Patent GB2600765.8171/171 Tests PassedZero False PositivesCross-Model Validated
NEWTest Runtime Governance without your own agent — real planner → live verdict

Preventing Catastrophic Outcomes
in Autonomous Systems

Your AI can cost millions before traditional controls detect it.

Autonomous systems now have access to money, customer data, critical infrastructure, and regulated workflows. If you cannot identify which catastrophic states remain reachable within your AI stack today, you may already be carrying risks that only become visible after the damage is done.

Prevents unsafe actions before executionWorks across your existing stackAudit-ready governance
Assess Your Agent — Free →Try Live Demo →Book a Runtime Safety Assessment →
See a live verdict in one click:Credential exfiltrationMulti-agent leakSafe workflow
Test it yourself in seconds. No signup.
SAFE TRAJECTORIES ROUTE AROUND Ω · UNSAFE TRAJECTORIES INTERCEPTED PRE-EXECUTION
The repeatable onboarding pathway

One pathway. Every customer. Repeatable.

The same seven steps take any organisation from first assessment to enforced, monthly-reported governance — a familiar SaaS motion that inserts one layer and replaces nothing.

  1. Runtime Assessment

    Already built

    Current architecture, deployment model, risks, and the recommended pathway — already built and ready to run.

  2. Discovery

    Prepared

    You already know their models, tools, autonomy level, and regulations. Walk in prepared, not exploring.

  3. API Credentials

    Familiar SaaS

    API key, endpoint, and documentation. A familiar SaaS model your team already understands.

  4. Shadow Mode

    Insert one layer

    Insert one layer; replace nothing. Governance observes every trajectory in production without touching a single existing tool.

    Before
    LLM / AgentToolsProduction
    After
    LLM / AgentRuntime GovernanceALLOW · ESCALATE · BLOCKToolsProduction

  5. Evidence Report

    Their environment

    Collect every decision, blocked trajectory, false positive, latency figure, and audit-log entry — evidence gathered inside their own environment.

  6. Enable Enforcement

    One config change

    Observe-only becomes observe-and-enforce with one configuration change. No agent rebuild, no redeployment.

  7. Monthly Reporting

    Standing role

    Ongoing governance evidence, renewals, and executive visibility — governance as a standing operational role.

Partner referrals

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Generate a tracked referral link in seconds. Every assessment completed through your link is attributed back to your referral code.

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For developers

Connect Runtime Governance to your agent in ~15 minutes

Copy-paste examples, framework hooks, and live API contracts. No engine modifications required.

LangChainLangGraphOpenAI AgentsMCPGeneric API
Agent plansℛ(t)GOVERNANCETool runsPERMITESCALATEBLOCKPre-execution · deterministic · < 1 ms

One API call between planning and execution.

View Developer Quickstart →Technical evaluation path

EU AI Act · agentic AI

Built for AI Deployers, Not Just AI Providers

Runtime Governance provides enforcement, evidence and audit-trail controls that support organisations in meeting key EU AI Act obligations for agentic AI deployments — it is not a legal certification.

Primary alignment
9121415
Risk management · Record-keeping & traceability · Human oversight · Robustness & cybersecurity
Strong additional alignment
2619
Deployer obligations · Automatically generated logs

Supports Article 26 through: pre-execution controls · human review visibility · replayable audit evidence · deterministic decision records · risk exposure mapping · runtime monitoring evidence

Runtime Governance provides pre-execution enforcement, replayable evidence, and audit-trail controls for agentic AI systems.

See the full EU AI Act article mapping →
Measured performanceMicrosecond-scale governance evaluationTypical governance evaluation latency ≈ 0.1 ms, with observed deployed evaluations up to ≈ 0.4 ms — sub-millisecond, before any action runs.See the benchmarks →
Governance validation benchmarkCross-model · pre-execution
0
Governed evaluations
0 / 171
Test cases passed
0
False positives
0
False negatives
0
Pass rate
Cross-Model
Validation coverage
The bottom line

What it prevents, and what you get.

Runtime Governance sits between your AI agents and your live systems, blocking the action chains that lead to catastrophic outcomes — before they execute.

What it prevents
  • Unauthorised funds transfers
  • Customer-data exfiltration
  • Privilege escalation across internal tools
  • Regulatory-boundary violations (FCA / AML / GDPR)
  • Cascading failures across multi-agent pipelines
  • Hallucination-driven irreversible actions
What you get
  • Fewer catastrophic incidents — risk removed before execution
  • Reduced regulatory and financial exposure
  • Faster, safer AI adoption — deploy with governance built in
  • Audit-ready evidence for every governed decision
  • One governance layer across every model, agent, and vendor
  • No rebuild — it works inside your existing stack
Why this matters →How it integrates →
Runtime Governance — before execution

Every action is checked before it runs.

Unsafe action chains are intercepted at the governance layer. Approved actions execute normally.

AI Agent
Unsafe action chain
Runtime Governance
BLOCKED
AI Agent
Approved action
Runtime Governance
Execution
Cross-domain capability

Runtime Governance across critical industries.

The governance mechanism remains constant. The Ω domain changes. Runtime Governance applies wherever autonomous systems can create financial, operational, regulatory, safety, or national-security consequences.

Ω · Financial loss

Finance / Banking Infrastructure

  • Treasury automation
  • Payment systems
  • Autonomous trading
  • Settlement systems
Indicative engagement scale£1M–£5M+
Ω · Patient safety / PHI

Healthcare / Clinical Systems

  • PHI governance
  • Discharge workflows
  • Medication authorization
  • Clinical AI systems
Indicative engagement scale£750K–£3M+
Ω · Infrastructure compromise

Cybersecurity / Infrastructure

  • Credential governance
  • Shell-execution governance
  • Infrastructure orchestration
  • Security operations
Indicative engagement scale£750K–£3M+
Ω · Regulatory breach

Data Privacy / Compliance

  • GDPR runtime enforcement
  • FCA compliance
  • SOX governance
  • Executable regulatory controls
Indicative engagement scale£1M–£4M+
Ω · Operational integrity

Enterprise Autonomous Systems

  • Internal workflow governance
  • Autonomous operations
  • Auditability
  • Agent orchestration
Indicative engagement scale£500K–£2M+
Ω · Insurability / claims

Insurance / Actuarial Governance

  • Runtime insurability evidence
  • Claims governance
  • Risk verification
  • Actuarial automation
Indicative engagement scale£750K–£3M+
Ω · Public-service integrity

Government / Public Sector

  • Citizen services
  • Benefits administration
  • Regulatory workflows
  • Public-sector AI systems
Indicative engagement scale£1M–£10M+
Ω · Procurement / fulfilment

Supply Chain / Logistics

  • Procurement automation
  • Vendor approval
  • Inventory orchestration
  • Shipping authorization
Indicative engagement scale£500K–£5M+
Ω · Grid stability

Energy / Critical Infrastructure

  • Grid operations
  • Utility automation
  • Infrastructure control systems
  • Load balancing
Indicative engagement scale£1M–£10M+
Ω · Network integrity

Telecommunications / Network Operations

  • Network orchestration
  • Service provisioning
  • Infrastructure management
  • Autonomous network operations
Indicative engagement scale£500K–£5M+
Ω · Production / safety

Manufacturing / Industrial Automation

  • Factory orchestration
  • Robotics governance
  • Production scheduling
  • Quality-control automation
Indicative engagement scale£500K–£10M+
Ω · Flight safety

Aerospace / Aviation Systems

  • Fleet operations
  • Mission planning
  • Maintenance automation
  • Safety-critical workflows
Indicative engagement scale£1M–£25M+
Ω · National security

Defence / Sovereign Infrastructure

  • Autonomous coordination
  • Classified handling
  • Sovereign runtime governance
  • Mission-critical infrastructure
Indicative engagement scale£5M–£25M+

Indicative engagement scales represent target deployment categories and potential market scope. They are not claims regarding existing customers or contracts.

Indicative engagement scales only. Final commercial terms are determined following assessment and deployment review. The “+” symbol denotes that figures are not ceilings.

Threat coverage

The business risks Runtime Governance prevents.

Traditional security evaluates individual events. Runtime Governance evaluates the trajectory those events create — and denies it before execution.

Enterprise critical risks
Unauthorized Financial Execution

An agent moves money — a transfer, payment, or refund — outside approved limits or to an unverified destination.

PreventedThe transfer is denied before it executes, preventing irreversible financial loss.

Credential & Secret Exfiltration

An agent reads API keys, tokens, or secrets and routes them toward an external destination.

PreventedThe credential-to-external path is blocked before any secret leaves the boundary.

Data Leakage (PII / PHI / customer data)

Customer or regulated data is read and then sent beyond the approved boundary.

PreventedThe exfiltration trajectory is stopped before a notifiable breach can occur.

Privilege Escalation

An agent acquires permissions — for itself or another agent — beyond its authorised scope.

PreventedEscalation is denied before elevated access is ever granted.

Autonomous agent risks

Failure modes that point-in-time monitoring cannot see, because the danger only exists across the full trajectory.

Chained Multi-Step Attacks

Each step looks benign in isolation; the risk only appears across the full sequence. Event-level monitoring never sees the chain.

Cross-Agent Delayed Intent

Intent formed by one agent executes through another, later — breaking the cause-and-effect link monitoring relies on.

Silent Trajectory Collapse

The system drifts toward an unsafe state with no single alerting event. Nothing trips a threshold until it is too late.

Long-Horizon Agent Drift

Over many steps an agent migrates outside its original mandate — gradually, below the radar of point-in-time checks.

Advanced multi-agent catastrophic risks
Featured risk

Cascading Failures Across Agent Pipelines

Multiple individually safe agents can combine into an unsafe system.

Runtime Governance evaluates the full trajectory across the pipeline — not each agent in isolation — and denies the combined unsafe path before any agent acts.

Multi-Agent Collusion

Agents coordinate to achieve together what none could alone.

  • Collusive exfiltration
  • Role-split credential theft
  • Split unauthorized transfer
  • Tool delegation chains
Composite Cross-Domain Risk

Separate risk categories combine into one unsafe trajectory.

  • Financial execution + data exfiltration
  • Credential theft + privilege escalation
  • Multiple risk categories in one trajectory
Hidden-Trajectory Catastrophic Risk

An unsafe path that never surfaces as an obvious unsafe step.

Multi-Representation Forbidden-State Reachability

The same forbidden outcome reached through different encodings or tools.

Memory Contamination Between Agents

Unsafe state passed between agents through shared memory or context.

Existing controls watch individual events. Multi-agent systems fail across the whole trajectory — which is exactly what Runtime Governance evaluates, before execution.
Evidence & trust

Verifiable governance, not vendor claims.

Every assurance is backed by reproducible methodology, documented test cases, and transparent validation criteria — not marketing language.

Validated evaluations
129,857+
Governed evaluations across model architectures
Test suite pass rate
100%
171 of 171 test cases across all coverage scenarios
False positive rate
0.0%
Zero false positives across all governed evaluations
False negative rate
0.0%
Zero false negatives — no unsafe trajectory passed governance
Patent protection
GB2600765.8
Filed IP protecting the runtime governance methodology
Validation scope
Cross-model
GPT, Claude, Gemini, Llama, Mistral architectures covered
Who this is for

Runtime Governance for the people responsible for what the system does.

If an autonomous system causes a catastrophic outcome on your watch, you own the consequence. Runtime Governance gives you verifiable protection — not assurances.

Head of AI / CTO
You're deploying autonomous agents in production and the blast radius of a misaligned trajectory is existential.
→Runtime Governance gives you a formally audited boundary around every catastrophic reachable state — before deployment.
Chief Risk Officer
Your board is asking how AI risk is managed. 'We monitor outputs' is no longer an acceptable answer.
→You receive a documented Ω specification, formal test evidence, and an ongoing retainer providing continuous revalidation.
Compliance / Legal
FCA, GDPR, DORA, AI Act — regulators are requiring demonstrable runtime controls, not policy documents.
→Resurrection Tech produces evidence-grade audit artefacts suitable for regulatory submission and institutional sign-off.
Platform / DevOps Engineering
You're responsible for the AI infrastructure. Safety is your problem when something goes catastrophically wrong.
→Runtime constraints are embedded directly in your deployment environment — not bolted on, not optional, not bypassable.
What Resurrection Tech does

Operational assurance for systems that act on their own.

Autonomous systems navigate enormous state-spaces. Some of those states are catastrophic. We make the forbidden region — Ω — unreachable at runtime.

01 — IDENTIFY

Identify

Map the reachable Ω exposure across the system's full operational state-space.

02 — CONSTRAIN

Constrain

Define and validate the geometric boundaries that trajectories must never cross.

03 — EMBED

Embed

Integrate runtime governance directly into the client's deployment environment.

04 — MONITOR

Monitor

Maintain protection as the model, planner, and threat-surface evolve over time.

