An AI agent does real work with little human direction — and that same autonomy creates new ways to fail that ordinary IT safeguards miss. Left unnamed, these failures get written off as “glitches” instead of risks the business owns. Here are the main ones, and what it takes to manage them.
The main ways agents fail
An agent makes decisions on its own and reacts in real time to whatever comes at it. That’s useful — and it’s also where the new failures come from.
Tricked into the wrong action
Prompt injection, jailbreaks, poisoned data: a cleverly worded input — or just messy data — can push an agent off its instructions and into doing something it shouldn’t, like approving a transaction or sending data out the door. The agent thinks it’s following a valid instruction; the cost lands on you.
Running away with itself
An agent without firm limits can fall into a loop — repeating an action thousands of times, burning through resources, or firing off high-speed actions before anyone notices. It can also quietly slip past the limits you set months ago, with nothing flagging that it has.
Reaching beyond its job
An agent’s power to use your tools — APIs, databases, payment systems — is also its biggest risk. If an over-permissioned agent is compromised or simply makes a mistake, the damage is limited only by what you let it touch. The fix is least privilege: each agent gets only the access its job needs, and no more.
How we manage these
We treat these failures not as software bugs but as risks the business owns — the kind your insurer, lender, and board will want an independent reading on.
- First, the floor. Until every agent passes through one gate and everything it does is logged, there’s no way to verify any other safeguard — there’s nothing to check it against.
- Then, the map. Every failure above maps to something we assess: how much an agent can do on its own, what data and IP it touches, its security, how dependent you are on a single vendor, and where it creates regulatory exposure.
- Then, the proof. We don’t issue paper of our own. We write the controls, your team operates to them, and we check they hold — from your own risk register and the logs your systems produce. Kept current and watched on the subscription, those are what your insurer, lender, or board can actually rely on.
What the people who fund you need
When your insurer, lender, or board asks how you handle prompt injection or a runaway agent, they’re not asking for a technical fix — they’re asking for evidence they can rely on: a board that can show the risk was looked at, an underwriter who can price the exposure, a lender who can write a clear condition into the loan. That evidence is the register and the logs — dated, methodical, with the controls for these failures in place, tested, and watched. That’s the kind of AI risk management the people who cover and fund you actually trust.
Summary
AI agents fail in predictable ways — injection, runaway loops, over-broad permissions, silent drift — and the business owns the fallout, not the vendor. One gate for every agent, tamper-evident logs, and a live register turn those failures into evidence your insurer, lender, and board can actually use.