Your agents are already calling APIs, moving data, and executing code. Tuora makes sure every single action traces back to a human who said yes.
Agentic AI is already handling consequential decisions in regulated environments, from trade accounting and client onboarding on Wall Street 1 to prior authorization and claims-denial workflows in health insurance 2. Enterprise deployment of these agents has accelerated sharply in the past year 3, and regulators have responded with traceability and human-oversight mandates such as the EU AI Act's logging obligations 4. Yet governance is not keeping pace: most organizations cannot yet hold their agents consistently accountable. The deeper issue is that existing controls largely answer who took an action, not why. Proving which agent was present does not prove its intent, its reasoning chain, the tools and data it consulted, or whether it stayed inside its authority boundary. Without intent-level provenance, including reasoning, tool calls, data accessed, and authority boundary, there is no auditable answer to why an agent decided what it decided.
Regulators and auditors need evidence that every autonomous action was authorized, scoped, and reviewable. TRS produces that evidence automatically.
Every tool call, API request, and data access is logged with the full intent context — the human sponsor, the authorized manifest, the runtime token, and the outcome. When regulators ask for evidence, you export a cryptographically signed trace, not a spreadsheet.
The authorization token links every agent action to a developer-authorized manifest and an end-user intent issued at runtime. Auditors do not have to trust your model card; they can verify who delegated authority for any specific action.
Tuora evaluates every tool call against the authorized intent boundary in real time. Unauthorized actions are stopped at the edge and logged with full provenance — giving you evidence of control effectiveness, not just post-incident forensics.
Regulators and auditors need evidence that every autonomous action was authorized, scoped, and reviewable. TRS produces that evidence automatically.
Every tool call, API request, and data access is logged with the full intent context — the human sponsor, the authorized manifest, the runtime token, and the outcome. When regulators ask for evidence, you export a cryptographically signed trace, not a spreadsheet.
The authorization token links every agent action to a developer-authorized manifest and an end-user intent issued at runtime. Auditors do not have to trust your model card; they can verify who delegated authority for any specific action.
Tuora evaluates every tool call against the authorized intent boundary in real time. Unauthorized actions are stopped at the edge and logged with full provenance — giving you evidence of control effectiveness, not just post-incident forensics.
The whitepaper is currently in progress. Check back soon for the full release on accountable agentic AI execution.
Coming soon
Regulators are starting to demand accountability for agentic AI. What's missing is the infrastructure to prove it — a way to trace every action an agent takes back to a human who actually said yes. That's the problem Tuora exists to solve.
Consequential decisions at scale
Agents now handle trade accounting, prior authorization, and KYC checks in production. The volume is growing faster than the governance layered designed for it.
Governance is not keeping pace
Only 2% of technology leaders say their AI agents are held fully accountable and consistently governed. The rest are flying blind.
Identity is not enough
Proving which agent took an action does not prove why it took that action. Accountability requires intent — the reasoning, tool calls, and authority boundary behind every decision.
CLick or tap a row for the full trace of the agent, backed by authorization token verification from the human sponsor, manifest verification, and runtime intent verification.
These answers focus on the practical questions security, platform, and compliance teams need resolved before agent actions can be trusted.
Once an agent can call tools, access data, or trigger downstream systems, the real risk is no longer just bad model output — it is unauthorized action. Tuora exists to make sure consequential agent behavior stays inside an approved scope and can always be tied back to a human authorization.
Tuora evaluates each consequential agent action at runtime against an authorized manifest and intent boundary. If the agent tries to use the wrong tool, reach beyond its approved scope, or take an action that was not authorized, Tuora can stop it and record what happened.
IAM can identify the agent, and logs can show that an action happened. Tuora adds the missing control layer: proof of why the action was allowed, who authorized it, whether it stayed within scope, and what evidence exists for review after the fact.
Tuora produces action-level provenance: the human sponsor, the authorized objective, the tools and data involved, the runtime decision, and the outcome. That gives security, audit, and compliance teams a defensible record of what an agent did and whether it acted within policy.
Tuora is most relevant for teams putting AI agents into production where actions have operational, financial, legal, or regulatory consequences. That usually starts with security, platform, governance, and compliance leaders responsible for proving that agent behavior is controlled, reviewable, and accountable.