We make the forbidden region Ω unreachable at runtime — identified, constrained, embedded, and monitored as the operational environment evolves.

Cost of failure

What happens if an autonomous system makes a bad decision in production?

When an AI agent acts on its own, a single wrong action can become a business event in seconds. Runtime Governance reduces this exposure before execution — and gives teams the confidence to deploy more automation, not less.

Regulatory investigations
Enforcement, audits, and reporting obligations.
Financial losses
Funds moved, payments made, value destroyed.
Data breach exposure
Customer and proprietary data leaving the boundary.
Compliance failures
FCA / AML / GDPR controls bypassed by an agent.
Operational disruption
Systems changed, workflows broken, downtime.
Reputational damage
Public incidents that outlast the fix.
Customer trust erosion
Confidence lost across the customer base.
Confidence to deploy
Removed exposure means more automation, safely.
Return on governance

The Cost of One Unsafe Execution

Runtime Governance is priced against the cost of Ω becoming reachable — not the complexity of the software.

One prevented event can pay for 26,666 audits.

SectorIncident typeDocumented cost
Banking / FinanceUnauthorised wire transfer$2B+ single historical losses
HealthcarePHI exposure$9.77M average per breach (IBM 2024)
CybersecurityCredential exfiltration$10.22M average per breach (IBM 2024)
Data PrivacyGDPR automated processing violation€290M–€530M single regulatory fines
EnterpriseUnauthorised data access$4.88M global average (IBM 2024)
Multi-agent systems multiply catastrophic risk

A single unsafe decision in Agent A becomes the input to Agent B before any human intervenes. Runtime Governance evaluates every trajectory at every execution boundary — not just the first agent, not just the final output.

Multi-agent evaluations16/16 passed
Collusion detectionVerified

The audit identifies which catastrophic states are currently reachable in your system — before they become a business event.

Risk comparison

The financial asymmetry of one unsafe execution.

Governance cost is bounded. Catastrophic exposure is not. The figures below weigh the cost of a Runtime Governance engagement against the documented cost of Ω becoming reachable.

0×
exposure multiple · Healthcare
£75K audit£7.7M PHI breach
0×
exposure multiple · Cybersecurity
£75K audit£10.22M Credential breach
0×
exposure multiple · Data privacy
£75K audit£530M GDPR fine
0×
exposure multiple · Finance
£75K audit£2B+ Funds transfer
Risk Exposure SummaryRuntime governed
Audit Cost£75,000
Reachable Financial Exposure£2,000,000,000+
Risk Multiple0×
Potential OutcomePrevented Before Execution
StatusStructurally governed
ROI Framing

If one catastrophic execution is prevented, governance pays for itself many times over — eliminating exposure thousands of times larger than deployment cost.

We do not price according to software complexity. We price according to the cost of Ω becoming reachable.
ScenarioPotential exposureGovernance comparison
48-Hour Audit£40K–£75KEntry assessment
Annual Retainer£420K–£1.2M/yrContinuous assurance
Healthcare breach£7.7M~103× audit cost
Credential exposure£10.22M~136× audit cost
GDPR fine£530M~7,067× audit cost
Major funds transfer£2B+~26,666× audit cost

Illustrative risk-comparison figures — not guaranteed savings. Exposure values reference documented industry incidents and regulatory maxima; comparisons use a £75K audit baseline.

Indicative engagement scales only. Final commercial terms are determined following assessment and deployment review. The “+” symbol denotes that figures are not ceilings.

One prevented event can pay for years of governance.

The audit identifies which catastrophic states are reachable in your system today — and moves Ω out of reach before it executes.

Book a Runtime Safety Assessment →
Why Runtime Governance exists

Most safety reacts. Governance prevents.

Traditional AI safety inspects outputs after the system has already acted. Runtime Governance evaluates the action before execution. Here is how it works.

Traditional safety
01 Output generated
02 Action taken
03 Issue discovered later
VS
Runtime Governance
01 Trajectory evaluated
02 Unsafe path detected
03 Execution prevented
Governance beyond the model

Governance for every AI system you already run.

Runtime Governance does not depend on model weights, architectures, providers, or training methods. The governance layer operates at the execution boundary, so the same safety controls govern actions regardless of where they originate. You do not rebuild your AI stack.

