Project summary

This project develops and productizes an auditable control layer for black-box AI and automation systems that are prone to looping, instability, and silent failure under load.

Modern AI agents and enterprise automations increasingly operate as opaque systems. When they enter unstable regimes, such as unproductive loops, escalating retries, or degraded decision quality, organizations often lack early detection, safe intervention mechanisms, and post-incident traceability. This creates operational, financial, and safety risk, especially in environments where delayed or incorrect actions can propagate harm.

We address this gap by building sensor-driven control systems that operate outside the underlying model. The system detects regime changes using calibrated structural signals, intervenes with bounded and reversible actions, and generates an auditable run report capturing what was sensed, what actions were taken, and why.

The architecture has been validated in a demanding industrial-scale analog: structured SAT search. This environment exhibits nonlinear dynamics, long stall regimes, and high sensitivity to control errors, making it a strong testbed for black-box safety mechanisms. In controlled experiments across structured and noisy regimes, our system delivered order-of-magnitude stability improvements in hard cases while preserving perfect performance in easy regimes. Crucially, interventions were budgeted, safe, and fully traceable, with no silent failures.

Funding will be used to harden this proven control primitive into a deployable SDK, integrate it with real AI agent and automation workflows, and run pilot deployments in shadow and gated-intervention modes. The outcome will be a practical AI safety tool that reduces loop risk, enforces bounded corrective action, and provides audit-grade visibility into black-box system behavior.

What are this project's goals? How will you achieve them?

Goal 1: Productize a proven safety control primitive for black box AI systems

What this means
Turn a validated research prototype into a reliable, deployable control layer that can wrap real AI agents and automation workflows without modifying the underlying model.

How we will achieve it
We will package the existing sensor intervention report loop into a modular SDK with explicit configuration, invariants, and acceptance tests. This includes formalizing the event schema, intervention budget logic, and audit report generation so the system behaves deterministically and fails loudly rather than silently.

Goal 2: Enable early detection of looping and instability in real AI workflows

What this means
Detect unproductive cycles, escalating retries, and regime shifts before they cause runaway cost, degraded outputs, or unsafe actions.

How we will achieve it
We will adapt the calibrated structure sensors validated in structured SAT search to enterprise AI and automation contexts by mapping equivalent signals such as retry depth, tool churn, state divergence, and latency plateaus. Sensors will be calibrated using early run baselines and frozen to avoid drift and collapse, preserving reliable regime separation.

Goal 3: Demonstrate bounded and safe intervention without degrading normal performance

What this means
Show that corrective actions can improve hard cases while leaving easy cases untouched, a core safety requirement.

How we will achieve it
Interventions will be strictly budgeted, reversible, and customer approved. The system will operate first in shadow mode to validate detection quality, then in gated intervention mode where actions only fire under high confidence conditions. Safe fallbacks ensure that blocked or infeasible actions degrade gracefully rather than freezing or escalating.

Goal 4: Produce audit grade run reports suitable for review and compliance

What this means
Ensure every detection and intervention is explainable after the fact, supporting incident analysis, governance, and regulatory review.

How we will achieve it
Each scheduled intervention will resolve to a terminal logged outcome capturing the sensed signal, decision policy, selected action, and execution result. Reports will be generated per run and aggregated over time to support operational review and safety trend analysis.

Goal 5: Validate transfer from industrial analogs to real AI systems

What this means
Demonstrate that the architecture generalizes beyond SAT search to practical AI agents and automation pipelines.

How we will achieve it
We will run pilots with two to three design partners, integrating the control layer with one AI agent workflow and one automation pipeline. Results will be measured in terms of reduced looping, bounded intervention behavior, and improved diagnosability rather than task performance gains.

Why these goals matter for AI safety

Together, these goals address a core safety gap in modern AI deployment: the lack of reliable, bounded, and auditable control over black box behavior. The project focuses on reducing operational risk through early detection, constrained corrective action, and forensic visibility, not on increasing model capability.

How will this funding be used?

Funding will be used to convert a validated safety control prototype into a deployable, auditable system and to demonstrate its effectiveness in real AI and automation workflows.

1. Productizing the safety control layer

A portion of the funding will support engineering work to harden the existing sensor intervention report loop into a production ready module. This includes formalizing configuration interfaces, defining invariants and acceptance tests, and packaging the system as a reusable SDK that can wrap black box AI agents and automation pipelines without modifying their internal logic.

This work focuses on reliability, determinism, and failure transparency rather than performance optimization.

2. Adapting sensors to real world AI workflows

Funding will support the translation of calibrated structure sensing from the industrial analog environment into enterprise AI and automation contexts. This includes identifying equivalent signals such as retry depth, tool churn, state divergence, and latency plateaus, and validating that baseline calibration and freeze mechanisms prevent sensor drift and collapse in live systems.

The goal is reliable regime detection that works early and degrades safely.

3. Implementing bounded and gated intervention mechanisms

Resources will be allocated to implementing customer approved intervention ladders with explicit budgets and safe fallbacks. This includes shadow mode operation, gated activation under high confidence conditions, and enforcement of strict limits so interventions cannot escalate or loop.

This ensures that corrective actions reduce risk without introducing new failure modes.

4. Building audit grade reporting and review artifacts

Funding will be used to build run report generation, aggregation, and review tooling. Each scheduled intervention will resolve to a logged terminal outcome capturing sensed signals, decisions, and execution paths. Reports will support post incident review, governance, and compliance use cases.

This directly addresses explainability and accountability gaps in black box AI deployment.

5. Pilot deployments and validation

Finally, funding will support pilot deployments with two to three design partners. These pilots will run first in shadow mode and then in gated intervention mode to validate loop reduction, bounded behavior, and improved diagnosability in real workflows. Outcomes will be measured in safety and reliability terms rather than task performance gains.

