CANARY: Why Long AI Conversations Need a Warning System 

by K. Kieth Jackson

There’s a reason the phrase “canary in a coal mine” still exists.

Before modern sensors, miners carried canaries underground. As long as the bird was fine, the air was safe. If the canary showed distress, work slowed. If the canary stopped singing, everyone left.

The canary didn’t fix the problem.
It made danger visible early enough to act.

That’s exactly the role CANARY plays in long-running AI conversations.

The Problem That Appears After Memory Is Solved

Most people think AI fails because it forgets things.

And yes  memory across sessions is a real problem. But once that’s solved, something more subtle shows up.

Even within a single conversation , no resets, no missing context,  AI reasoning slowly degrades.

Plans lose structure.
Definitions drift.
Rules soften.
Assumptions sneak in.

Nothing crashes. Nothing obviously breaks.

The system keeps talking.

By the time failure is visible, the reasoning has already collapsed.

That’s in-session drift, and it’s harder to catch than forgetting.

Why Drift Is So Dangerous

In short conversations, drift is annoying.

In long ones, it’s catastrophic.

When reasoning stretches across dozens of turns:

• small inconsistencies compound,

• early assumptions become foundations,

• and subtle errors propagate into everything that follows.

The most dangerous part?
Drift is silent.

Outputs stay fluent. Confidence often increases. The system sounds like it knows what it’s doing ....even when it doesn’t.

This is why people often say:

“It was working fine… until suddenly it wasn’t.”

Why Existing Fixes Don’t Catch It

Bigger context windows help memory, not integrity.

Retrieval helps recall facts, not whether reasoning still makes sense.

Alignment improves behavior, not multi-step coherence.

Token counts don’t predict drift reliably.

All of these address what the model knows , not how well its reasoning is holding together right now.

What was missing was a runtime warning system.

What CANARY Actually Does

CANARY doesn’t change the model.
It doesn’t inspect hidden layers.
It doesn’t score probabilities or predict hallucinations.

Instead, it enforces one simple rule:

Before continuing, reasoning must declare its own health.

Every response begins with a visible state:

🟢 GREEN — Reasoning appears stable. Safe to continue.
🟡 YELLOW — Early warning. Slow down, verify assumptions.
🔴 RED — Critical drift detected. Stop and repair before continuing.

That signal is the canary.

Not perfect. Not magical. But visible.

Why Visibility Changes Everything

Once drift becomes explicit, the failure pattern changes.

Instead of:

• discovering problems after damage spreads,

• rebuilding large sections of work,

• or guessing where things went wrong,

you get:

• early warning,

• controlled pauses,

• and intentional recovery.

CANARY doesn’t decide what to do next.
It tells humans when judgment is required.

That distinction matters.

Why CANARY Has Layers

One canary isn’t enough in every mine.

Some environments are stable.
Some are deep, complex, and dangerous.

CANARY evolved in layers because real-world use demanded it.

CANARY-Signal

Minimal warning. Almost no overhead.
Just makes drift impossible to ignore.

CANARY-Containment

Adds recovery rules.
When reasoning turns RED, work halts, state is anchored, and damage stops spreading.

CANARY-Orchestration

For long projects and multi-model work.
Adds versioning, replay, audit trails, and strict governance.

Each layer exists because the previous one eventually proved insufficient.

A Key Insight: Drift Isn’t About Intelligence

Different AI models drift differently.

Some are conservative and self-correct early.
Others confidently invent details under pressure.

This isn’t a flaw. It’s behavior.

CANARY adapts to that reality instead of assuming all models behave the same.

Runtime integrity has to be calibrated ....not guessed.

What Changed in Practice

Before CANARY:

• drift was frequent and unpredictable,

• recovery was expensive,

• and long sessions felt fragile.

After CANARY:

• drift became visible,

• recovery became fast and deterministic,

• and long-running work became routine instead of risky.

Most importantly, human effort shifted from constant vigilance to intentional decision-making.

That’s the difference between babysitting a system and working with one.

Why the Name Fits

A canary doesn’t prevent danger.

It prevents silent danger.

CANARY doesn’t promise perfect reasoning.
It promises that when reasoning starts to fail, you’ll know — early enough to act.

In long-running AI work, that’s the difference between experimentation and engineering.

This post is adapted from the paper CANARY: A Runtime Integrity Architecture for Detecting and Containing Drift in Long-Running LLM Reasoning (2025).