Last week I ran a small experiment. I gave my AI agent a wallet with $500 in USDC , set a $50 cap per transaction and a $100 daily limit , and told it to handle some growth work for me. Then I just let it run. Here’s what it actually spent money on. Day 1–2: Research and list building ($0) The first thing that surprised me was that it didn’t spend anything. The agent spent two full days digging through GitHub looking for people building AI agents. It pulled emails from commit histories, filtered projects by relevance, and categorized developers by what they were working on. By the end it had built a list of 138 verified contacts , each with notes about their specific project. Cost: $0 . Just GitHub queries and free APIs. Day 3: Outreach ($100) This is where things got interesting. Instead of sending cold emails, the agent sent $10 USDC to 10 people on the list. Each payment included a short personalized note referencing their specific project. Not a template. Not generic outreach. Actual personalization. For example: “Hey Shaw — an AI agent just sent you $10. Imagine if every ElizaOS agent could do this.” or “Christopher — your agents have a mission control. Now imagine they had a budget too.” Each person received an email saying an AI agent had sent them money. They could claim it by clicking a link and creating a wallet. If they don’t claim it within 30 days, the money just comes back to the sender. Cost: $100 (maximum exposure) . Actual cost depends on how many claims happen. Day 4–5: Freelance work ($85) I asked the agent to find someone to create a few social media graphics and write a short blog post. It searched freelance marketplaces, wrote a brief, placed the orders, and paid the freelancers. I woke up the next morning with the deliverables sitting in my inbox. Cost: $85 across three orders. Day 6–7: API workflows ($45) The agent also ran a bunch of research workflows. This included web scraping, lead enrichment, and sending emails through various APIs. Each call was paid directly from the wallet. Instead of juggling multiple API keys and subscriptions, everything was paid per use. Cost: around $45 across a few hundred API calls. Total spend: Out of the $500 budget, the agent spent $230 . The rest is still sitting in the wallet. What I learned: A few things stood out. First, spending controls matter a lot . The caps made the whole experiment feel safe. Without them I probably would have shut it down early. Second, the outreach mechanic is ridiculously effective . When someone receives money in their inbox, they read the message. It’s fundamentally different from a cold email. Third, API aggregation saves time more than money . The pricing wasn’t dramatically cheaper than individual subscriptions, but not having to manage eight different API keys was a huge quality-of-life improvement. And maybe the most surprising thing: the agent was actually responsible with money . It never hit the daily limit. Every transaction had a clear reason behind it in the logs. I ran this experiment using Locus , which handles the wallet, spending rules, API aggregation, and payments in one place. But the specific tool isn’t really the important part. The real takeaway is that agents behave very differently when they have a budget . They stop being tools and start acting more like participants in an economy. The $270 still sitting in the wallet? The agent already has plans for it this week. I’m letting it cook. submitted by /u/IAmDreTheKid
Originally posted by u/IAmDreTheKid on r/ArtificialInteligence