Why infinite context windows aren't the answer. How Elani's Budgeter uses intelligent constraints to improve agent reasoning and reduce noise.
There is a prevailing myth in the AI industry: "If we just make the context window bigger, the model will understand everything."
We've seen context windows grow from 4k to 128k, to 1 million tokens and beyond. The promise is seductive—feed your entire company's history into the model, and it will perfectly recall that one Slack message from three years ago.
But in practice, this fails. It fails for the same reason a human executive fails when you dump 10,000 unread emails on their desk: Information Saturation.
When you flood an LLM with noise, its reasoning capabilities degrade. It gets "distracted" by irrelevant details. It hallucinates connections that aren't there.
At Elani, we take a different approach. We don't just dump data into the model. We budget its attention.
Every time an AI agent "reads" an email, two costs are incurred:
To manage this, we built a core component called the Budgeter, part of our Ingestion V2 pipeline.
Most simple RAG (Retrieval Augmented Generation) systems work like a vacuum cleaner: suck up everything, index it, and hope for the best.
Elani's ingestion pipeline acts more like a triage nurse. It consists of distinct stages designed to filter noise before it reaches the expensive reasoning layers.
First, our scheduler-worker triggers a scan. We fetch metadata—headers, subjects, timestamps—but not the full body content. This is cheap and fast.
This is where the magic happens. The ExtractionGate stage uses lightweight heuristics and cheaper models to ask a simple question: "Is this item likely to contain value?"
It looks at:
The Budgeter enforces strict limits. Even if 500 emails look "interesting," we might only have the "budget" (time/compute) to deeply process the top 50 right now.
The logic resides in packages/shared-utils/src/orchestrators/ingestion-v2/budgeter.ts. It prioritizes items based on urgency and importance, ensuring that we process the critical few rather than the mediocre many.
// Conceptual logic of the Budgeter
if (candidate.score < threshold || currentSpend > dailyBudget) {
return "defer"; // Save for later or discard
}
return "process";
Only when an item passes the Gate and the Budgeter do we spend the resources to fetch the full body and run our ExtractionClassifier. This deep-read extracts entities, dates, and action items.
By aggressively filtering the input, we achieve something counter-intuitive: Better output.
Because Elani is only reasoning about high-signal data, her "mental workspace" is clean. She connects the dots between the CEO's email and the Q3 roadmap because she isn't distracted by the 4,000 automated notifications in between.
As we move into 2026, the challenge for AI won't be accessing information—it will be ignoring it.
We are building Elani to be the best at ignoring the noise, so she (and you) can focus on the signal.