Summary

Funding is used to reduce AI deployment risk by delivering a practical safety control layer that detects instability, intervenes conservatively, and produces audit grade evidence. The work prioritizes robustness, bounded action, and transparency over capability expansion.

Who is on your team? What's your track record on similar projects?

Team and Track Record

This project is led by a two-person team with clearly separated but complementary technical and strategic roles, combining deep hands-on safety experience with disciplined system design and deployment focus.

Jeff Butcher — Technical Lead and Research Owner

Jeff leads all technical research, system design, implementation, and validation. He is responsible for the regime sensing architecture, calibration methods, bounded intervention logic, robustness guarantees, and experimental validation.

Jeff’s background includes extensive experience in medical response, hazardous materials, and emergency operations, environments where failure modes must be anticipated, actions must remain bounded, and outcomes must be documented clearly. In this project, he designed and debugged the full sensor-driven control loop and validated it in a demanding industrial analog, structured SAT search. This work included identifying and fixing classic failure modes such as sensor baseline collapse, brittle thresholds, and silent actuation errors.

The existing results and proofs described in this proposal are directly attributable to Jeff’s technical work and hands-on debugging discipline.

Dr. Ron Spradling — Systems Strategy, Safety Framing, and Commercialization

Ron provides systems-level design input, adversarial brainstorming, safety framing, and commercialization leadership. His role is to pressure-test assumptions, translate technical mechanisms into defensible safety claims, and ensure the system is designed for real deployment constraints such as governance, auditability, and customer adoption.

Ron’s background includes military aviation structural maintenance and safety-critical operations, where procedural rigor, fault tolerance, and clear accountability are essential. He focuses on ensuring that interventions remain bounded, failure modes are surfaced early, and system behavior can be explained after the fact. Ron also leads pilot structuring, stakeholder alignment, and the transition from validated prototype to deployable safety infrastructure.

Shared safety culture and operating discipline

Both team members bring hands-on experience from high-stress, high-consequence operational domains. Jeff’s experience in medical and emergency response and Ron’s experience in aviation maintenance contribute a shared discipline around failure analysis, procedural rigor, and operating under conditions where errors must be contained rather than masked.

This shared safety culture directly informs the project’s design choices. The system assumes failures are possible, constrains responses through explicit budgets, provides safe fallbacks when preferred actions are infeasible, and produces audit-grade traces for every scheduled intervention. The architecture reflects how real-world safety systems are practiced: detect early, intervene conservatively, and leave a clear forensic trail.

Why this team is well suited for the project

This project is not exploratory research. The core technical risks have already been encountered and resolved in a harsh test environment where shortcuts fail quickly. The team has demonstrated the ability to design, debug, and validate safety mechanisms end to end, and now focuses on careful productization and real-world deployment rather than speculative development.

What are the most likely causes and outcomes if this project fails?

Likely Cause 1: Regime sensing does not transfer cleanly to some real-world workflows

What could go wrong
Signals that cleanly separate regimes in the industrial analog may be noisier or less stable in certain enterprise AI or automation workflows, especially those with sparse events or weak feedback structure.

Outcome if this occurs
The system may fail to reliably distinguish between productive and unproductive behavior early enough to justify intervention.

Why this is contained
The system is designed to degrade safely. In cases of low confidence, it operates in detection-only or reporting mode without intervention. This still delivers audit and diagnostic value without introducing new risk.

Likely Cause 2: Over-conservatism limits intervention effectiveness

What could go wrong
To avoid unsafe actions, intervention thresholds and budgets may be set too conservatively during early deployments, limiting measurable improvements in stability.

Outcome if this occurs
Safety gains may be modest in initial pilots, even though the system behaves correctly.

Why this is acceptable
This is an intentional safety bias. Conservative behavior validates boundedness and non-interference first. Thresholds can be adjusted incrementally once confidence is established, without architectural changes.

Likely Cause 3: Integration friction in real environments

What could go wrong
Enterprise AI systems and automation pipelines vary widely in observability, instrumentation, and governance constraints, which can slow integration or limit accessible signals.

Outcome if this occurs
Pilot deployments may take longer than expected or require narrower initial scope.

Why this is manageable
The control layer is designed as an external wrapper, not a deep integration. Shadow mode operation allows value delivery through detection and reporting even when intervention is delayed.

Likely Cause 4: Audit and reporting requirements exceed initial assumptions

What could go wrong
Early users may require more detailed or differently structured reports to meet internal governance or regulatory needs.

Outcome if this occurs
Additional engineering effort may be needed to extend reporting formats and aggregation logic.

Why this is low risk
Reporting is already a first-class design goal. Extending report structure does not affect sensing or intervention safety and can be iterated independently.

Worst-case outcome and why it is bounded

Worst-case scenario
The system fails to deliver reliable intervention benefits in certain domains and remains a detection and reporting tool rather than an active control layer.

Why this is still a useful outcome
Even in this case, the project delivers value by improving diagnosability, transparency, and auditability of black-box AI behavior. It does not increase system capability or risk and does not introduce unsafe actions.

What failure does NOT look like

• No uncontrolled interventions

• No hidden or silent failures

• No degradation of normal system behavior

• No amplification of risk

Failure modes are visible, logged, and conservative by design.

Why this answer matters for AI safety

This project treats failure as a safety design input. Likely failure modes are anticipated, constrained, and surfaced early. Even in failure, the system reduces risk by improving visibility and accountability rather than increasing automation authority.

How much money have you raised in the last 12 months, and from where?

We have not raised external funding in the past 12 months.

The work described in this proposal was developed and validated without outside capital. Early results were achieved through focused technical execution rather than funded scale. We are now seeking initial non-dilutive or early capital to productize the validated safety control architecture and run pilot deployments.