AI agents for audit evidence collection

An invitation does not show who attended

Suppose an agreed internal audit request asks for an attendance record for a specified team at a completed training session. The training organizer adds a calendar invitation to the folder. It contains information about the meeting but does not show who actually attended.

An agent can find the file, connect it to the request, and prepare a short explanation for the coordinator: an invitation has been received, while attendance still needs a supporting record. The request and document should be available alongside that explanation. The coordinator opens them, checks the difference, and can send the organizer a specific question:

The folder contains an invitation to the training session. The request asks for an attendance record for the specified team at that session. Please send material showing who attended or point us to where we can find it.

The organizer can look for the attendance record and send it against the same request. Its absence from the folder does not establish whether employees attended or whether a record exists elsewhere.

When the organizer supplies the record, the agent can attach it to the same request and show the coordinator the new material. The coordinator checks that it concerns the correct team and session before passing it on for review. The people conducting the audit assess what the document establishes.

Keep each reply with the request it answers

With many requests underway, the coordinator needs to see who still owes an answer and what has arrived. An agent can connect the request list with correspondence and files, including explanations sent in separate threads. A colleague taking over can open the request, see the documents and latest question, and continue collecting the material without reconstructing the history from a folder of attachments.

The starting point remains the list agreed by the audit team. If a request is unclear, the coordinator needs clarification from its author. An agent can help explain the uncertainty, but it should not expand the audit scope by adding requirements. Finding the applicable working instructions is a separate need, covered in our article on an AI knowledge base for audit methodology.

An existing platform may already check for gaps

Vanta’s Information Request List supports request owners, evidence collection, and internal review before auditor visibility. Its AI checks externally uploaded evidence against requests and flags gaps; existing Vanta evidence is not re-evaluated this way. The feature requires an upgrade or add-on.

For a small set of requests, a shared list with an owner and a document link may be sufficient. Connecting tools becomes more relevant when material keeps arriving in different inboxes and folders, leaving the coordinator to match it to requests and gather further explanations by hand. Assess the work that remains outside the existing platform.

Connect audit requests, files, and follow-ups

Syntalith builds AI agents connected to company tools. A proposed initial scope would cover one package of audit requests. The agent finds documents in agreed locations, compares them with the requests, and drafts questions for designated people. It then gathers their replies with the relevant request. The coordinator receives material to review and an explanation of what still needs an employee’s response.

Your team defines the requests and identifies who reviews documents before they are shared. Using a completed case, we can assess together whether the prepared question would have helped the organizer find the right material immediately.

Tell us about the last attachment that arrived but did not answer the request. Where did you find it, and whom did you have to ask for more information? That gives us a practical starting point for discussing whether to connect sources and follow-up correspondence or make better use of the current tool. See Syntalith’s pricing page for service pricing information.