Document review and approval processes are central to many business operations, especially where regulatory compliance, safety, or financial accuracy is involved. Despite their importance, these workflows are often manual, inconsistent, and time-consuming. While many organizations have implemented electronic review and approval systems, these platforms often require significant technical effort to build and are costly to manage and maintain. In contrast, smaller teams or midsize businesses may still rely on email, spreadsheets, or ad-hoc checklists to manage critical reviews, increasing the risk of missed steps or delayed decisions. These informal tools also offer limited visibility into the document’s progress or the accountability for each step, making it difficult to track progress or enforce consistency.
In both cases, AI presents an opportunity. At Docuvela, we’ve been exploring how AI can support and enhance review and approval workflows. By designing intelligent, rule-driven AI agents that can assist with tasks like creating routing rules or performing initial reviews, we’ve started to uncover ways organizations can streamline their processes without compromising quality or accountability.
This post shares what we’ve learned so far, how it applies to real-world challenges, and how customers can begin to adopt similar practices in their environments.
Our Vision: Smarter Workflows with AI Agents
We believe AI can be a powerful enabler to improve how organizations manage document review and approval workflows. Our vision centers on intelligent agents that streamline repetitive or complex tasks and help reduce bottlenecks.
We have developed two core AI agents to demonstrate this approach:
- The Initiator Agent is designed to assist administrative users in configuring workflow processes. Through a chat-style interface, the administrative user describes the business process and routing rules in natural language. The agent then translates that input into structured, machine-readable rules, which are stored in Veladocs. This setup allows routing logic to be created and refined over time without requiring technical expertise or system reconfiguration.
- The Reviewer Agent assists by performing initial document reviews. It evaluates content quality, completeness, and clarity, providing confidence scores and flags for potential issues that require human attention.
We expect that most organizations will start by adopting AI-assisted reviews. This approach can significantly speed up the review cycle while ensuring consistent quality checks. AI-driven approvals, however, remain a longer-term goal and are unlikely to be appropriate soon, especially for customers in highly regulated industries. Human oversight will continue to be essential to ensure accountability and compliance.
By combining AI agents with flexible human-in-the-loop workflows, we aim to create smarter, more adaptable workflow processes that enable reviewers to focus on the important content details, rather than editorial wordsmithing and document hygiene checks, reducing friction and allowing organizations to focus on what matters most.
Initiator Agent: Defining Workflow Logic with Natural Language
The Initiator Agent was designed for administrative users who manage review and approval processes but may not have deep technical expertise. Instead of complex BPMN templates and workflow process interfaces, users interact with the agent through a simple chat interface. They describe their workflow in plain language, who needs to review, under what conditions, and in what sequence.
The agent translates this input into a structured set of routing rules stored as JSON in Veladocs. These rules can be versioned, audited, and updated over time as the business process evolves.
Take the following example, where an administrator describes routing rules for SOP documents:
When routing qa_sop documents, first send to the `quality_assurance` group, but only if the `priority` attribute is not "Low". Then, send to the `regulatory_affairs` group, but only if the `regulatory` attribute is set to "True". Finally, send to the sop_reviewers group.
Based on this natural language input, the agent generates and stores the JSON file below in Velaodocs. The JSON file is then read by the workflow engine without any coding or technical configuration:
{
"workflow" : {
"steps" : [ {
"participants" : [ {
"id" : "quality_assurance",
"type" : "group"
} ],
"rules" : [ {
"documentType" : "qa_sop",
"conditions" : [ {
"field" : "priority",
"operator" : "!=",
"values" : [ "Low" ]
} ]
} ]
}, {
"participants" : [ {
"id" : "regulatory_affairs",
"type" : "group"
} ],
"rules" : [ {
"documentType" : "qa_sop",
"conditions" : [ {
"field" : "regulatory",
"operator" : "=",
"values" : [ "True" ]
} ]
} ]
}, {
"participants" : [ {
"id" : "sop_reviewers",
"type" : "group"
} ],
"rules" : [ {
"documentType" : "qa_sop",
"conditions" : [ ]
} ]
} ]
}
}The Initiator Agent correctly generates and stores routing logic rules, reducing what previously took hours of configuration for IT resources to a few minutes of natural language input. Additionally, the agent can be utilized by business SME resources, eliminating the risk that business rules are lost in translation when coordinating with IT.
Reviewer Agent: Performing First-Pass Document Reviews
The Reviewer Agent was created to support content quality checks before documents move forward in the review process. While the agent can be tweaked for specific business cases, in our initial testing, it evaluates documents for three key criteria: completeness, clarity, and content quality. The agent returns an overall review result (“success” or “failure”), a confidence score, and – when the review fails – a set of reasons for failure.
