Nothing risky happens
without someone saying yes first
A business running AI agents eventually asks the same question: what exactly did the agent do, and who approved it? Without a system, the answer lives scattered across logs nobody reads consistently. We build one approval queue for anything risky across your agents, and one audit log that answers that question for any action, any agent, any time.
The process today
A business with more than one AI agent running usually has no single place to answer “what did the agents do today, and who approved the risky parts?” Each agent might log its own actions somewhere, but reconstructing a full picture means checking several systems, and most teams only do that reconstruction after something has already gone wrong, not as routine oversight.
The second cost is that “risky” is defined inconsistently, if at all. One agent might have a hard-coded limit on what it can spend or send, another might not, and whether a given action required a human yes often depends on how that particular agent happened to be built rather than a deliberate policy applied the same way everywhere.
The third is that when a mistake does happen, the first question anyone asks, who approved this, or did it run automatically, is often hard to answer quickly, which slows down the response exactly when speed matters most.
What the agent does
We build one approval queue that sits in front of any action your team marks as risky, regardless of which agent is proposing it: a payment, a deletion, a customer-facing message above a size or sensitivity threshold, a discount above a cap. The person reviewing sees the full context of the proposed action, not just a bare request, and approves or rejects with one click.
Every action any agent takes, whether it ran automatically or went through approval, is written to one audit log, append-only and timestamped, so you have a single place to answer exactly what happened, when, and whether a human signed off. If the approval queue backs up past a threshold your team sets, a manager gets alerted rather than discovering a growing backlog by accident.
Typical integrations: whatever messaging or dashboard tool your team already monitors, and a connection into each agent in scope so its actions route through the shared queue and log.
What stays with humans
Defining what counts as a risky action worth gating is a policy decision your team makes, not something we decide for you, though we help draft a sensible starting list based on what we have seen go wrong elsewhere. Reviewing queued actions and making the actual approve or reject call stays entirely with a person; the system’s job is to make sure that call happens, not to make it. Periodically revisiting the risk thresholds as your operation changes is an ongoing task we hand off with clear documentation.
Guards
The audit log is append-only, so no entry can be edited or deleted after the fact, which matters both for internal review and for any external compliance requirement. The approval queue has no bypass path, an action marked risky cannot proceed without a recorded human decision, built as a hard rule rather than a setting an agent could work around. The whole system is tested against real agent actions in a dry run before any live agent is wired into it, and access to approve actions is itself permissioned, so not everyone who can see the queue can clear it.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $900 | 1 to 3 agents, risky action rules, approval queue, audit log | 1 to 2 weeks |
| Department package | from $2,800 | Full agent fleet coverage, nuanced approval tiers, compliance export | 3 to 6 weeks |
Running cost is usually $20 to $70 a month in hosting and model usage depending on agent count and action volume.
Related
This pairs well with multi-agent orchestration for operations since a shared approval and audit layer is most valuable once more than one agent is in play, and with agent cost and quality monitoring for the operational health side of the same oversight. See the AI agents service page and the automation-everything overview for full package details. For real builds on multi-agent operations and legacy system recovery with full auditability, see the ProBay own marketplace AI agent team case study and the factory ERP recovery case study.
Can’t answer who approved what your agents did last week? Get in touch and we will map which of your agents’ actions actually need a human yes.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does it cost to set up an approval queue and audit log?
From $900 covering 1 to 3 agents and a defined set of risky action types, live in 1 to 2 weeks. Larger agent fleets with more nuanced approval rules usually run $2,000 to $3,500.
How long before it is live?
1 to 2 weeks once we know which agents are in scope and your team has defined what counts as a risky action worth gating.
What counts as a risky action that needs approval?
Whatever your team decides: sending money, deleting data, a message to a customer above a certain size, a discount above a threshold. We help define the list but the final call is yours.
Does this slow agents down?
Only on the actions you choose to gate. Routine, low-risk actions keep running automatically; the queue exists specifically for the actions where a person should look first.
Is the audit log tamper-proof?
Log entries are append-only and timestamped, so earlier entries cannot be edited or deleted after the fact, which matters for both internal review and any external compliance need.