Alexey Spas is the founder and CEO of Instinctools, a software engineering company focused on AI-powered digital solutions.
The burden of document management is easy to underestimate because it rarely shows up as a single big task. Instead, it’s distributed across the company; an employee renames and saves a file under the wrong version, forwards an email so others can get an important attachment, copies information into a spreadsheet or searches for the latest version of a document.
None of these actions require much effort, but added together, they consume professional capacity and create opportunities for delay and error.
The old way of handling documentation ruins most productive hours.
Knowledge work is already highly fragmented. A 2025 Microsoft research report found that staff can be interrupted hundreds of times a day. There’s no time for deep focus in the modern office, be it remote or on-site, as emails, messages and ad hoc meetings can distract employees every two minutes. That’s hours of senior experts’ time down the drain, most of it during natural performance spikes when human thinking is the sharpest.
Agents can absorb much of this traffic and save staff from digging through the constantly growing backlog of notifications and documents. An autonomous system can do the first pass: sort a request, tag it, apply the company’s naming and sensitivity rules, surface the deadlines and assign an owner.
Agents are shifting from responding to making decisions.
AI agents can parse the company’s knowledge base, operate within the software ecosystem, make decisions and act on them, lightening the cognitive weight that white-collar employees face daily.
One recent project we worked on involved an AI operational platform for a German multientity professional services firm. The inbox was one of the sources of unprioritized knowledge chaos for staff members to sort out before they could get to actual work. They wanted to change the document management workflow to fit the agents to their full proactive potential.
The difference between a mediocre and a skillful agent is your business context.
If agentic autonomy alone were enough to rewire established workflows, every company would already be agent-first. The reason why we don’t see such a rise is because LLMs know only how the work is done in general, not at a particular organization. That broad knowledge may cover the common shape of quoting, advisory and insurance practices, but it won’t stretch to the context-aware calls that a specific multipractice firm requires.
For AI agents to become a sound investment, they have to be well-versed in the company’s proprietary intelligence and operational knowledge. Codifying that on-the-ground context is no small feat for enterprises whose intricate workflows have hardened over the years, but prioritizing it early can put a company ahead of the competition.
Organizations should resist creating isolated agents for every document task.
At a multientity company scale, one can build agents with task-specific skills one by one. The drawback is that each agent operating with separate contexts, permissions and audit rules will recreate the fragmentation the project was intended to solve.
Through a governed layer, specialized agents can inherit the same data access, security and cost controls, workflow logic and review requirements. While this shared operational foundation can be created from scratch, we built it within an agentic AI framework to speed up the professional services firm’s employee assistant chatbot, quoting and advisory agent project.
AI’s agency doesn’t replace that of humans.
In the agentic era, the risk is not only that the systems would confidently say the wrong thing but that they’ll just as confidently do the wrong thing, acting outside established guardrails. Agentic operations should be AI-led but human-owned, with senior experts supervising multiagent systems, making the final call and owning the decisions.
In the firm’s setup, every agent’s output was treated as a working draft for a qualified professional to approve. With every AI-generated finding linked to its exact source, assurance staff could verify evidence in seconds. Moreover, every agent action had a mandatory audit trail, retained for 12 months, available for internal staff and external auditors and regulators.
It’s time to shift the focus.
Document-heavy work looks much the same across industries that otherwise have little in common: quoting in finance, claims processing in healthcare and regulatory submissions in pharmaceuticals. Wherever you look, well-paid specialists lose hours to intake, drafting and cross-checking before any of their judgment comes into play.
Business leaders must shift the focus from chasing multiple agents—each promising high ROI—to investing in the operational foundation, without which only a fraction of that ROI can materialize.
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