The Reviewer Agent can also accept dynamic metadata input to provide context before the review step. For example, in our testing for this blog post, the document title attribute is used to set up the agent for each document, providing the agent with additional information on which to base the review.
In our internal testing, we utilized our Docuvela Password Management Policy to put the reviewer agent to the test. With the policy as is, the Reviewer Agent returned JSON indicating the document passed review with 90% confidence.
More interesting are the edited versions of the policy that mimic scenarios that should fail review:
Completeness
In this relatively straightforward use case, the document was edited to remove sections and leave other sections unfinished. The agent easily caught these use cases with failure messages such as:
The ‘AUDIENCE’ section is present but completely empty, lacking any description of who the intended readers/users of the policy are.
The document ends with a ‘Commented [GS1]: TODO’ note, indicating the document is unfinished and missing potential important content.
Clarity
Next, we edited the policy to remove clarifying text to see if the AI could correctly catch when the document needed additional details. This included specific policies, but also instructed the agent to pay attention to the indicated audience in the document to make sure that the text was not overly technical for general audiences.
As with the Completeness test, the agent performed well with Clarity, returning suggestions such as:
The policy includes technical terms and controls (e.g., multi-factor authentication, role-based access controls, automatic logoff) that may require further explanation for the entire employee audience to ensure clarity and appropriateness.
The ‘Removing Access’ section does not mention verification steps or audit trails, which could be relevant for compliance and security assurance.
In this test, we purposefully placed overly complex language in the document. The AI agent correctly caught this in its review, stating:
The instructions are generally clear but include some complex language (e.g., ‘syntactically invalid passphrases composed of a concatenation of three or more semantically associated lexemes interleaved with non-alphabetic delimiters’) which could be challenging for all employees. This reduces clarity and audience appropriateness.
Content Quality
For this use case, we wanted to test the AI Review agent’s ability to correctly spot quality issues in the policy. To test, we removed some of the password policy guidelines to see if the agent could spot this in the review.
The Reviewer agent correctly caught these quality issues, with responses such as:
The PASSWORD REQUIREMENTS section is incomplete in defining password complexity, listing only a lower-case letter, upper-case letter, and a number but no mention of special characters or additional complexity details.
The document lacks specific instructions for password complexity enforcement mechanisms (e.g., password history, lockout thresholds), which are common in Fortune 500-grade password policies and necessary to ensure completeness.
Key Takeaways
Our work with AI workflow agents in Veladocs has surfaced several important insights for organizations exploring this space:
- AI Agents Can Enhance Review and Approval Processes Today
Agentic AI introduces a meaningful shift in how review and approval workflows can be designed and executed. Natural language-driven configuration, particularly through chat-style interfaces, significantly lowers the barrier to managing workflow logic. We believe this pattern will become standard for modern workflow systems. - AI-Assisted Review Can Be Fully Automated, Approval Is Further Off
Organizations are already seeing value from AI-assisted review workflows, where documents are first reviewed by an AI agent before being passed on to human reviewers. This model maintains oversight while improving speed and consistency. Fully autonomous approval agents, however, are likely still several steps away, particularly for regulated industries where human decision-making remains essential. - Domain Knowledge Integration Will Boost Review Effectiveness
The performance of the Reviewer Agent could be improved by incorporating domain-specific knowledge using Retrieval-Augmented Generation (RAG). For example, connecting the agent to internal review policies or SOP repositories would allow it to assess documents with more relevant context, leading to more accurate and useful reviews. - Model Selection and Prompt Design Matter
The quality of results varies significantly based on the AI model used and how the prompt is structured. Our tests showed that the gpt-4.1-mini model produced strong, reliable results in most scenarios. The gpt-4.1-nano model, while faster, was less consistent. We also tested gpt-4o-mini, which showed promise in some tasks but was less effective in others. Prompt engineering played a key role in refining the behavior and reliability of the agents, emphasizing the importance of thoughtful design and iteration.
What’s Next
Our initial work with AI workflow agents has been focused on building and testing the core backend functionality – defining how agents are configured, how they process input, and how they return structured, auditable results. These early results have shown strong potential, and we’re now looking ahead to the next phase of development.
Our next step is to extend this functionality into the user interface. This includes building intuitive tools for managing workflow processes using the Initiator Agent, as well as interactive UI components to support review and approval tasks powered by the Reviewer Agent. Our goal is to make these experiences simple, transparent, and seamlessly integrated with existing Veladocs capabilities.
We’re also continuing to refine how AI agents interact with domain-specific knowledge, how they’re configured, and how they’re used in both regulated and unregulated environments. This includes supporting hybrid workflows that balance automation with human oversight.
If you’re exploring ways to improve your own review and approval processes – whether through AI assistance or more flexible workflow design – we’d love to connect. We’re actively working with customers to understand real-world needs and tailor solutions that meet those challenges.
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