Structural safety for autonomous AI
Your AI agent has access to your email, your files, your calendar. We build the structural guarantees that stop it doing something irreversible without your informed consent.
Threshold Signalworks builds safety infrastructure for autonomous AI systems. Not prompts that ask the model to be careful. Architecture that makes catastrophic mistakes mechanically harder. Guardrails that survive context compaction. Audit trails that prove what happened.
Structural guardrails for autonomous agents. Classifies actions by risk, requires structured approval before anything irreversible, logs every action to a hash-chained audit trail. Constraints live on disk, not in context, so they survive compaction. Available now as an OpenClaw skill.
Behavioural evaluation and drift detection for language models. Measures instruction fidelity, hallucination rates, and safety compliance across model versions and workflow changes. Reproducible artefact packs with full provenance.
Confidence scoring and stabilisation for model outputs. Detects premature convergence, flags low-confidence reasoning, and provides calibrated uncertainty signals that integrate with Keel's risk assessment pipeline.
Persistent safety for teams. Syncs policies and audit logs across multiple agents, provides a web dashboard for reviewing agent activity, and produces compliance-ready audit exports. EU-hosted, GDPR-native.
Threshold Systems is the research arm of Threshold Signalworks. We study how instability enters during inference, tool use, and autonomous workflow execution, and we build measurement and intervention tools grounded in that understanding.
Current work spans AI evaluation protocols, cognitive architecture under constraint, and human decision-making in high-uncertainty environments. Publications and artefact packs are released through threshold.systems.
Public artefact packs (evaluation runs, reports, provenance chains) will appear here as they are released.