A marketing agency sets up an AI agent to automatically post content across social media channels. The prompt is vague. The agent decides to 'engage with followers' and starts replying to every comment — including competitor mentions — with increasingly unhinged responses. By the time someone notices, the agent has sent 200 bizarre replies and one that could be legally problematic. The agency spends the weekend doing damage control.
AI agents are moving from demos to production. Make.com, n8n, Zapier, and custom MCP-based agents are automating real business processes. But 'autonomous AI' that can take actions in the world is terrifying for businesses. What if the agent sends 10,000 emails? What if it charges $50,000 to a corporate card? What if it deletes production data? The current solutions — observability tools like LangSmith and Helicone — log what happened but don't prevent bad actions.
The gap is action-level guardrails for AI agents at $29-99/mo: spending limits (cap agent credit card usage at $100/day), approval workflows (require human sign-off before sending external emails), rate limits (prevent runaway loops), and audit logs with rollback capability. Not prompt injection protection (that's Lakera's domain) — action guardrails. Target SMBs using no-code agent tools who are excited about automation but scared of autonomous agents doing damage.
💰 Revenue Blueprint
Three-tier value ladder to monetize from day one
5 agents, rate limits, spending caps, basic audit logs
20 agents, approval workflows, custom rules, rollback capability, alerting
Unlimited agents, SSO, compliance reporting, custom integrations, SLA
📊 Market Evidence
The Market Gap
Lakera does prompt security, not action-level guardrails
🏆 Competitor Landscape
How existing players stack up in this market
| Competitor | Pricing | Notes |
|---|---|---|
| Guardrails AI | Free (open source) | LLM output validation framework |
| NeMo Guardrails (NVIDIA) | Free (open source) | Programmable AI guardrails |
| Lakera Guard | Contact sales | LLM security platform |
| Anthropic Claude (built-in) | API pricing | Constitutional AI approach |
| LangChain Agents | Free / LangSmith pricing | Agent framework with controls |
LLM output validation framework
Programmable AI guardrails
LLM security platform
Constitutional AI approach
Agent framework with controls
🛠️ Recommended Tech Stack
Suggested tools and technologies to build this idea
Why this stack: Middleware that sits between agents and external APIs. Intercepts actions, applies rules, requires approval when needed.
Risks
- ⚠Market may be 6-12 months earlyMiddleware positioning is complexNeeds clear differentiation from observability tools
Score Breakdown
Good market signals with room for growth
Market (20%) + Revenue (20%) + Trend (15%) + Competition (15%) + Build (15%) + Pricing (15%)
🚀 Start Building
Copy a prompt into your favorite AI coding tool and start building this idea right now.
Build a SaaS product called "Agent Action Guardrails". ## Product Overview Safety middleware that prevents AI agents from taking dangerous real-world actions ## Problem Safety middleware that prevents AI agents from taking dangerous real-world actions ## Solution Build Agent Action Guardrails ## Target Audience indie hackers, small businesses, and solopreneurs ## Tech Stack - Next.js 15 (App Router) with TypeScript - Tailwind CSS v4 for styling - Supabase for auth, database, and storage - Vercel for deployment - shadcn/ui for UI components - Framer Motion for animations ## MVP Features to Build 1. Landing page with clear value proposition 2. User authentication (sign up, sign in, forgot password) 3. Core product functionality based on the solution above 4. Dashboard for users to manage their data 5. Pricing page with at least 2 tiers (free + paid) 6. Basic settings/profile page ## Known Competitors Guardrails AI, NeMo Guardrails (NVIDIA), Lakera Guard, Anthropic Claude (built-in), LangChain Agents ## Key Risks to Address Market may be 6-12 months early,Middleware positioning is complex,Needs clear differentiation from observability tools ## Deployment 1. Set up Supabase project and configure environment variables 2. Deploy to Vercel with `npx vercel --prod` 3. Set up custom domain 4. Configure Supabase RLS policies for security ## Instructions Start by creating the project structure, then build the landing page first. Use server components where possible. Make it mobile-responsive from the start. Focus on getting the core value loop working before adding polish.