The hype and the reality
Everyone's talking about AI agents. Half of LinkedIn insists they'll replace entire teams. The other half already tested them and got disappointed because the agent hallucinated, got customer data wrong, or got stuck in loops.
The truth is in the middle — but more useful than either extreme.
AI agents work well in well-defined tasks within larger processes. Not as team replacements, but as extra members who do the repetitive work nobody wants to do.
What an AI agent actually is
An AI agent is a language model with access to tools: it can read files, search, fill forms, send emails, call APIs. The difference from a normal chatbot is that the agent can take a sequence of actions to complete a task, not just answer a question.
The problem is that "taking a sequence of actions" sounds simple, but in practice requires you to define exactly what the agent's scope is. Agents without clear scope wander, make up answers, and create more work than they save.
Where agents work well in processes
Triage and classification
An agent can read an intake form, identify the type of request, extract relevant data, and create a case already classified with fields filled in. That's it. Nothing more. And for that, it works very well.
In law firms, for example, an agent that triages initial inquiries — reads the potential client's form, classifies by legal area and urgency, fills the case with summary and extracted data — cuts work that used to take 20 minutes per case.
Data extraction from documents
Contracts, medical reports, certificates, invoices. Documents full of structured information that someone needs to copy into a system. Agents with vision and extraction tools do this reasonably well when the format is consistent.
The caution here is always maintaining a human review step. Not because the agent always makes mistakes — but because when it does, the error is silent. The field looks filled in, but the value is wrong.
Draft generation and communications
An agent can draft a collection email, a deadline notification, or a case history summary for a report. The text will need review, but it comes out 80% ready.
This works better than it sounds, especially for repetitive communications where tone and content are predictable. An insurance claim has a well-defined communication flow: receipt confirmation, document request, analysis in progress, result. An agent can draft each message based on the current state of the case.
Automated checks and alerts
Agents can monitor conditions and act: if a case has been stuck for 48 hours without an update, send an alert to the responsible person. If a document has an expired validity date, open a task. If a required field was filled in inconsistently, flag for review.
This kind of "process watchdog" is where agents consistently deliver value with low risk.
Where agents don't work (yet)
Decisions requiring real judgment
Approving credit, evaluating whether a contract has problematic clauses, deciding if a candidate advances to the next selection stage. Agents can help with analysis, but the final decision needs a human who's accountable for the outcome.
This isn't a temporary technical limitation. It's structural: someone needs to be responsible for the decision. Agents don't have accountability.
Unstructured processes
If the process isn't defined, an agent won't define it for you. It'll do the best it can with vague instructions, and the result will be inconsistent. Before putting an agent in a process, the process needs to be documented.
Edge cases
The processes that consume the most human time are the exceptions: the case that doesn't fit the template, the client with a special situation, the document that arrived in the wrong format. Agents are trained on the average. They handle common cases well, rare ones poorly.
How to implement in a way that works
The most common mistake is starting with the most ambitious agent: "I want the agent to manage the entire process." This almost never works on the first attempt.
What works is starting small and specific:
- 1Choose a repetitive task in a process you already have structured
- 2Define exactly what the agent should do — input, output, limits
- 3Keep human review in the loop, at least initially
- 4Measure whether it's actually saving time, not just looking automated
- 5Expand only after that task is working well
In CaseFy, AI agents are configured per template: you define which agent runs at which stage, with what prompt, with access to which case data. The result goes to a review step before advancing the process. This keeps the human in control without eliminating the automation benefit.
What changes in the next two years
Language models are improving at structured reasoning. The ability to follow complex instructions without inventing data is increasing. The cost is falling.
This doesn't mean agents will replace entire teams. It means the cost of putting an agent on a task will fall to the point where it makes sense for increasingly smaller and more specific tasks.
The company that will win isn't the one that automates the most, but the one that knows where automation makes sense — and where human judgment is still the most valuable asset.