Directum proposed workflow agents as a practical path for introducing AI into business processes

Directum proposed a more practical scenario for implementing AI in companies: not fully autonomous "digital employees," but workflow agents that operate based on predefined logic within a corporate system. According to the company, this format removes the main barriers facing business — fear of the "black box," data leak risks, and unclear return on investment.

Why Not Autopilot

Directum proceeds from a rather conservative market demand: mid-market and large enterprises don't want to build processes on external cloud services when it comes to contracts, correspondence, and internal regulations. Companies are waiting for local deployment, predictable integrations, and clear boundaries on data access. That's why instead of demonstration AI agents "on the side," the company creates built-in assistants within Directum RX that operate in the corporate perimeter and access internal documents through RAG.

This way, the agent receives not abstract knowledge, but the context of a specific organization: local regulatory acts, instructions, email history, and role-based access model. An employee can directly in the system interface provide a link to an incoming email, ask to find similar cases, and request a draft response. An important point is that humans don't disappear from the process: they verify the result and retain the final decision, while the machine handles the search, verification, and preparatory routine.

How Workflow Is Structured

Directum describes the workflow agent as an intermediate, but already useful class of AI agents. It doesn't attempt to become a universal "digital employee" that will figure out any ambiguous situation on its own. Its task is simpler and more practical: follow a pre-described route, execute typical steps, and hand off the process to a human if the task goes beyond the acceptable scenario. It's precisely this manageability that makes the approach attractive for business that needs not a wow effect, but a predictable result.

• extract data from a contract and identify key terms;
• verify requisites, amount, and basic restrictions according to company rules;
• automatically route typical documents through the correct approval branch;
• if there's a risk or non-standard wording, stop the flow and send the document for manual review with explanation.

Using contracts as an example is particularly illustrative. The agent can extract data from a document, verify the amount and requisites, understand whether the contract fits corporate rules, and when a formulation is questionable, immediately flag it as a risk. This gives business transparency: you can see at what point a rule triggered, why the document went to manual review, and where exactly human intervention is needed. For compliance, lawyers, and managers, such a scenario is noticeably calmer than implementing a "black box" with full autonomy.

Numbers and Next Steps

In one of Directum's cases, such an agent starts immediately after a contract is uploaded to the system. It launches a chain of pre-configured prompts, cross-checks the text with corporate checklists for different counterparty types, verifies payment terms and transfer of ownership rights. Then branching kicks in: for contracts up to 1 million rubles, a standard flow is possible where the agent makes a decision on an agreed scenario and notifies the employee, while for amounts over 1 million it generates a structured report with a list of discrepancies and critical points.

The numbers for such a scenario are no longer experimental but operational: the time for initial review dropped from 30 to 5 minutes, annual savings on payroll reached approximately 4.8 million rubles, and review accuracy, according to the company, was 95%. This freed two in-house lawyers from a flow of routine tasks and allowed them to focus on complex contracts. Directum sees the next step in "orchestrators" that will coordinate several narrow agents — for contracts, invoices, and execution deadlines.

"AI drives the process, humans control it and make final decisions in

exceptional cases."

What It Means

The corporate AI market is gradually abandoning the idea of instantly replacing people with fully autonomous agents and shifting toward a more pragmatic model. For business, this means a simple route: pick one overloaded process, hand the routine to a workflow agent, measure the savings, and only then scale the approach to other operations.