We don't just provide end-to-end API, we're a SOTA harness where you can bring in your own OpenAI or Claude keys and run it on and also we're the only modular API in the market - we give you the ability to control any part of CUA and charge by the type of call as you can see in https://coasty.ai/docs.
We're SOC2 and HIPAA compliant and have zero data retention policies through our enterprise platform where it's all covered and we don't keep any data that you don't want us to!
With our API, you can also create a very customizable blend of deterministic workflows and AI calls for CUA, so for example, Coasty for recovery can kick in when something unexpected like a dialog box or popup happens during your deterministic workflow runs.
The checkpoint and invariant model is a strong fit for these workflows. Having approval gates plus a replayable event log makes the agent's decisions much easier to audit than a simple end-to-end task API.
The most interesting thing about this is buried in a reply rather than the post: the ability to blend deterministic workflows with AI calls, so the agent kicks in on recovery when a dialog or popup breaks the scripted path.
That's your actual differentiator and I'd lead with it. Right now the post frames it as observe-decide-execute-observe, which reads as "another CUA agent," and the top comment is immediately asking how you differ from the other CUA APIs in the batch. But deterministic-by-default with AI on exception is a genuinely different shape from both of the alternatives you name. RPA breaks when the screen changes; a pure agent is nondeterministic on the 95% of steps that never needed judgment in the first place. Doing the boring path deterministically and paying for a model only when reality deviates is the thing that would make me try this.
I came at the same conclusion from a completely unrelated domain (I build asset generators, no computer-use anywhere near it) and the tool only got good the day I stopped letting the model do the parts that had to be identical every run. Generation is sampling. Same prompt, same model, different run, different result. The skill is knowing which steps genuinely need judgment and refusing to spend a sampling operation on the ones that don't. Sounds like you've built exactly that control surface and then under-sold it.
Genuine question on the handoff, since that's where I'd expect this to get hard: when the deterministic path hits something unexpected and the agent takes over for recovery, how does it hand control back? Does it have to return the UI to a known state that the script recognizes, or does the script resume wherever the agent left off? That reconciliation seems like the interesting engineering, and it's also where I'd expect the failure modes to live.
we run screen-driven agents against web forms in production and the failure mode that took us longest to find wasn't navigation, it was commits that don't commit. a react controlled select can render the right value after a click while the framework's internal state never updated, so every pixel says done and the submitted payload says null. vision-only verification passes because the screen genuinely looks correct.
curious how you handle that class without DOM access. screenshot-after-action catches missing UI feedback, but when the UI itself is lying about form state the only reliable tells we found were downstream: the confirmation page, an outbound request, an email arriving. do your verification events ever consume anything besides pixels, or do you lean on the human approval gates for the risky commits?
Great observation on the "commits that don't commit" failure mode. We ran into the exact same class of bug building document extraction for regulated financial workflows — turns out vision-only verification has a blind spot that shows up whether you're parsing a screen or a PDF.
In tax preparation, documents have a lot of "the screen lies" equivalents: a PDF renderer can display perfectly aligned columns while the underlying text layer has misaligned fields. We learned this the hard way when an OCR pipeline extracted "123 Main St" beautifully from a W-2 image, but the XML metadata had a completely different address. The screen looked right, the data was wrong.
Our approach was a second verification layer structurally independent from extraction: deterministic rules that reconcile AI output against the source document's known structure. For a W-2, that means cross-walking every box number against the IRS's published schema and flagging deviations — same principle as your "downstream tells" but formalized into a rules engine.
The key insight: in regulated domains, the verification layer must consume something structurally different from what extraction consumed. If both use the same pixel-based representation, they share the same failure modes. Using DOM state, API call logs, or document metadata schemas as the verification source catches the cases where "pixel says done, payload says null."
Defining explicit "verification invariants" (similar to Coasty's approach) has been the most practical defense — things like "total across all line items must equal reported total" or "taxpayer name must match across all documents in the return." Simple arithmetic checks that don't need AI but catch the expensive failure modes that vision alone misses.
Curious how Coasty handles multi-source reconciliation — e.g., when the data on screen needs to be verified against a separate document (like a PDF guidance doc) rather than just against itself?
