AI · alternatives · 2026
24 alternatives to Agent-101-safety-layer
A practical guide to building safety layers for AI agents
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Venn.ai▲336Delegate real work to AI agents with safety guardrails
Mar 2026 · venn.ai · its alternatives →
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com · its alternatives →
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com · its alternatives →
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I built a skill library for OpenClaw (always-on AI agent runtime, not session-based) where the agent can teach itself new behaviors during normal conversation. The idea: you tell your agent "every time I ask for a code review, always check for security issues first." It invokes a create-skill skill, writes a new SKILL.md, and that behavior is live immediately — no restart, no config change, no developer required. What I think is actually useful (the safety cluster): • loop-circuit-breaker: OpenClaw retries ALL errors identically. This halts on the 2nd identical failure before it burns your…
Mar 2026 · github.com · its alternatives →
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24 agent skills, each distilled from one concrete engineering failure — short, self-contained rule sets an AI coding agent (or a human) can apply without the original context.
Aug 2026 · whetstone.akbarsha.dev · its alternatives →
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Hi HN, We’re building security tooling around agentic AI systems. Today, we're releasing our public MCP catalog with detailed risk analysis for every MCP server we've found on the internet: https://mcp.armor1.ai/mcp-directory We all love agents and the power that MCPs unlock: suddenly your AI assistant can query databases, manage files, call APIs, and interact with the real world. But when we started adopting MCPs ourselves, we kept running into the same nagging questions: Is this MCP safe? Where is my data actually going? Could it execute destructive actions? Is it…
Feb 2026 · mcp.armor1.ai · its alternatives →
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As a Security Engineer with over 10+ years in industry, I kept running into the same problem with coding agents: Instructions are not guarantees. I put guidance in `CLAUDE.md`, `AGENTS.md`, memory files, MCP descriptions, and tool documentation. I explicitly told the agent things like: - Use the code graph for architecture questions instead of grepping the repository. - Do not use deprecated APIs or Unsafe code. - Prefer specific tools for specific tasks. The agent would still ignore those instructions surprisingly often. It would grep the entire repo, use deprecated APIs, or choose a slower…
Apr 2026 · sigmashake.com · its alternatives →
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5 tiny npm libs to stop AI agents misbehaving in prd
May 2026 · npmjs.com · its alternatives →
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Hi HN, Over the past two years I’ve built and debugged a fair number of production pipelines—mainly retrieval‑augmented generation stacks, agent frameworks, and multi‑step reasoning services. A pattern emerged: most incidents weren’t outright crashes, but silent structural faults that slowly compromised relevance, accuracy, or stability. I began logging every recurring fault in a shared notebook. Colleagues started using the list for post‑mortems, so I turned it into a small public reference: 16 distinct failure modes (semantic drift after chunking, embedding/meaning mismatches,…
2025 · github.com · its alternatives →
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7 layers between your AI agents and disaster.
Feb 2026 · takeinterest.ai · its alternatives →
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I built a collection of webhook skills because AI coding agents are surprisingly bad at webhook integrations. The generated code looks reasonable until you run it, then signature verification fails, raw body handling is wrong, or the middleware order breaks everything. PostHog's research on LLM code generation (https://posthog.com/blog/correct-llm-code-generation) found that agents produce more reliable code when referencing known-working examples rather than reconstructing from training data. That's the approach here. `webhook-skills` is a collection of provider-specific…
Feb 2026 · github.com · its alternatives →
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Your AI validates bad decisions. These tools challenge them.
Jun 2026 · reasoning.services · its alternatives →
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Certus▲7Liability insurance for AI agents. One line of code
Mar 2026 · getcertus.org · its alternatives →
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500+ algorithmic-trading skills for AI coding agents in the agentskills.io format. Every skill ships a working Python reference implementation and its own tests - 20,291 in CI. Broker APIs, backtesting, risk, execution, ML, compliance and 10 more domains. Apache-2.0 - HimanshuJ16/Algo-Trading-Skills
Sep 2026 · github.com · its alternatives →
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AI agents now have impressive reasoning capabilities. This raises an important question: how dangerous are these AI agents at identifying & exploiting web vulnerabilities? We created CVE-bench to find out (I'm one contributor of 16). To our knowledge CVE-bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0). To properly evaluate agents’ attacks, we built isolated environments with containerization and identified 8 common attack…
2025 · github.com · its alternatives →
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Project AI agents from unsafe actions before they execute.
Aug 2026 · agentfirewall.launchreadyal.com · its alternatives →
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev · its alternatives →
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The open standard for High-Density AI coding instructions
Feb 2026 · npmjs.com · its alternatives →
- 23
Attack-test your AI agents and grade what they did
Aug 2026 · tryredlineai.co · its alternatives →
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Also compare
- Venn.ai alternatives
- Deep Work Plan alternatives
- I built a firewall for agents because prompt engineering isn't security alternatives
- OpenClaw-superpowers – Self-modifying skill library for OpenClaw agents alternatives
- Whetstone – 20 Claude Code skills, each distilled from one real failure alternatives
- Risk Analysis Database of Every MCP Server alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →