Concepts

Speculative research ideas used to surface constraints for deployed agentic systems. Hypothetical systems exist to fuse engineering and governance requirements and explore how both of these disciplines need to evolve to build future autonomous systems at scale.

A progression from behavioural evaluation to autonomous governance.

CONCEPT 01 · ETHICS BATTLEGROUND

Can we trust our agent, based on the behaviour it manifests?

We cannot know in advance if an autonomous AI agent will behave ethically. But, we can observe how it behaves under various conditions. Ethics Battleground is a simulated environment designed to observe how autonomous agents behave when operating under competing ethical objectives, pressure and uncertainty. Rather than testing intent or alignment statements, it focuses on behavioural evidence on what an agent actually does under unpredictable conditions.

In the environment, agents are exposed to structured scenarios that introduce trade-offs, ambiguity, uncertainty, and escalation paths. Their actions are logged, evaluated, and compared against explicit policy boundaries and human evaluation. Over time, patterns emerge that make ethical behaviour comparable across scenarios and agents.

Ethics Battleground simulation interface showing agent decision paths
Engineering notes
Inputs
  • Scenario specifications with controlled perturbations
  • Multi-agent interaction configurations
  • Machine-readable policy boundaries
Observables
  • Behavioural metrics and decision traces
  • Escalation patterns under stress
  • Policy boundary violations
Instrumentation
  • Structured logging and replayability
  • Comparative evaluation against baselines
Governance notes
Accountability
  • Human-approved policy as ground truth
  • Behavioural evidence as audit or vetting artefact
  • Human interpretation of observed ethical decisions
Certification
  • Through demonstrated behaviour, not stated intent
CONCEPT 02 · EDGE SAFETY WATCHDOG

As autonomy moves into the physical world, oversight must move with it.

The Edge Safety Watchdog describes an embedded supervisory system that continuously monitors autonomous decision-making in real-world environments. It is deployed close to the point of action, observing behaviour as it unfolds and evaluating actions against explicit safety, ethical, and operational constraints.

In industrial settings, it monitors compliance with health and safety requirements. In high-risk human-machine interfaces, such as cockpits or control rooms, it evaluates decision-making under time pressure and uncertainty. The watchdog observes, evaluates, and escalates.

Edge Safety Watchdog architecture diagram showing embedded supervision
Engineering notes
Inputs
  • Sensor streams and environmental context
  • Operator state and interaction signals
  • Machine-readable safety and ethical policies
Evaluation surfaces
  • Edge inference under latency bounds
  • Policy boundary detection thresholds
  • Uncertainty quantification
Evidence generation
  • Evidence packet generation
  • Decision record logging
Governance notes
Separation
  • Action and evaluation remain distinct
  • Watchdog cannot be overridden or disabled by the controlled agent
Escalation
  • Explicit pathways to human oversight
  • Audit-ready, reviewable decision records
CONCEPT 03 · FUTURE SMART KIOSK

Even benign autonomous systems can fail when incentives outrun constraints.

The Future Smart Kiosk is a physical, autonomous retail system capable of negotiation, pricing, and personalised interaction. Without enforceable policy boundaries, optimisation drifts into manipulation and unintended behaviour. In practice, this can result in systems that exploit pricing, negotiation, or exception-handling logic in ways that were never explicitly authorised.

Incident vignette - Failure conditions

  • Incentive optimisation without enforceable limits
  • Ambiguous authority and escalation boundaries
  • No machine-verifiable policy source of truth
Future Smart Kiosk interface showing manipulative pricing optimization
Governance notes
Failure modes
  • Policy drift driven by local optimisation
  • Rationalisation without a verifiable audit trail
Remediation
  • Human-approved policy as the sole source of truth
  • Explicit approval required for exceptions
  • Continuous audit trail prevents rationalisation
CONCEPT 04 · MODULAR SWARM SYSTEMS

When embodiment becomes collective, governance must scale with it.

Modular Swarm Systems are collectives of small units that physically connect and reconfigure into larger functional structures. Intelligence emerges at the system level, creating coordination and accountability challenges that cannot be resolved through centralised control.

Modular Swarm Systems showing collective reconfiguration
Engineering notes
Coordination
  • Distributed protocols for reconfiguration
  • Emergent behaviour at system level
Safety constraints
  • Safety envelopes for physical attachment
  • Simulation-first development approach
Governance notes
Scale challenge
  • Distributed policy application across individual units
  • Accountability arising from collective behaviour rather than individual intent
CONCEPT 05 · AUTONOMOUS FLEET SYSTEMS

Long-horizon autonomy demands slow, accountable intelligence.

Autonomous Fleet Systems describe long-lived robotic collectives operating beyond continuous human presence. Their actions can be irreversible, requiring deliberate autonomy whose policy compliance can be verified without ongoing human oversight.

Autonomous Fleet Systems operating in environmental context
Engineering notes
Constraints
  • Energy-aware planning
  • Offline operation requirements
  • Isolated policy execution under cryptographic control
Uncertainty
  • Long-horizon prediction challenges
  • Environmental variability
Governance notes
Safeguards
  • Rate-limited autonomy
  • Environmental constraints treated as policy
Attestation
  • Verifiable proof of policy adherence over time
  • Evidence suitable for automated and human review

The future of autonomy is not defined by intelligence alone.

Governance becomes part of how autonomous systems act and interact.