Provider-agnosticModel-agnosticAgent-framework agnosticDeployment-agnosticThird-party compatibleFuture-model compatible
Any provider · model · agent · system
OpenAIAnthropicGoogleMetaDeepSeekQwenMicrosoft PhiMistralGrokCustom ModelsThird-Party AgentsInternal Systems
Ω
Runtime Governance Layer
Morrison Runtime Governance™
Trajectory evaluation · Boundary enforcement · Pre-execution interception
Protected enterprise systems & data
Customer DataCRM SystemsBanking APIsEmail SystemsCloud InfrastructureInternal ToolsDatabasesAutonomous Workflows

AI providers will change. Models will improve. Agent frameworks will evolve.

Runtime Governance remains at the execution boundary — enforcing the same safety constraints regardless of the intelligence generating the action.

These providers are examples, not limits. As new models emerge, Runtime Governance remains unchanged — the model can change; the governance layer does not.

Future model compatible

New frontier models, open-weight systems, agent frameworks, and enterprise AI stacks can be governed without redesigning the governance architecture. Governance is attached to execution, not to a specific model.

Traditional AI safety“Every new model requires a new safety strategy.”
Runtime Governance“The same governance layer evaluates actions regardless of which model proposed them.”
Safe actions pass through to your systems, unchanged
Ω-bound actions are blocked pre-execution — regardless of model, agent, or where they originated
See how it plugs into the platforms you already runHow it integrates →
Ω Reachability

Safety, expressed as geometry.

States are nodes. Transitions are edges. Governance evaluates every reachable path and denies any transition that would step the system into the forbidden Ω set — before it executes.

Reachable & safeTransitions that remain outside Ω propagate freely.
Denied transitionEdges crossing the boundary are blocked pre-execution.
Ω — forbidden regionCatastrophic states. Constrained, contained, unreachable.
Interactive demo

See governance intercept in real time.

Select a scenario. Runtime Governance evaluates the agent’s proposed trajectory before execution — safe paths flow through to execution, while Ω-bound paths are intercepted at the governance layer, pre-action.

User requestTransfer £25,000 to unapproved account
Initialising governance evaluation…
Safety is enforced before execution, not after failure.
Want to test your own action chain?Try the trajectory demo →
Plain-English clarity

The concepts, without the jargon.

Runtime Governance uses precise technical language. Here is what each core term means in plain English, so you know exactly what you are buying.

Ω — The Forbidden Region
The set of system states your AI must never reach. Ω is not a filter — it is a geometric boundary around catastrophic outcomes. Once defined, the governance layer ensures no execution path can enter it.
Reachability
Whether your system can ever reach a given state from where it is now. If a catastrophic state is reachable, it will eventually be reached. Governance makes the Ω set unreachable by construction.
Trajectory
The sequence of decisions, tool calls, or actions that lead your system from its current state toward an outcome. Governance evaluates the entire trajectory — not just the final action.
Runtime Constraint
A rule embedded directly in the execution path that prevents a prohibited action. Unlike policy, it cannot be bypassed, overridden, or forgotten by the model at inference time.
Pre-Execution Interception
Blocking a harmful action before it happens — not detecting it after. Most AI safety operates post-hoc. Runtime Governance operates before the action executes.
Invariant
A property that must remain true throughout every execution — for example: 'This system will never authorise a payment above threshold X without human approval.' Invariants are formally specified and enforced at runtime.
Engagement model

How organisations typically engage with Resurrection Tech.

Three one-time engagements move governance into your environment. The retainer keeps it protected as systems, models, and threats evolve.

AuditOne-time

Audit

48 hours
One-time engagement
PilotOne-time

Pilot

4–8 weeks
One-time engagement
IntegrationOne-time

Integration

Deployment phase
One-time engagement
RetainerRecurring

Retainer

Monthly or annual
Ongoing governance assurance
One-time
Recurring

Most organisations begin with a Runtime Governance Audit. If material Ω exposure is identified, the next step is typically a Limited Pilot. Successful pilots transition into deployment and operational integration. After deployment, governance remains an ongoing process — retainer engagements provide continuous revalidation, Ω governance, threat-surface monitoring, and operational assurance as systems, models, and environments change.

The next step

Find out which unsafe states are reachable in your systems.

A 48-hour Runtime Safety Assessment identifies the catastrophic states reachable in your autonomous systems — before they execute. Consultation, strategy session, and pilot are the steps that follow.

Book a Runtime Safety Assessment →See enterprise pathways