Scaling AI agents requires treating them like employees in regulated roles, with written rules, clearly defined responsibilities and a named human manager accountable for their performance. Guest author Rick Folgmann, COO at Get Covered, tells companies they should start with tasks governed by established rules, adjust autonomy to each workflow's risks, and regularly review results before expanding agents' authority. A landlord in California has 21 days after a resident moves out to return the security deposit or send an itemized list of deductions.
Miss it in bad faith, and the penalty can reach twice the deposit. That deadline doesn't care whether the letter was supposed to come from a person or a piece of software. Either way, someone will answer for it.
At Get Covered, we now run 16 AI agents across more than 3 million rental units. Most companies are about to face that same question, and few are ready for it. Deloitte's 2026 State of AI survey found that 23% of companies use agentic AI at least moderately today, and 74% expect to within two years.
Gartner has predicted that more than 40% of agentic AI projects will be canceled by the end of 2027. Those numbers make a case for running agents the way you would run staff in a regulated job, with written rules, a defined scope, a manager and a regular review. Start with work that already has a rulebook.
The best early candidates for agents are tasks where the rules already exist somewhere outside anyone's head: a statute, a contract clause, an insurance requirement in a vendor agreement. Mc Kinsey's most recent State of AI research found that the small group of companies seeing significant bottom-line returns from AI, about 6% of respondents, were nearly 3x as likely to have fundamentally redesigned their workflows. Set autonomy by workflow.
Tech execs often ask whether an AI system is autonomous, as if that were a fixed property of the product. In practice, the same agent should run with different levels of freedom depending on what it touches. Give every agent an owner with a name.
Earlier in my career I learned that every automated credit decision eventually had to be explained to a customer, a regulator, or both. The model did it was never an acceptable answer.
Source: Crunchbase News · Summarized by HeadlinesBriefing