Dor Sharoni
Founder of Pruvz, the business evidence layer for AI agents: independent verification of high-impact agent actions against the systems of record a business already trusts.
Background
Dor Sharoni is a senior backend engineer with experience building production AI, real-time processing, and enterprise systems at scale. Building production AI and enterprise integrations highlighted a recurring gap: teams could see that an agent invoked a tool, but often lacked independent evidence that the intended outcome appeared in the system of record. He founded Pruvz to make high-impact AI agent outcomes independently verifiable against systems of record.
Why Pruvz
Pruvz is the answer to that gap: an evidence layer that treats the systems of record, not the agent's own report, as the source of truth. Every high-impact action becomes a verified business record, from the decision-time context and policy snapshot through the executed action to the outcome the systems of record confirm, with mismatches routed to human review. The full verification flow runs end to end in the product demo, and the founding design-partner program is open to 3-5 teams.
Connect
Find Dor on LinkedIn, follow Pruvz on LinkedIn, or write to hello@pruvz.ai. Bringing an agent workflow of your own? Book a design-partner call.
Writing and research
Guides by Dor Sharoni on AI agent outcome verification, business evidence, audit trails, and human review, plus the AI discoverability benchmark: first-party research, with a downloadable dataset, on how AI answer engines discover and describe an early-stage product.
Policy Snapshots: Proving Which Rules Your AI Agent Followed
A policy snapshot is the preserved, decision-time copy of the rules an AI agent followed. What a complete snapshot captures and why versioning alone is not enough.
Read the guideHow to Verify AI Agent Actions Against Systems of Record
A practical guide to AI agent outcome verification: define the expected outcome, read the result back from the system of record, handle eventual consistency, and classify every consequential action, without ever re-executing it.
Read the guideHuman Review of AI Agent Decisions: Beyond the Rubber Stamp
Why reviewing every AI agent action fails, what the EU AI Act and GDPR expect from human oversight, and how exception-based review backed by complete evidence makes each review count.
Read the guideThe Best AI Agent Audit Trail Tools in 2026
A buyer's guide to AI agent audit trail tools in 2026: the four categories (observability, governance, audit logging, and business evidence), representative tools in each, and how to match one to the decision you need to defend.
Read the guideWhat Is Business Evidence for AI Agents?
Business evidence is the verified, action-level record that proves an AI agent action followed the policy in force at decision time and that the outcome really occurred in the system of record.
Read the guideAI Agent Governance vs. Business Evidence: Policy Is Not Proof
AI agent governance defines the rules and controls for your agents. Business evidence shows whether each consequential action followed them and verifies the outcome against your systems of record. The difference, and why a governed program still needs action-level proof.
Read the guideAI Agent Audit Trail: What to Capture, and Why It Is Not Enough
An AI agent audit trail shows what an agent did. Learn the components a complete record must capture, and why a successful API call is not a verified business outcome.
Read the guideAgent Observability vs. Business Evidence: What's the Difference?
Agent observability shows how an AI agent ran. Business evidence proves what it did and verifies the outcome against your systems of record. The difference, and why technical traces alone cannot verify consequential agent actions.
Read the guide