AEVS (Agent Execution Verification System) is a drop-in SDK that records every AI agent tool call and gives agents verifiable execution receipts. It captures the tool, inputs, outputs, status, and timing as tamper-evident proof, so teams can verify what an agent actually executed without relying on chat history or fragile logs.
We built AEVS at Fetch AI because as AI agents start doing real work, it’s becoming harder to answer a simple but important question:
What did the agent actually execute? 🤔
Chat history can tell you what an agent said. Logs can help, but they’re often scattered, incomplete, or easy to lose. For agents that call tools, trigger workflows, move data, or interact with external systems, teams need something stronger: a verifiable record of execution ✅
That’s what AEVS provides.
AEVS is a drop-in SDK that records every AI agent tool call as a tamper-evident execution receipt
Each receipt captures the tool used, inputs, outputs, status, and timing, so teams can verify what an agent actually did without relying only on chat history or fragile logs.
The goal is to make agent execution more trustworthy, auditable, and production-ready 🔐
It’s designed for builders working with agent frameworks like LangChain/LangGraph, MCP tools, and custom agent stacks 🛠️
You can add AEVS to your existing workflow and start generating verifiable receipts for tool calls without rebuilding your agent from scratch 🚀
We’re especially interested in use cases where agents perform actions that matter: customer support workflows, financial operations, internal tools, compliance-sensitive tasks, data pipelines, and API automation
I’d love feedback from agent builders, infra teams, and anyone thinking about trust and verification for AI systems 🙌
This hits a real trust gap for agents: chat history is not an execution log. Tamper-evident receipts for tool calls could become table stakes. Are receipts designed to be portable across agent frameworks, or mostly SDK-local at launch?
This resonates. I ship an MCP server for ad-account monitoring, and the audit log of every tool call (tool, sanitized params, latency, result) turned out to be the feature people trusted most, especially once agents could trigger actions on live accounts. The piece I'd add: receipts answer "what happened," but for actions that move money or change customer state, the bigger win is gating the write before it executes, not just proving it after. Receipt plus an approval step is the real combo. Are you thinking about a pre-execution hook, or is AEVS purely the record after the call fires?
Can AEVS verify why an agent made a decision, or only what action it executed?
The receipt model makes sense for agent workflows where a tool call can change customer state, like support ops or billing automation, and chat logs are not enough. Capturing inputs, outputs, status, and timing as a receipt feels useful, but the edge case I’d worry about is sensitive payloads. Can teams redact specific fields before the receipt is written while still keeping the proof tamper-evident?
Congrats on the launch! As agents become more autonomous, knowing what they actually did becomes just as important as what they said. The execution receipt concept is really interesting.
One thing we're particularly excited about is how easy AEVS is to adopt.
and it can help integrate AEVS directly into your workflow.
Our goal was simple: make verifiable agent execution accessible without forcing developers to redesign their existing stack.
About AEVS on Product Hunt
“proof-of-execution for AI agents”
AEVS launched on Product Hunt on June 15th, 2026 and earned 97 upvotes and 15 comments, placing #13 on the daily leaderboard. AEVS (Agent Execution Verification System) is a drop-in SDK that records every AI agent tool call and gives agents verifiable execution receipts. It captures the tool, inputs, outputs, status, and timing as tamper-evident proof, so teams can verify what an agent actually executed without relying on chat history or fragile logs.
AEVS was featured in Developer Tools (514k followers), Artificial Intelligence (471k followers) and SDK (773 followers) on Product Hunt. Together, these topics include over 172.6k products, making this a competitive space to launch in.
Who hunted AEVS?
AEVS was hunted by Mehul. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AEVS stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Mehul
Hey Product Hunt 👋
We built AEVS at Fetch AI because as AI agents start doing real work, it’s becoming harder to answer a simple but important question:
What did the agent actually execute? 🤔
Chat history can tell you what an agent said. Logs can help, but they’re often scattered, incomplete, or easy to lose. For agents that call tools, trigger workflows, move data, or interact with external systems, teams need something stronger: a verifiable record of execution ✅
That’s what AEVS provides.
AEVS is a drop-in SDK that records every AI agent tool call as a tamper-evident execution receipt
Each receipt captures the tool used, inputs, outputs, status, and timing, so teams can verify what an agent actually did without relying only on chat history or fragile logs.
The goal is to make agent execution more trustworthy, auditable, and production-ready 🔐
It’s designed for builders working with agent frameworks like LangChain/LangGraph, MCP tools, and custom agent stacks 🛠️
You can add AEVS to your existing workflow and start generating verifiable receipts for tool calls without rebuilding your agent from scratch 🚀
We’re especially interested in use cases where agents perform actions that matter: customer support workflows, financial operations, internal tools, compliance-sensitive tasks, data pipelines, and API automation
I’d love feedback from agent builders, infra teams, and anyone thinking about trust and verification for AI systems 🙌