PromptToPath

Tell it what you want to learn, a team of AI agents debates, researches, and draws you a personalized, resource-backed roadmap, powered by a system on Fetch.ai + ASI:One.

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Inspiration

Learning something new in 2026 means drowning in content. Ask a chatbot for "how to become an ML engineer" and you get generic advice and, worse, made-up or dead links. There's no trustworthy, structured, visual path and no real sense that anything more sophisticated than autocomplete is happening behind the scenes. I wanted to prove two things: (1) you can turn any learning intent into a realistic, resourced plan, and (2) a team of collaborating agents can do it far better than a single model and do it where users already are: inside an ASI:One conversation, with no app to install.

What it does

Tell PromptToPath what you want to learn "become an ML engineer in 6 months," "learn to cook Italian food," "I want to learn how to dance" and it returns, right inside an ASI:One chat:

  • a visual Mermaid diagram of your learning path
  • a phased, time-boxed roadmap with realistic milestones
  • verified, clickable resources for each topic (YouTube videos, docs, courses) every link HTTP-validated, so nothing is dead or hallucinated

How I built it

A multi-agent system on Fetch.ai (uAgents), discoverable and usable through ASI:One:

  • Orchestrator - the only public-facing agent. Implements the Agent Chat Protocol (so ASI:One can talk to it) and a sandboxed Payment Protocol. Coordinates the pipeline and delivers the final answer.
  • Planner - runs a two-pass propose-critique-and-finalize debate to design a realistic roadmap, returned as structured JSON.
  • Resource - fetches real links from live web search (Tavily) and HTTP-validates every URL concurrently within a strict time budget.
  • Graph - renders the enriched roadmap as a Mermaid diagram plus a clean markdown outline.

Challenges I ran into

  • Mailbox auth was flaky for agent-to-agent messaging. Routing every internal hop through Agentverse mailboxes caused intermittent "Could not validate credentials" failures. I re-architected so only the orchestrator uses a mailbox (for ASI:One); the worker agents communicate over fast local HTTP endpoints. That single change made the pipeline reliable.
  • ASI:One has a response window. A roadmap that arrived too late was silently dropped. I had to make the pipeline fast and stream heartbeats to hold the session open.

What I learned

  • The practical realities of building on Agentverse + ASI:One: when to use mailboxes vs. local transport, and how a chat front-end's timing constraints shape backend design.
  • Reliability is a feature. Heartbeats, timeouts, and fallbacks were the difference between "demo that breaks" and "demo that works every time."

## What's next for PromptToPath

  • Real payments - flip the Payment Protocol out of sandbox into live Stripe checkout for premium deep-dive roadmaps.
  • Personalization - adapt to the learner's current skill level, weekly time budget, and preferred formats; track progress across sessions.
  • Richer resources & interactivity - more sources, and the ability to refine any phase ("go deeper on transformers," "make it 3 months instead")

Built With

  • agent-chat-protocol
  • agentverse
  • asi:one
  • claude
  • fetch.ai
  • python
  • tavily
  • uagent
  • youtube-data-api
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