MVP Spec Template for AI Agent: Ready-to-Use and Real Example
This page gives you a programmatic, real-world MVP Spec template for an AI agent, plus a detailed, filled example of an email triage bot. You'll see what 'good' looks like, measurable KPIs, and real feature lists. Follow detailed steps or generate a custom version with MakeMyPRD.
What this is
An MVP Spec template for AI Agent is a structured requirements document outlining the minimal feature set, flow, benchmarks, and technical stack for launching a functional AI agent. It defines what to build, how to measure success, and which tools to use. In modern stacks, PMs often specify integrations with tools like Supabase for cloud storage, Next.js for the frontend, and Claude Code or OpenAI Codex as LLM providers. This doc is used to align PMs, designers, and engineers, clarify scope, and cut dev time—ideal for teams shipping with Replit or Vercel.
Compared to alternatives
| Option | Best for | Trade-off |
|---|---|---|
| Google Docs PRD | Legacy teams needing freeform docs | Slow to update; lacks structure for AI workflows |
| Notion Spec Template | Teams living in Notion, fast collaboration | Unstructured for code; tough to extract ready-to-build requirements |
| MakeMyPRD | Programmatic spec generation, export to code tools | Template-driven, less freeform for unusual products |
| Lovable project template | Building AI agents with Supabase & React in days | Opinionated on tech stack; not great for non-React flows |
| Cursor structured PRD | AI agent teams deploying fast with Cursor AI IDE | Some learning curve, focused on dev handoff over PM process |
A real example
MVP Spec: SmartMail Triage AI Agent
Goal: Reduce average email triage time for customer support teams by 40% within 3 weeks of deployment.
Target User: Customer support agents at e-commerce companies handling 1000+ emails per week.
Core Features:
- Email Ingestion: Connects to Gmail and Outlook using OAuth; fetch batch of up to 5000 emails/hr via API.
- Classification: Labels each email as 'Order Issue', 'Return', 'Spam', or 'Other' using Claude Code (Anthropic) v3.
- Priority Tagging: Assigns Urgent/Non-Urgent status based on entity extraction (customer sentiment, keywords).
- Draft Suggested Reply: Generates draft response using Codex, inserts into response box. Requires analyst review pre-send.
- Dashboard: Web dashboard (Next.js, deployed to Vercel) with metrics, retrain triggers, API logs.
- Data Store: Stores labeled emails, feedback, and response times in Supabase.
Out of Scope:
- Multi-language support (English only)
- End-user (customer-facing) email writing
KPIs:
- 40% reduction in triage time (measured via dashboard; baseline = 4min/email)
- 90%+ classification accuracy after 2 weeks
- <5% draft rejection rate by support analysts
Design Constraints:
- UI must load in <1.5s cold start (tracked via Vercel analytics)
- GDPR compliance
Stack: Claude Code for LLM-powered labeling; Codex for reply draft; Next.js+Vercel frontend; Supabase db.
Milestones:
- Day 0: Ingestion & manual labeling demo (2 devs, 3 days)
- Day 8: LLM classification live
- Day 14: Draft reply gen + analyst review
- Day 21: Dashboard + end-to-end test
Risks:
- LLM hallucination risk → restrict reply template set
- Email provider API limits
How to use this
- Identify core user and flow: Define your AI agent's first user segment and the absolute minimal journey. For example, customer support triage or sales lead scoring.
- Choose stack and APIs: Select which LLMs (Claude Code, Codex), frameworks (Next.js, Supabase), and integration points you'll use. List each tool explicitly in the spec.
- Define features tightly: Limit v1 to 3–5 clear, atomic features: input/output, LLM task, review point. Note what you are NOT building.
- Set measurable KPIs: Assign specific, time-bound outcomes (e.g., 'reduce triage time 40% in 3 weeks,' '95% accuracy') and explain how you'll track—Supabase or custom dashboard?
- Document design and privacy constraints: Capture frontend load targets, PII handling, and which non-goals matter (e.g., accessibility, latency objective).
- Hand off for build with real example: Export or share the filled spec directly to your team, ready for dev kickoff or AI-first code pair tools like Cursor or Replit.
FAQ
What tools do I need to ship an MVP AI agent?
You'll want at least one LLM provider (Claude Code, Codex), a storage layer (Supabase works well), and a deployment solution like Vercel or Replit. Frontend can be built rapidly on Next.js. For programmatic specs, MakeMyPRD speeds up planning.
How do I write a PRD for an AI agent, not just a regular SaaS?
Use a focused MVP spec template: define what the AI does, which APIs and tools will handle what, set measurable KPIs, and call out what the agent won’t do. Check out our filled SmartMail example above or see more in our PRD template hub.
Can I use this template for chatbot, Chrome extension, or internal tools?
Yes. Start from the AI agent MVP template, but adjust feature scope and KPIs for your use case. For examples, see our templates for PRD for Chrome Extension and PRD template for internal tool.
What are common mistakes when scoping v1 for AI agents?
Scoping too wide (multi-language, too many use cases), failing to commit to a specific stack (e.g., not naming Codex or Claude), and not writing down measurable KPIs. Your devs need clarity and tradeoffs, not generalities.
How do I get a custom MVP spec for my AI agent idea?
Use MakeMyPRD. Answer questions about user, problem, stack, and goals. You'll get a programmatic, ready-to-export PRD or MVP spec draft tailored to your product.