The team that solves this at scale — across providers, not just ElevenLabs — will be enormous.”
“We're entering an era where AI writes code and AI breaks code — Shannon is the first credible entry in the adversarial AI category for developers. The agentic loop of analyze-exploit-verify is the right architecture. This becomes infrastructure-grade once it integrates into CI/CD pipelines as a mandatory gate.”
“Brand safety for voice is genuinely underserved. Written AI outputs can be reviewed and filtered; voice interactions happen in real time with no undo. Knowing your agent won't say something off-brand to a live customer is worth paying for, especially for high-volume contact centers.”
“As someone who builds web tools and can't afford a dedicated security team, Shannon feels like a genuine safety net. The output is human-readable with full reproduction steps — not a wall of CVE numbers I have to decode. Exactly what indie builders need.”
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