I use computer-use agents daily as an end user (browser automation, even wireless ADB to install builds on my phone), so genuine question: how does your API handle the diffrence between reversible and ireversible actions ?
Clicking around a page is one thing, but submitting a form, sending a message or confirming a payment is another — is there a mechanism for the agent to pause and hand back to the human before those, or is that left entirely to the caller?
Asking cause in my exp that boundary is where trust in these agents is won or lost.
throw03172019 | a day ago
[OP] nkov47 | a day ago
throw03172019 | 23 hours ago
[OP] nkov47 | 23 hours ago
throw03172019 | 21 hours ago
[OP] nkov47 | 20 hours ago
owebmaster | 11 hours ago
[OP] nkov47 | a day ago
jkwang | 12 hours ago
owebmaster | 11 hours ago
localplugins | 11 hours ago
That's your actual differentiator and I'd lead with it. Right now the post frames it as observe-decide-execute-observe, which reads as "another CUA agent," and the top comment is immediately asking how you differ from the other CUA APIs in the batch. But deterministic-by-default with AI on exception is a genuinely different shape from both of the alternatives you name. RPA breaks when the screen changes; a pure agent is nondeterministic on the 95% of steps that never needed judgment in the first place. Doing the boring path deterministically and paying for a model only when reality deviates is the thing that would make me try this.
I came at the same conclusion from a completely unrelated domain (I build asset generators, no computer-use anywhere near it) and the tool only got good the day I stopped letting the model do the parts that had to be identical every run. Generation is sampling. Same prompt, same model, different run, different result. The skill is knowing which steps genuinely need judgment and refusing to spend a sampling operation on the ones that don't. Sounds like you've built exactly that control surface and then under-sold it.
Genuine question on the handoff, since that's where I'd expect this to get hard: when the deterministic path hits something unexpected and the agent takes over for recovery, how does it hand control back? Does it have to return the UI to a known state that the script recognizes, or does the script resume wherever the agent left off? That reconciliation seems like the interesting engineering, and it's also where I'd expect the failure modes to live.
owebmaster | 9 hours ago
sneefle | 10 hours ago
curious how you handle that class without DOM access. screenshot-after-action catches missing UI feedback, but when the UI itself is lying about form state the only reliable tells we found were downstream: the confirmation page, an outbound request, an email arriving. do your verification events ever consume anything besides pixels, or do you lean on the human approval gates for the risky commits?
madikz | 8 hours ago
In tax preparation, documents have a lot of "the screen lies" equivalents: a PDF renderer can display perfectly aligned columns while the underlying text layer has misaligned fields. We learned this the hard way when an OCR pipeline extracted "123 Main St" beautifully from a W-2 image, but the XML metadata had a completely different address. The screen looked right, the data was wrong.
Our approach was a second verification layer structurally independent from extraction: deterministic rules that reconcile AI output against the source document's known structure. For a W-2, that means cross-walking every box number against the IRS's published schema and flagging deviations — same principle as your "downstream tells" but formalized into a rules engine.
The key insight: in regulated domains, the verification layer must consume something structurally different from what extraction consumed. If both use the same pixel-based representation, they share the same failure modes. Using DOM state, API call logs, or document metadata schemas as the verification source catches the cases where "pixel says done, payload says null."
Defining explicit "verification invariants" (similar to Coasty's approach) has been the most practical defense — things like "total across all line items must equal reported total" or "taxpayer name must match across all documents in the return." Simple arithmetic checks that don't need AI but catch the expensive failure modes that vision alone misses.
Curious how Coasty handles multi-source reconciliation — e.g., when the data on screen needs to be verified against a separate document (like a PDF guidance doc) rather than just against itself?
StarPA | 8 hours ago
I use computer-use agents daily as an end user (browser automation, even wireless ADB to install builds on my phone), so genuine question: how does your API handle the diffrence between reversible and ireversible actions ?
Clicking around a page is one thing, but submitting a form, sending a message or confirming a payment is another — is there a mechanism for the agent to pause and hand back to the human before those, or is that left entirely to the caller?
Asking cause in my exp that boundary is where trust in these agents is won or lost.