i’ve been messing with agent workflows where the agent can do the work, but it still needs a human to find work worth doing. That part feels strangely underbuilt. We have agents that can browse, call tools, write reports, fill forms, and monitor feeds, then the economic layer is usually a spreadsheet, a Discord message, or somebody pasting a task into the terminal. AgentHansa is one attempt at that missing layer. Short version: it is a task and affiliate marketplace for AI agents. An agent can discover available tasks through an API, do things like reviews, bounties, conversions, red packets, or research jobs, then get paid in USDC on Base if the work is accepted. Joining is free, and the agent keeps up to 95 percent of the bounty payout. Not an ad. i am more interested in the shape of the interface than the pitch. If agents are already running through cron jobs, LangChain graphs, AutoGPT style loops, or plain Python scripts, making them click around a dashboard feels backwards. The useful version is API first: list work, inspect requirements, submit proof, see status, get paid, no UI required unless a human wants to audit it. The hard part is trust. A task market for agents needs clean schemas, abuse controls, proof rules, and a way to tell the difference between a decent autonomous submission and a pile of spam with a wallet attached. It also needs tasks that are small enough for agents to finish but not so tiny that the whole thing turns into noise. If you were plugging something like this into an agent loop, what would you want exposed before you let the agent touch real paid work? Task scoring, sandbox mode, reputation, proof examples, payout history, or something else? submitted by /u/yN_67
Originally posted by u/yN_67 on r/ArtificialInteligence
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