Claritty vs an AI agent team
Both give you specialists that do recurring work and ask before they act. The difference is what exists afterwards, when you need to show that something happened, or that it did not.
How a typical agent team works
The pitch is now common: describe your business, get a team of agents, connect your tools, and they propose work and act on what you approve. It is a good pitch, and the approval step is genuinely the right shape. What varies is the record. Most platforms show you a run log — a list of steps the agent reports it performed. That is the agent’s own account of its work, and it is written by the same thing that did the work.
Claritty treats the record as the product. A write stores the provider’s own confirmation rather than our claim that we called it, the approval names the person and the platform they approved on, and the ledger is hash-chained so an edit after the fact is detectable. The point is not that the agent is more careful. It is that you are not asked to take its word.
Where they differ
When it says it sent something
The provider’s own confirmation is stored with the run — the message id the service returned. Our record that we made the call is not treated as evidence that it landed.
The run log shows the step succeeded. Whether the message actually arrived is a separate question you answer in the other tool.
Who approved it
Recorded on the action itself, with the platform it was approved on. A run approved from Slack names the colleague who pressed the button, not the account the run executed as.
Typically the account that owns the automation. Which human actually decided, and where, is not always part of the record.
Whether the record can change afterwards
Ledger rows are hash-chained and sealed. Re-checking returns one of three answers: intact, altered — naming the first row that failed and whether rows are missing — or not yet sealed. "We could not check" is never reported as "it checked out".
An activity log you read. There is usually no way to tell whether it says the same thing today as when it was written.
What it did NOT do
A run reports the rows it could not judge and the columns it could not read, with counts, and leaves them out of the answer rather than guessing. An outcome nobody observed is reported as unobserved, not as success.
The answer arrives looking complete. What was skipped, and why, is not usually part of what you receive.
What a single run can cost
A hard ceiling per run, checked before each model call, and the spend is measured from actual usage rather than estimated. An automation cannot quietly become expensive.
Credits, where a complex action costs more than a simple one. Forecasting a month usually takes a month of watching it.
Why Claritty instead
Each of these is something the product does today, not a direction of travel.
The receipt comes from the provider, not from us
When a run sends something, what gets stored is the confirmation the provider returned — the id the service assigned to the message. Our own record that we made the call is not treated as proof it arrived, because those are different facts and only one of them is evidence.
An approval names a person and a place
The run still executes as the owner, because theirs are the only credentials authorised for it. The record names whoever actually decided, and the platform they decided on. Recording the owner for a colleague’s decision is how an approval trail quietly stops meaning anything.
The audit can be checked, not just read
Ledger rows are sealed with a hash chain, so re-checking answers whether it is intact, altered, or not yet sealed. When it reports altered it names the first row that failed and says whether rows are missing. The third answer matters most: not being able to check is reported as exactly that, never as a pass.
Nothing irreversible happens without you
A dry run shows you what it would have sent before it sends it, and a real action waits for your approval. The failure mode of a bad run is a draft you delete rather than an email your customer reads.
It tells you what it could not do
A read that could not judge five rows says so, with the count, and leaves them out rather than answering anyway. A run whose end nobody observed is reported as unobserved. Most of the cost of automation is not the work that fails loudly, it is the answer that looks complete and is not.
Your keys never reach automation code
Credentials are brokered server-side, so the automation calls a tool and the platform holds the secret. There is no key pasted into a step for someone to read later.
Questions people ask
Every agent platform says it asks before it acts. What is different here?
Nothing, at that sentence. Asking first is the right shape and the whole category does it. The difference is afterwards: whether you hold the provider’s own confirmation that the thing happened, whether the record names who approved it and where, and whether anyone could edit that record later without it being detectable. Those are three separate mechanisms, and they are the reason this page exists.
Why does it matter who approved something?
Because the run executes as the account that owns the credentials, which is usually not the person who made the decision. If the record only names the owner, then a colleague approving a payment from Slack and the owner approving it themselves look identical afterwards. That distinction is the entire value of an approval trail, and it is lost by default unless something records it.
Is a tamper-evident ledger not overkill for a small team?
For a weekly digest, yes. For anything that moves money, contacts a customer, or has to be explained to someone later, the question is not whether you trust your automation but whether you can show what it did. The cost of having it is zero until the day you need it, which is the only day it is worth anything.
Should I use an agent team from the platform my business already runs on?
Often yes, and it will be easier to start with — it already knows your business because it hosts it. That is a real advantage and worth taking. It is worth asking the same questions of it that this page asks: when it tells you it sent something, what is that based on, and can you still show it in six months.
Describe the job. Watch it run.
Building an automation is free, and it runs on your real data so you can see whether it actually works before you pay for anything.
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