Mandate – treating AI agents like economic actors, not scripts

Hacker News

Hi HN, I’ve been working on a small MVP called Mandate. The idea is simple: AI agents can spend money and call tools, but today we mostly control them with prompts and conventions. I wanted something closer to IAM / firewall thinking, but for agents. Mandate enforces authority at runtime, outside the LLM. Core concepts: - Agent = stable identity (not a process) - Policy = static, versioned authority template - Rules = select policies based on invocation context (env, user tier, etc.) - Mandate = short-lived authority issued per invocation - Enforcement = deterministic allow/block of tool + LLM calls This lets you: - cap spend per invocation or over time - restrict tools and MCP servers - kill an agent instantly - audit every decision with reason codes No prompt tricks, no AI judgment — just mechanical enforcement. Repo (very early MVP): https://github.com/kashaf12/mandate I’m not sure yet if this is something teams actually want, or if it’s too early / overkill. I’d really value feedback from people running agents or automation in production: - Have you hit failures where prompts weren’t enough? - Do you already enforce hard limits internally? - What would make this useful vs annoying? Thanks for reading.

Visit product View on Hacker NewsDec 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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