As AI agents become more autonomous, managing them individually can get messy.
Teams may need a central place to control agent permissions, monitor activity, manage access, and keep humans accountable for important actions.
I’m exploring this idea through Sharkly.ai, where AI agents are treated as active participants in team workflows rather than simple assistants.
For those building agentic systems: what would you consider essential in an AI agent governance tool?
A governance record that says “acted on values” without saying which values, as understood when, is writing checks against a shifting account.
Values are Overton-subjective — what counts as careful today was paranoid yesterday and will be naive tomorrow. The record has to capture the values in play at run time, not assume a fixed set that the future will honor.
The essential primitive is probably not in your list. Permissions, monitoring, access management, accountability — those all govern the agent. The governance gap is the grounds: permission policy answers “may it,” but it never answers “on what values.”
Consider what the governance layer could count instead: which runs went ahead without any declared scope, which values came from nowhere, which approvals were session-level standing in for step-level ones. A permission layer sees “listing management was approved.” A grounds layer sees that four irreversible actions happened inside that approval and none of them had a declared scope.
But there’s a deeper gap underneath the tooling question: when the technology makes the mistake, the user pays the penalty. The agent holds the power to act — it sends, it shares, it commits — and the human holds the consequences. The buyer drove thirty minutes; the user got the bill. Every incident report reads the same way: the system acted, the person answered for it. A governance tool that only monitors the agent while the penalties keep landing on the user isn’t governing the power; it’s documenting it.
The check that could prove this wrong: take your current monitoring and ask it to distinguish a message that closes a deal from a message that says hello. Both are permitted actions on the same connector. If the tooling can’t tell them apart, it’s governing actions, not runs — and the incidents that matter live at the run level. Then ask who pays when the tooling misses one, and which values it was enforcing. If the answer is always the user under values nobody wrote down, the governance is pointed at the wrong party under the wrong sky.
Small next step: for each agent action your tool logs, add three fields — “scope declared: yes/no,” “who bears the cost if this goes wrong,” and “values in play (as of this date).” You don’t need the scope yet. Just count how many runs go ahead with the first field empty, the second always reading “user,” and the third blank. Those three numbers are your governance debt.