AI tool comparison
Agent Armor vs Shannon
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Security
Agent Armor
Zero-trust Rust runtime that governs every AI agent action before it runs
75%
Panel ship
—
Community
Paid
Entry
Agent Armor is a lightweight governance layer for AI agents, written in Rust and designed to intercept every agent action before execution. It sits in front of LangChain, CrewAI, AutoGen, or Claude Code and runs each proposed action through an 8-stage decision pipeline: intent classification, credential leak scanning, rate limiting, resource scoping, behavioral fingerprinting, semantic deduplication, human-review escalation, and final allow/block. The project is MCP-aware and can intercept tool calls at the protocol level, which means it works regardless of which agent framework you're using. Actions that pass all 8 layers execute normally; those that fail can be automatically blocked, held for human review, or rewritten to a safer equivalent. A live dashboard shows agent activity, pending reviews, and anomaly alerts. Version 0.3.0 arrived as a Show HN today and hit the front page. The author, Edoardo Bambini, built it after a production incident where a coding agent attempted to overwrite git history on the main branch. The timing is good — as more teams ship agents to production, "what guardrails do I put between the agent and the real world?" is an increasingly urgent question.
Security
Shannon
Autonomous AI that finds your vulnerabilities and exploits them — for you
75%
Panel ship
—
Community
Free
Entry
Shannon is an autonomous AI security research agent from Keygraph that takes a target (web app, API, or codebase) and runs a full offensive security workflow: static analysis, attack surface mapping across OWASP Top 10, and then actual proof-of-concept exploit execution — all without manual intervention. It orchestrates real security tools (Nmap, Subfinder, SQLMap, Playwright) under the hood, not just generating reports. The Lite tier (AGPL-3.0) handles web apps and API endpoints, running browser automation and fuzzing attacks autonomously. Shannon Pro (commercial) adds SAST/SCA integration, CI/CD pipeline hooks for PR scanning, and team-level finding management. The model layer is pluggable — defaults to GPT-4o for planning with Claude Sonnet for exploit reasoning, but can be pointed at local models. What sets Shannon apart from tools like Burp Suite or ZAP is the agentic loop: it doesn't just surface a list of potential issues, it attempts exploitation and logs what worked. For small security teams and solo founders doing pre-launch security checks, this compresses days of pentesting work into a single automated run. The open-source Lite tier is the real news here — genuine autonomous exploitation capability, freely available.
Reviewer scorecard
“I've been looking for exactly this: a framework-agnostic safety layer I can drop in front of my agents without rewriting them. The credential leak scanning alone is worth the integration cost — agents have a bad habit of echoing secrets into tool calls.”
“I've been paying $400/month for a pentesting retainer for pre-launch checks. Shannon Lite ran against my staging environment and surfaced an actual SQLi vulnerability in 20 minutes that my last manual audit missed. The AGPL license means I can self-host it in my CI pipeline without worrying about data leaving my network.”
“An 8-stage pipeline on every agent action is a lot of latency overhead, especially for interactive agents. And sophisticated attackers will study the classifier patterns — once Agent Armor is widely deployed, the 8 stages become an adversarial target. This is good for basic hygiene, not a security guarantee.”
“Autonomous exploitation tools have serious dual-use liability. The AGPL license doesn't prevent anyone from running Shannon against systems they don't own — and AI-generated PoC exploits at this speed are a real threat multiplier for less-sophisticated attackers. I'd want to see proper authorization checks and rate limiting baked into the Lite tier before recommending this broadly.”
“The agent governance market will be worth more than the agent framework market within 3 years. As AI agents take real-world actions with real consequences, something has to sit between the model and the world. Agent Armor is an early but serious attempt at the right architecture.”
“Security tooling is going through the same shift coding did with Copilot — autonomous agents are going to make pentesting accessible to every small team that currently can't afford it. Shannon is an early version of what eventually becomes a background daemon watching your entire attack surface 24/7.”
“The dashboard is beautifully designed for a security tool — clear threat visualization, pending review queue, agent behavior timeline. I actually want to run this just to see what my agents are attempting even when nothing looks wrong.”
“Less relevant to my workflow directly, but I've started including 'ran Shannon against my portfolio site' in client pitches as a trust signal. The fact that indie creators can now point a professional-grade security tool at their own work without a $5K budget is a shift worth noting.”
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