Workiva launches Agent Studio, a no-code AI agent platform for finance and compliance

Workiva has launched an AI agent platform called Agent Studio, together with new tools aimed at regulatory and disclosure work, betting that non-technical staff can build their own automation instead of waiting on IT.

The core of Agent Studio is no code. A user describes the job in plain language, the platform asks for missing information and returns an outline of goals and next steps, then builds a workflow on a visual canvas where every step can be inspected, edited or reordered. Prebuilt templates offer a starting point for tasks such as a period-end flash report.

Unlike generic enterprise AI tools, Agent Studio sits inside Workiva AI. Agents can be grounded in the organization's own context, including prior filings, internal policies, auditor checklists and institutional guidance, so that claims trace back to a source the company has already validated.

Control is the design center. Workiva says users decide who can build agents, what they can build and which data and documents agents may touch, with an auditable record of every action. Rules for human review, approval and oversight can be set at run time so agents ask for input before sensitive steps.

The company's own example is a period-end flash report, which normally takes days of building spreadsheets, chasing colleagues for explanations and pulling the pieces together. In Agent Studio, a user describes the need, for example automating the report, calculating variances, pulling key metrics and flagging what moved, and then confirms the plan the platform proposes.

The target users are defined as finance, risk and sustainability practitioners rather than engineers. Workiva stresses in its FAQ that building an agent requires no coding and no engineering queue, and that the agents are saved, reusable and shareable across a team.

On security, Workiva says data in Workiva AI is encrypted in transit and is not used to train large language models, and that agents built by customers inherit the platform's existing security, permissions, traceability and audit trail.

The significance is that this pushes agents out of demos and into regulated workflows. Disclosure and compliance work demands strong auditability and clear accountability, so whether auditors, risk teams and regulators accept the records AI agents leave behind will decide how far enterprise agents can go.