“AI governance” currently covers a lot of different things. Some products focus on model behavior. Some focus on observability. Some focus on security or compliance. Some focus on the software changes produced by AI systems.
So before comparing vendors, define the problem you're actually buying a tool to solve. For software development, one useful framing: can this system evaluate a change before it merges, and explain why it should or shouldn't proceed? Seven questions follow from that.
1. Does it run before merge or after deployment?
Start here, because it eliminates the most vendors fastest. A tool that analyzes production traces and incidents is doing useful work — it is just not the same work as evaluating a change before it ships.
Ask: where in the development lifecycle does the decision happen? If the answer is “we give you full visibility,” that's an answer about observability, not about governance. See what AI observability is and isn't for why that distinction keeps getting blurred.
2. Does it explain why?
A tool that says “Risk: high” isn't enough. An engineer looking at an actual PR needs to be able to act on the finding, which means seeing:
- What changed
- What it affects
- Which dependencies matter
- What historical signals contributed
- Why the change received the result it did
A score without evidence is just a number someone will learn to click past.
3. Can you distinguish agent changes from human changes?
Once AI agents and engineers both contribute to the same repository, Git author information stops being a reliable signal of origin. An agent commit under a human's credentials looks exactly like a human commit.
- Can you identify the origin of a change?
- Can you associate it with a specific agent run?
- Can you see where a human intervened?
- Is that information available at the PR level, not just in a log somewhere?
4. Does it fit the PR workflow you already use?
A governance system that requires developers to open another dashboard on every change has a built-in adoption problem, and adoption problems in governance tooling resolve in exactly one direction.
Ask: what changes about a developer's existing workflow? Ideally the signal appears where the change is already being reviewed — PR checks, merge gates, existing CI workflows, or the code review system the team already lives in.
5. Does it produce an audit trail?
For any given change, you want to be able to answer: what was flagged, why, what evidence supported the finding, what decision was made, who or what made it, and whether the change was eventually merged. “Everything is logged” is not the same as being able to produce that record for a specific PR from six weeks ago.
6. How does it behave when it's wrong?
Every risk system will sometimes flag something that turns out to be fine. That's not a defect; it's the cost of having a gate at all. What matters is the behavior around it.
Ask: does it warn, block, explain, allow an override — and does it record the override? A system that blocks too many harmless changes gets disabled within a quarter. A system that never blocks anything isn't much of a gate. The interesting product decisions all live between those two failure modes.
7. What's the cost of not having it?
The last question isn't about vendors at all. Look at your own pipeline:
- How many AI-generated changes are being merged?
- How much reviewer capacity do they consume?
- How many changes are reverted?
- How many incidents can be traced to a specific change?
- How often does a production issue reveal something that wasn't caught before merge?
If you can't answer those, that's worth knowing before you shortlist anything — the measurement gap on the cost side is usually the same gap.
What a strong answer looks like
| Question | Weak answer | Stronger answer |
|---|---|---|
| When does it run? | “Full visibility into your AI systems” | On the change, before merge |
| Does it explain why? | “We show the logs” | We show the evidence behind the risk |
| Agent vs human? | “You can see the commit author” | Change origin is queryable per PR |
| Workflow fit? | “We integrate with your stack” | It runs in the PR workflow you already use |
| Audit trail? | “Everything is logged” | Here's the decision record for a real change |
| False positives? | “Industry-leading accuracy” | Here's how often we're wrong, and what happens then |
| Cost of not having it? | “AI risk is existential” | Here are the numbers from your own pipeline |
The left column isn't dishonest. It's just answering a different question than the one you asked.
Governance isn't another dashboard
A useful governance layer produces an actual decision: merge, merge with warning, require review, or block. Not a score, not a feed, not a weekly digest somebody skims.
The important part is that the decision is tied to evidence and happens at the point where the team can still do something about the risk. For Tomosu, that means evaluating the production risk of a change before it merges — the same argument made at more length in The Missing Layer in Modern Software Engineering.
Run this checklist against any vendor, including Tomosu. It's a better way to evaluate the category than starting from a feature comparison page.
Frequently asked questions
What's the difference between an AI observability platform and an AI governance layer?
An observability platform tracks what a model or agent did and why — prompts, tool calls, outputs. A governance layer uses that information to make a decision about whether a specific change should merge. Observability is descriptive; governance is a gate. See What Is AI Observability? for the fuller definition.
Do I need a governance layer if I already have Datadog, Arize, or Langfuse?
Probably, if you want the merge decision itself checked. Those tools tell you what happened after a call was made. None of them evaluate whether a specific diff should ship. See LLM Observability Tools Compared for what each one actually covers.
Do I need this if I already use code review automation?
It depends what your current tool does. Most code review automation was built to catch bugs and style issues in human-written diffs, not to evaluate whether an autonomous agent's reasoning was sound. See Code Review Automation Fixed the Wrong Problem for where that gap usually shows up.
What does an AI governance layer cost?
That varies enough by vendor and usage that we're not going to put a number here you can't verify. Check current pricing directly rather than trusting a blog post's numbers, ours included: tomosu.ai/pricing.
Is this the same as AI-generated code pre-screening?
Closely related. Pre-screening is one mechanism a governance layer might use. See why AI-generated code needs a different kind of pre-screening for the mechanics.
Should this run pre-merge, post-merge, or both?
Pre-merge is where it catches a bad change before it costs anything. Post-merge tools are doing root-cause analysis on something that already shipped — a different and still useful job, just not the one this checklist is for. See MTTR Is the Wrong Number to Stare At for that side of it.
Run this checklist against any vendor, including us. If you want to see how Tomosu answers all seven, the platform page has the specifics rather than the pitch. Book a call →
