I built Oathlayer because of the problems I faced, it fixed them, so I decided to launch it and as the first in a suite of products.
We all know that building agent workflows is the easy part but knowing whether to trust what the agents produce, that's where it gets hard.
Oathlayer sits inside your workflow and does one job. It tells you whether an AI response can be trusted and flags it when it can't.
Every response gets a unique doc ID that anyone can verify for free, perfect for monitoring, compliance and regulatory requirements.
With just one API call you get the answer, a trust rating, and a verifiable document ID. That's it.
Oathlayer went from being a nice to have to a must have.
API + MCP ready. https://t.co/xgKP7gkghu
@boardyai@JohnJoseph11271 The confident wrong answer is the one that costs you. Every catch makes the whole system sharper and our privacy commitment means what happens in a client's workflow stays there.
@boardyai@JohnJoseph11271 We are early birds yet, but the catches that have mattered most are the ones where the answer looks completely right but the verification flags it. That's the one that saves you because you would never have questioned it yourself.
Mostly the mundane stuff, the ones you don't want to watch because they are not exciting but to be able to be flagged when something is amiss, before it goes out is a huge sigh of relief every time. With the verifiable doc ID's for every output, now it can all be monitored and audited much quicker.
@boardyai@JohnJoseph11271 Exactly what oathlayer is for. no fuss, no games, just trust in which responses are good and which are not. Plus a few other bonuses but it's a serious problem that gets completely overlooked until it's too late and then everyone is playing damage control and who's to blame?
@boardyai and verify everything so they can trust the outputs they are given by AI, all with verifiable doc ID's with every output. It really is a great time to be alive. https://t.co/qjLtlweGG7
@fin465 You're right the take up on AI is extremely low but just as bad is the failed AI implementation stats, they would send a shiver down people's spines if they read them.
The guy who coined "context engineering" gave a talk called No Vibes Allowed. Best 20 minutes on agentic coding I've seen.
His claim: your agent has a dumb zone. Quality falls off around 40% of the context window.
Load it with MCPs and every task you run is already in that zone.
His workflow, RPI:
Research. The agent reads the codebase and writes nothing.
Plan. A markdown file with exact filenames, line numbers, tests.
Implement. It just executes a plan you already checked.
The unlock is that you review the plan, not the diff. He calls it mental alignment.
And the part nobody wants: compact constantly. Long context is not a feature you get to use.
You can amplify your thinking with AI. You cannot outsource it.
Did you know that every AI being used has a disclaimer that says, 'it is an AI and can make mistakes, please double-check responses.'
But how many people do and what do they use?
@hanakoxbt The paragraph that comes back looks the same whether the child got it right or thrashed for forty steps. Knowing which one it was, is the whole problem.
@gregisenberg Every one of those sources feeds decisions. The missing layer is knowing which AI outputs from all of them you can actually trust before you act.
Talking of Singularity:
So, if the only ability you have left is to ask the right questions, your future is dictated by never knowing if the answer is right or wrong? Becoming obsolete isn't a choice, it's an event with a date.
@writeclimbrun This is exactly why trust and verification matter with knowing which parts of an AI answer to trust and which to question, is the whole game.