AI agents are rapidly moving from chat interfaces to real-world automation.
They can browse websites, execute workflows, approve actions, call APIs, generate content, and make decisions with increasing aut
onomy.
But here’s a question I don’t see discussed enough:
Can an AI agent prove what it actually did?
When something goes wrong weeks or months later, can we reliably answer:
- Which agent executed the action?
- Which model was used?
- What permissions did the agent have?
- Which policies were enforced?
- Was human approval required?
- Has the record
been modified since the decision was made?
Today, most systems rely on internal logs.
That works for debugging.
It becomes much harder when you need independent verification, auditing, compliance, customer disputes, or risk assessment.
I’ve been exploring an idea called AI Decision Receipts.
The concept is simple:
Every significant AI decision or action generates a cryptographically verifiable receipt containing metadata about the decision without exposing prompts, ou
tputs, documents, or sensitive business information.
The receipt can later be independently verified to prove:
- Who (or what) made the decision
- When it happened
- Under which permissions and policies
- Whether human review occurred
- Whether the record remains unchanged
I’m currently building an experimental platform around this concept called Signatrust.
My goal is not to replace existing agent frameworks, but to explore whether AI systems need a trust and accountability layer similar to how HTTPS, OAuth, and OpenAPI became foundational
layers for the web.
I’d be interested in feedback from developers working with:
- AI Agents
- LangChain
- CrewAI
- AutoGen
- OpenAI Agents SDK
- MCP
- Multi-agent systems
- AI infrastructure
Do you think AI Decision Receipts are a real problem worth solving, or would existing logging systems be sufficient?
https://signatrust.net