A standing review board for what your AI agents actually did — triage proposed agent actions, scrutinise the higher-risk ones, and keep a transparent record of every approve/deny so autonomous teammates stay accountable.
Leaders and platform owners overseeing AI agents under propose/approve governance.
Quick answer
The Agent audit review template is a ready-made workspace for Leaders and platform owners overseeing AI agents under propose/approve governance.
Agent audit review template in short
Included when you apply it
Applying this template creates the 1 board below — with every list — and pre-loads 3 sample issues, all yours to edit. The automation rules further down are suggestions you can wire up next; they aren't created for you yet.
A preview of how this template lays out — the boards, their custom workflow states, and where the sample issues land. WIP caps show a badge.
Agent oversight
Review the agent's bulk-archive proposal on stale issues
Destructive bulk ops route through approval — confirm the scope can't over-match before approving.
Audit the triage agent's auto-label decisions this week
Spot-check a sample for accuracy and false positives; tune the agent's instructions if drift appears.
Decide whether to grant the agent a new tool capability
These rules aren't created when you apply the template — they're recipes you can wire up in Settings → Automations once your board exists.
When
An agent proposes a destructive action
Then
Open a review card and require an explicit human approve before it runs
When
A proposed agent action is approved or denied
Then
Record the decision in the same audit trail as the runtime broker
When
An agent is granted a new tool capability
Then
Notify the workspace owner and log the capability change
The column order encodes a rule, not a workflow preference: nothing an agent proposes reaches 'Approved' without landing in a human's queue first. 'Flagged' exists as a separate lane from 'Under review' because not every proposed action carries the same risk — a label suggestion and a bulk-archive proposal should not compete for the same reviewer's attention at the same urgency, and collapsing them into one lane is how a reviewer starts rubber-stamping everything to keep up.
A read-only or single-item proposal (an auto-label, a status suggestion) can move straight through 'Under review' with a quick glance. Anything destructive — bulk archive, bulk update, delete — belongs in 'Flagged' by default, because a wrong approval there is not reversible by simply re-running the agent. This is not a policy you have to invent: the same distinction already exists in the tool registry's destructive flag, so the board is reflecting a real gate, not adding an artificial one.
The agent's stated reasoning is usually right; the failure mode is a query or a filter that matches more than the agent assumed it would. Before approving a bulk action, read the actual scope — how many records, which workspace, which team — the same way you would sanity-check a raw SQL WHERE clause before running it. An agent that explains itself well and an agent that scoped itself correctly are two different things, and only one of them is what you are approving.
Low-risk actions that ship without a human in the loop are still worth spot-checking on a cadence — that is what the 'Audit the triage agent's auto-label decisions' card in the sample issues is for. Drift shows up first in the boring, high-volume category, not in the rare destructive one, because nobody is watching it in real time.
Every approve/deny here should land in the same audit trail the runtime broker writes to for auto-approved operations — one record of what agents did and what humans decided about it, not two systems that can drift apart. A governance board that keeps its own separate log duplicates the work and eventually disagrees with the source of truth.
Granting an agent a new tool capability is a different kind of decision than approving one action — it changes what every future proposal can contain. Treat it like a permissions change, not a to-do: who requested it, what it lets the agent touch, and whether the existing review cadence can actually keep up with what it would now be able to propose.
This board encodes one opinion about how the work should run. Here is where that opinion is wrong and something else fits better.
Honest comparisons, including where the other tool wins. Planoda is pre-launch, so nothing below is a benchmark — it is a description of how each product approaches this job.
Both ship AI features directly inside the product — writing, summarizing, autofilling fields — and both are more mature and more widely used today than anything pre-launch Planoda can claim. Neither, as far as their public documentation shows, exposes those AI actions as a first-class 'proposed action' object with its own review queue; the AI assist is embedded in the authoring flow rather than gated behind an approval step.
Where ClickUp Brain / Notion AI is better: If what you need is AI that helps a human write and edit faster, both are further along and more broadly adopted than an approval-gated agent model. This template is for a different job — reviewing autonomous or semi-autonomous actions, not accelerating a human who stays fully in the loop the whole time.
Most SaaS platforms ship an audit log: a searchable record of what happened, by whom, when. That is valuable and this board is not a replacement for it — the runtime audit trail this board writes into is exactly that kind of log. The difference is timing: an audit log is a record after the fact, while this board's job is the gate before the fact, for the subset of actions risky enough to need one.
Where A platform audit log (Slack, GitHub, admin consoles) is better: A dedicated audit-log product will have better search, retention, export, and compliance tooling than a kanban board's history ever will. For 'what happened and can I prove it', reach for that. For 'should this be allowed to happen', that is what this board adds on top.
Purpose-built LLM observability platforms trace prompts, tool calls, and model outputs in detail meant for engineers debugging agent behavior — token-level, trace-level, far more granular than a review card will ever show. This board is not that: it shows a reviewer what the agent wants to do and asks for a decision, not why the model produced that plan.
Where LLM observability tools (LangSmith and similar) is better: For debugging why an agent behaved a certain way — bad prompt, bad retrieval, a model regression — a real observability tool with full trace visibility is the right instrument, and it is considerably deeper than anything a kanban card surfaces.
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