AEVS: Proof-of-Execution for AI Agents
AEVS is a drop-in SDK that records every AI agent tool call and generates tamper-evident execution receipts, enabling teams to verify what an agent actually executed without relying on chat history or fragile logs.
AEVS: proof-of-execution for AI agents | Product Hunt
AEVS
Launching today
proof-of-execution for AI agents
39 followers
proof-of-execution for AI agents
39 followers
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Engineering & Development
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AI Agents
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.
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Launch tags:Developer Tools•Artificial Intelligence•SDK
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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 🙌
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4d ago
One thing we're particularly excited about is how easy AEVS is to adopt.
✅ ~2 lines of code to integrate
✅ Supports LangChain, LangGraph, and MCP today
✅ CrewAI support coming soon
You can even tell your coding agent:
Read https://aevs.fetch.ai/llms.txt and add AEVS to my agent
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.
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1h ago
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.
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12m ago