Introducing Presigate: The Pre-Flight Decision Gate for AI Agents
Autonomous AI agents can now spend your money, close your trades, and trigger your workflows. We built Presigate because none of them had a reliable way to check whether conditions were right before they acted.
Autonomous AI agents can now spend your money, close your trades, and trigger your workflows. They do not wait for you to approve each step. That is the point of them — you set the objective, they execute.
The problem is that execution without condition-checking is not automation. It is a liability.
The Gap Nobody Is Talking About
Think about how a good human operator behaves before taking an action with real consequences. They check conditions. Is this the right moment? Has the underlying situation changed since the last time I looked? Are the inputs I am relying on actually clean, or has something upstream gone stale?
That pre-flight check — that brief, structured pause before action — is what keeps discretionary judgment inside the loop without requiring humans to approve every step.
Current AI agent frameworks do not have a native equivalent. They can reason. They can plan. They can call APIs and execute transactions with speed and precision no human team can match. But they have no reliable mechanism for verifying that the conditions warrant the action before they take it. They act on the state of the world as of the last input they received. If that input is wrong, stale, or taken out of context for the current environment, the agent still acts.
This is not a failure of intelligence. It is a structural gap in the infrastructure.
The Oracle Analogy
There is a concept in systems design — developed in the context of smart contracts but applicable far more broadly — sometimes called the oracle problem. Any system that acts on external state has to get that state from somewhere. The integrity of the action depends entirely on the integrity of the input. You can build a perfectly sound decision engine and still produce bad outcomes if the data feeding it is wrong, manipulated, or simply outdated.
The solution was a class of infrastructure called oracles: dedicated, independent services whose job is to attest to real-world conditions so that systems acting on those conditions can trust what they are reading.
Agents need something analogous. Not a data feed, exactly — something more opinionated than that. A service that synthesizes conditions into a gate signal: act, or wait.
That is what we built.
What Presigate Is
Presigate is the pre-flight decision gate for AI agents.
Before an agent takes a consequential action — executing a trade, deploying capital, triggering a workflow, closing a position — it queries Presigate. Presigate synthesizes a set of market and data-integrity conditions and returns a gate signal: conditions are favorable, or they are not.
The agent does not need to understand the underlying analysis. It reads the gate.
The architecture underneath it is built around what we call five primitives. Four are inputs: RXI™ (regime), MMI™ (microstructure), CSI™ (sentiment), and TVI™ (data integrity). The fifth, GSI™, is the synthesized gate output — the signal that agent logic actually acts on. Each input primitive answers a specific structural question about whether conditions warrant action. Together they produce a gate that is harder to fool than any single signal.
The acronyms are not the point. The point is that a human operator, before acting, would implicitly ask all five of those questions. Presigate makes that check explicit, fast, and callable.
Where We Are
I want to be direct about the stage we are at, because I think honesty about that is more useful than a polished launch narrative.
Presigate is early. The underlying primitives have been in development through CoinRoc, our grid trading analysis platform, where regime detection and condition gating were not optional features but core operating requirements — you either knew whether the market warranted action or you did not, and the cost of getting it wrong was measurable. That work gave us a validated foundation.
We are now extending that foundation into a standalone service with a clean API, testing the gate signal out-of-sample against conditions we did not train on, and measuring honestly. We are not in a position to publish a full performance record yet, because we do not have enough out-of-sample data to make that record meaningful. We will publish it when we do.
The whitepaper at presigate.com/whitepaper covers the methodology in technical detail — the primitives, the synthesis logic, the validation approach, and what we are measuring. If you want to understand how the gate works rather than just what it does, start there.
If you want to see the product and try the demo, start at presigate.com.
Why This Matters Now
The timing is not accidental. Agent frameworks have matured to the point where deploying autonomous execution is no longer a research exercise — it is a product decision. The number of pipelines running agents against live state, with real consequences, is growing fast.
What has not kept pace is the infrastructure layer that makes that execution trustworthy. Right now, most agent deployments handle condition-checking the same way they handle everything else: they ask the model. The model does its best. But reasoning about conditions is not the same as measuring them, and a model’s best guess about whether market microstructure is healthy is not a substitute for a dedicated service that reads and synthesizes that directly.
The pre-flight check belongs in the infrastructure layer. Not as a prompt. Not as a chain-of-thought. As a gate.
That is the thesis. We are building the infrastructure.
Presigate is in early development. Nothing in this post constitutes investment advice or a guarantee of system performance. RXI™, MMI™, CSI™, TVI™, and GSI™ are trademarks of Yodacom LLC, trademark registration pending.