AI Partner

An AI workforce for your whole team

5 followers

An AI workforce for your whole team

5 followers

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Productivity

•

AI Agents

Goal in, deliverable out. AI Partner decomposes work into steps, runs research and synthesis, executes tools, validates results, and returns finished files. joins your video meetings as your assistant, helps you plan long term goals and achieve them.

Launch tags:Productivity•Artificial Intelligence•Maker Tools

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Framer AI AgentsDesign and publish professional sites with AI

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Maker

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👋 Hi Product Hunt! I'm Amit, the creator of AI Partner. Thank you for checking us out. Over the past two years, I've used almost every major AI tool available. They were all incredibly useful—but I kept running into the same limitation. They answer questions. They don't own work. If I asked: "Research our top five competitors and prepare a presentation." Most AI tools would give me text. I'd still have to: search the web compare sources organize findings generate slides review everything fix mistakes export the final files The AI helped, but I was still the project manager. So I started building AI Partner around a different idea. Not a chatbot. A partner. Instead of asking it one question at a time, you give it a goal. For example: "Research our top five competitors and produce a PowerPoint." AI Partner will: Break the goal into executable tasks Research across the web Use real tools and MCP servers Analyze and synthesize information Create documents, spreadsheets, presentations, reports, or code Validate its own outputs Retry when something fails Deliver finished files But work doesn't stop there. AI Partner can also join your Teams meetings as your AI assistant, listen to the conversation, answer questions in real time when asked, capture decisions and action items, remember the meeting context, and continue working after the meeting ends—whether that's writing documentation, creating follow-up tasks, generating presentations, or researching topics that came up during the discussion. The vision is simple: An AI workforce for your whole team. Instead of one AI waiting for prompts, imagine a team of AI specialists that can research, analyze, write, automate, attend meetings, collaborate, and execute real business work from start to finish. Some of the capabilities available today include: Long-term memory across projects and conversations 160+ built-in tools 41+ MCP server integrations Browser automation Docker sandboxed execution Deep research Scheduled and proactive execution Self-hosted deployment for organizations that need complete control over their data This is still early, and there is a lot we want to improve. The feedback I'm looking for isn't "Is this cool?" I'd genuinely love to know: What business task would you trust an AI workforce to own? What would stop you from using AI Partner today? If AI Partner could join one of your meetings tomorrow, what would you expect it to do? I'll be here throughout the day answering every question, discussing the architecture, taking feature requests, and talking about where we're heading next. Thank you for checking out AI Partner. I'm excited to hear your feedback.

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1mo ago

Maker

"Loop engineering" is the emerging term for a real shift in how agentic AI gets built: the observation that an agent's quality comes less from the model or the prompt, and more from the engineering of the loop wrapped around the model — what enters each iteration, how results and errors feed back, when it stops, how it verifies, and how it recovers. Prompt engineering tunes one call; loop engineering designs the system of calls: perceive → decide → act → observe → update state → repeat, with budgets, gates, and recovery around it.

The canonical pillars people mean by it:

1. THE LOOP ITSELF      reason → act (tool) → observe → update → repeat
2. CONTEXT ENGINEERING  what the model SEES each turn (trim, pin, summarize)
3. FEEDBACK CHANNELS    errors/validation injected back as course-correction
4. VERIFICATION         never trust the model's "done" — external judges/gates
5. TERMINATION          success criteria, step/time/cost budgets, stuck detection
6. RECOVERY             retry, replan, fallback, self-correction, salvage
7. STATE & RESUME       checkpoints, persistence, crash recovery

AI Partner is not just built on this principle — it's one of the more complete implementations of it I've seen.

Answered by Anthropic's Fable 5

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1mo ago

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1. THE LOOP ITSELF reason → act (tool) → observe → update → repeat 2. CONTEXT ENGINEERING what the model SEES each turn (trim, pin, summarize) 3. FEEDBACK CHANNELS errors/validation injected back as course-correction 4. VERIFICATION never trust the model's "done" — external judges/gates 5. TERMINATION success criteria, step/time/cost budgets, stuck detection 6. RECOVERY retry, replan, fallback, self-correction, salvage 7. STATE & RESUME checkpoints, persistence, crash recovery