Every AI agent action.
Accountable.
The Agent Accountability Platform — tamper-evident provenance from action capture to audit report.
The missing layer between agent observability and AI governance. Explore the SDK →
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Your AI agents are taking actions. Can you prove what they did?
When an AI agent calls an API, writes to a database, or sends an email — there is typically no structured record of what happened, what triggered it, or whether a human authorized it.
No audit trail
Agent actions are buried in unstructured logs. When an auditor asks what happened, you call an engineer.
No authorization record
Was a human in the loop? Did anyone approve that API call? Most agent frameworks don't record this.
No compliance mapping
Regulators are starting to ask. SOC 2, HIPAA, EU AI Act — most companies have nothing to show them.
No decision provenance
Agents reason and decide before acting — but the decision chain that led to a bad outcome lives only in a log that requires a developer to interpret. Observability tools capture what happened; Providex AI captures why the agent decided to do it.
An AI agent opened a reverse SSH tunnel — and governance tooling missed it entirely
In a documented incident involving Alibaba Cloud infrastructure, an AI coding agent autonomously established a reverse SSH tunnel without instruction. The firewall caught it — not the governance layer. There was no structured record of the agent's decision to take that action, no authorization checkpoint, and no audit trail.1
This is the Decision-level accountability gap Providex AI closes.
1 ROCK & ROLL & IFLOW & DT Joint Team. (2025). Let it flow: Agentic crafting on rock and roll, building the ROME model within an open agentic learning ecosystem (arXiv Preprint). arxiv.org/pdf/2512.24873
How Providex AI works
From agent execution to compliance artifact — one accountability graph captures every step.
Agent runs
Your LangGraph, CrewAI, or AutoGen agent executes in production as it always has.
RootSign intercepts
@rootsign.trace · zero code changes to your agent — < 5ms overhead, no logic changes.
Decision + Action captured
Every reasoning step and tool call recorded together — the why and the what, hash-chained.
HiTL checkpoint
Human-in-the-loop approvals logged as Approval records — who, when, with what context.
Compliance artifacts
Records map to SOC 2, HIPAA, EU AI Act Article 12/13/14. Export in under 5 minutes.
Seven entities. One accountability graph:
Agent · Session · Decision · Action · Policy · Approval · Incident
agents per enterprise on average, deployed today
of orgs with full visibility into their agent landscape
of the EU AI Act now requires automatic logging
the gap no observability tool, security platform, or GRC tool currently closes
Built for engineers. Ready for auditors.
Open-source Python SDK. Add tamper-evident provenance and human-in-the-loop authorization with one line.
Drop-in SDK. Under 10 minutes.
Instrument your LangGraph or CrewAI pipeline with a single call. SHA-256 hash-chained records, less than 5ms overhead, and a rootsign verify CLI that proves the chain is intact.
import rootsign
from rootsign import session
# Instrument your LangGraph tools with one line
async with session(agent_id=agent.agent_id, client=client) as ctx:
tools = rootsign.wrap_tools([send_email, query_db], ctx=ctx)
# your graph runs here — every tool call is captured
# Verify the chain
# rootsign verify <session_id>
# VALID ✓ — 47 records, chain intactAudit-ready compliance dashboard
Comply — Phase 2One-click SOC 2, HIPAA §164.312, and EU AI Act Article 13/14 reports from the same accountability graph — no engineering ticket.
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