TL;DR: Enterprises are connecting AI agents to the sensitive systems that matter most, starting from ERP. Legacy compliance standards are not built for non-deterministic agents. TrustAI verifies agents against your policies and builds a record of comprehensive evals for continuous compliance and audits.

Ask: If you want to verify an agent’s safety before deploying in your company or if you are building an agent that connects to private data from other enterprises, find us at hello@trytrust.ai and trytrust.ai.

Demo: https://youtu.be/ouPgUN9Ap8o

🤔 Why Now

AI agents just landed inside the systems that run the enterprise. SAP shipped Joule, NetSuite opened up to MCP, third-party vendors are wiring their own agents into your stack, and your internal teams are building more. Fortune 100 companies are already letting these agents post journal entries, approve invoices, pull customer data, and ship code. But legacy systems like GRC, SOC 2, ISO were created for deterministic systems and aren’t comprehensive enough to ensure safety with AI agents. 

🧨 The Problem

When an agent can reach a connected system that holds sensitive information, the critical question is whether you can prove it only did the work you explicitly requested, and didn’t touch anything else. Most teams can’t trust and verify this, for three reasons: 

1. Agents start over-permissioned. Narrowing access is tedious, so agents get broad permissions on day one and nobody goes back to tighten them.
2. Access drifts. As agents, roles, and integrations change, permissions creep past what the job needs, and no one catches it until something breaks.
3. There's no evidence. When an auditor or an incident asks what an agent did and whether it was permitted, the answer is scattered across logs that were never built to answer it.

🥳 Our Solution

TrustAI closes all three, for every agent that touches a system you care about:

• Verify every action before deployment. Native, third-party, or internal, we check what each agent does against what it's permitted to do, and flag anything out of bounds before it turns into a problem.
• Catch drift and over-permissioning. TrustAI learns your policies and flags when an agent's access has crept beyond what it needs, so you can pull it back.
• Audit-ready by default. Every check becomes part of a system of record for auditing down the road, mapped to the controls they already use across SOC 2, ISO 42001, and AIUC-1.

TrustAI is continuous. We attach verification to the moment an agent deploys or changes, so nothing goes unverified between reviews.

🚀 The Future

Enterprises will need a new standard, one built for how agents actually behave, not retrofitted from deterministic software. Our bet is that the standard belongs to whoever can evaluate agents rigorously enough to certify them, across data privacy, robustness, hallucination, and the failure modes legacy audits never had to check for. We're building that eval layer now, so that when enterprises reach for a way to trust an agent before they ship it, the answer is TrustAI.

🏋️‍♂️ Team

We're Hannah (CEO) and Medha (CTO), MIT engineers who built TrustAI. Medha did LLM inference-optimization research with Jacob Andreas at MIT and was on the US Physics Olympiad Team. Hannah did economic research at the World Bank and quant at Virtu Financial. We've been building together since our freshman year.

Reach out to evaluate your agents at trytrust.ai or email hello@trytrust.ai :)