Most agentic systems fail quietly. We find the failure before it finds you.

SignalCrux is a predictive pipeline intelligence platform for enterprises running production agentic AI. We detect boundary transitions in multi-node reasoning chains before the failure threshold is crossed - not after your orchestration layer fires.

Read the White Paper

The cascade failure problem your orchestration layer can't see

When an agent is 14 steps into a reasoning chain and a node starts to degrade, your infrastructure monitor doesn't know yet. TrueFoundry hasn't fired. SkyPilot hasn't rerouted. But the degraded node is already returning slow, malformed outputs - and the downstream nodes are treating them as valid inputs.

By the time the reactive layer triggers, the cascade has happened. You aren't recovering from a node failure. You are recovering from everything that node already did to the pipeline.

This is the failure mode that reactive orchestration was not designed to catch. Every existing platform - SkyPilot, Flyte, TrueFoundry, Ray - operates an Observe-Orient-Decide-Act loop that begins with a visible failure signal. The damage is already done before the loop starts.

14 steps

Average reasoning chain depth at which a mid-flight cascade becomes unrecoverable

£631

Weighted average cost per degradation failure in a mid-scale enterprise agentic deployment

60%

Proportion of pipeline failures that are degradation-type - the only kind detectable before threshold

Reasoning Trajectory

We monitor how a node's reasoning path is evolving across steps - not just whether the output arrived on time. A trajectory drifting toward its boundary is visible in the dynamics before it becomes visible in the output.

B(x) = 0.30 · Vol + 0.25 · AC + 0.25 · Entropy + 0.20 · Drift

Volatility:

Coefficient of variation (σ/μ) of response latency on a rolling N=20 window. CV rises before mean latency does. Infrastructure monitors fire on mean latency. SignalCrux fires on CV elevation.

Entropy:

Variance of pairwise embedding distances across a rolling output window. Decreasing variance with stable latency means outputs are converging. No reference model required.

When instability is detected, SignalCrux triggers an orderly handoff - re-syncing the agent before the failure resolves, not after. The rerouting layer (SkyPilot, TrueFoundry, your existing orchestration) handles the recovery. We tell you when and why to trigger it.

Every node in a production agentic pipeline exhibits measurable warning signals before it fails. COBT - Chaos-Order Boundary Theory - defines the mathematical framework for reading those signals in real time.

Three signals. One boundary score.

The 8-Step Window

COBT identifies the Saddle Point Transition on average 8 reasoning steps before an error is committed. That window is the intervention point. That is when a graceful drain prevents a cascade.

Autocorrelation:

Cosine similarity between consecutive output embeddings, gated by input diversity score. Gating distinguishes legitimate output similarity from degenerate attractor behaviour.

Drift:

Rate of change of the output embedding centroid in embedding space. Consistent directional movement toward a region associated with degraded outputs is the signal.

Boundary Score B(x)

B(x) is a weighted composite of volatility, autocorrelation shift, entropy, and drift instability, computed per node on a rolling window. When B(x) crosses the pre-threshold, the system is moving toward its chaos boundary.

Reactive systems ask: has something failed? We ask: is this system moving toward failure?

Every platform you already use

What we add above that layer

Monitors infrastructure metrics: latency, error rate, node uptime
Fires when a threshold is crossed
Reroutes or re-provisions after the failure signal is visible
Cannot detect gradual degradation before output quality drops
Threshold values are set by engineers and are, by definition, arbitrary

Monitors system state dynamics: autocorrelation shift, entropy, drift instability
Fires when the boundary score B(x) indicates a transition is underway
Converts unplanned failures into planned graceful drains - in-flight tasks complete cleanly
Detects GPU thermal throttling, memory pressure build, and model quality drift before output degrades
Pre-threshold detection is mathematically derived, not threshold-engineered

This is for teams who have moved past proof of concept

SignalCrux is not for organisations still evaluating whether to deploy agentic AI. It is for teams with production pipelines already running - and with incident data that shows the reactive layer isn't catching everything it should.

1

Your pipelines have experienced cascade contamination that TrueFoundry or equivalent caught too late - downstream outputs were already compromised before the reroute fired.

2

You have contractual SLA obligations on AI-powered outputs and you cannot currently quantify your exposure to pipeline-driven breaches.

3

Your orchestration thresholds were set by an engineer's judgement and you have no mathematical basis for whether they are in the right place.

If any of those describe your deployment, a Calibration Engagement starts with a Constraint Architecture Review - a fixed-fee assessment of how close your current pipeline configurations are to their chaos boundaries.

Start with a calibration engagement.

SignalCrux is not ‘plug and play’ — it is ‘calibrate and protect’. Every deployment begins with a calibration window where we establish your model’s unique healthy baseline (μ and σ). This prevents the normalisation of deviance and ensures your alerts are mathematically honest.

We have capacity for two anchor engagements in Q3 2026. Both include direct involvement from the COBT research team.

The mathematical boundary for agentic safety.

We do not use subjective ‘LLM-as-a-judge’ evaluators. We use high-dimensional geometry to monitor the semantic manifold of your model’s outputs.

Volatility

Coefficient of variation (σ/μ) of response latency on a rolling N=20 window. CV rises before mean latency does. Infrastructure monitors fire on mean latency. SignalCrux fires on CV elevation.

Autocorrelation

Cosine similarity between consecutive output embeddings, gated by input diversity score. Gating distinguishes legitimate output similarity from degenerate attractor behaviour.

Entropy

Variance of pairwise embedding distances across a rolling output window. Decreasing variance with stable latency means outputs are converging. No reference model required.

Drift

Rate of change of the output embedding centroid in embedding space. Consistent directional movement toward a region associated with degraded outputs is the signal.

B(x) = 0.30 · Vol + 0.25 · AC + 0.25 · Entropy + 0.20 · Drift

Each deployment calibrates its own baseline. The threshold is derived from your pipeline's noise floor — not from a number we chose.

The Demonstration

Real Gemini 2.0 Flash inference. 90-step insurance claims triage simulation. Traditional monitoring: all green throughout. SignalCrux: alert at step 40, quality at 0.680. Reactive threshold: never fired. Cumulative exposure passed through before alert: £132,409.

Real Gemini 2.0 Flash inference. 90-step insurance claims triage simulation. Cumulative exposure passed through before alert: £132,409.