What

Agentive is an audit-focused workspace for requesting, validating, and testing audit evidence with clients. It is built for audit teams, from smaller regional firms to larger national firms, and centers on managing client requests, reviewing uploaded documents, and completing testing procedures in one place.

The product appears positioned as a purpose-built audit request and evidence platform rather than a general collaboration tool. Based on the page, its core workflow starts when a client request is updated, then uses AI to review submitted documents against requirements, perform testing and analysis, prompt for follow-up when discrepancies appear, and support auditor review with citations back to source documents.

Features

Client request management: Centralizes engagement requests, tasks, and testing so auditors and clients can work from a single audit-specific workspace.
AI validation of uploaded evidence: Reviews documents in real time against stated requirements such as sample details and trial balance amounts to identify gaps earlier.
Automated testing and analysis: Uses a firm's testing plan, natural language prompts, and document matching to complete audit procedure testing and generate analysis quickly.
Source-linked citations: Connects generated details back to source documents in one click, which helps reviewers verify conclusions more efficiently.
Client follow-up alerts: Prompts clients to explain discrepancies or upload corrected documents, helping reduce manual back-and-forth.
Engagement visibility and logs: Provides status updates, activity logs, and configurable email summaries so both auditors and clients can track progress across the engagement.

Helpful Tips

Evaluate how closely the platform can reflect your firm's testing plan and evidence standards, since the value of AI-based testing depends on a well-defined audit methodology.
Pilot it on repetitive procedures such as accounts receivable, expense testing, payroll procedures, or revenue recognition, where request-and-review cycles are frequent.
Confirm how auditors review exceptions and override automated outputs, especially for engagements that require significant judgment beyond document matching.
Use activity logs and status views to set clearer ownership between client and audit team members, which can improve turnaround times on evidence collection.
The page highlights AI validation and testing speed, but it does not fully describe data governance, implementation scope, or system integrations, so those areas would need separate diligence.