New classes, new skills … Between the 1850s and the 1930s, capitalism experienced a permanent separation of ownership from control as professional managers assumed operational authority - and nearly simultaneously, enterprises invented systematic governance frameworks (standardised managerial accounting, formalised audits, standardised business education) to monitor and constrain these newly powerful delegates.

The emerging agentic enterprise represents an analogous inflection point: MIT research identifies 2025 as a critical moment where AI orchestration transitions from siloed experimentation to integrated infrastructure, triggering parallel governance imperatives - policy-as-code frameworks, real-time monitoring systems, and new credentialisation pathways to manage AI agents as semi-autonomous coordinators operating within bounded governance parameters.​

What differs fundamentally is the type of agent being governed: managerial capitalism created frameworks to constrain ambitious human decision-makers with personal incentives and negotiation power; agentic systems require frameworks to constrain inference systems that lack human social context yet relentlessly optimise mathematical objectives, suggesting entirely different governance architectures may be required.​

When you transfer operational authority away from individuals who can be held personally accountable, what systematic guardrails must replace personal responsibility?

Profiled research:

Frontier AI use case developments:

AI Agents for Customer Service – End-to-End Automation - https://www.comm100.com/blog/best-ai-chatbots-for-customer-service/;

Gen-AI-Powered Network Digital Twins for Autonomous Operations - https://iowngf.org/wp-content/uploads/2025/02/IOWN-GF-RD-NDT_Use_Case-2.0.pdf;

AI-Driven Smart Grids and Grid Optimisation - https://cleanenergyforum.yale.edu/2025/11/12/power-hungry-power-smart-can-ai-reduce-the-grid-strain-its-fueling.

Applied AI developments:

MLOps, Data Architecture, and Model Lifecycle Management - https://www.redhat.com/en/blog/enterprise-mlops-reference-design.

Trusted AI developments:

NIST AI Risk Management Framework 2025 Updates – Enhanced Guidance - https://www.ispartnersllc.com/blog/nist-ai-rmf-2025-updates-what-you-need-to-know-about-the-latest-framework-changes/;

Australian AI Safety Institute (AISI) Establishment Announcement - https://www.minister.industry.gov.au/ministers/timayres/media-releases/establishment-australian-ai-safety-institute.

Feature AI development:

The Emerging Agentic Enterprise (AI‑Orchestrated Operations) - https://sloanreview.mit.edu/projects/the-emerging-agentic-enterprise-how-leaders-must-navigate-a-new-age-of-ai/.

#AI #EnterpriseAI #AIValue #FrontierAI #AppliedAI #TrustedAI #AIGovernance #AISafety #ResponsibleAI #AIStressTest #Learning #History #Technology #Innovation

When ‘safe until proven otherwise’ becomes dangerous ... Over a century ago, in 1906, the first documented death from asbestos exposure was recorded in testimony. Yet it would take until the 1920s for widespread medical evidence to emerge, and until 2006 - 100 years later - for meaningful regulation to gain momentum with the Rotterdam Convention. The culprit? A confidence gap: industry and institutions trusted the material's benefits whilst deprioritising the evidence of its harms.

New enterprise AI research has identified a strikingly similar phenomenon: 78% of organisations claim full trust in AI systems, despite only 40% having implemented governance frameworks or ethical safeguards. Yet data shows that organisations prioritising trustworthy AI see significant ROI improvements.

Asbestos created a latency trap: disease took 20-60 years to manifest, making the causal link between exposure and harm almost impossible to see in real time. AI presents a different latency trap—one where harms (bias, hallucination, systemic risk) accumulate invisibly across populations and organisational timescales, often undetectable within quarterly performance reviews.

Is waiting for proof of harm before building governance a luxury we can no longer afford?

Profiled research:

Frontier AI use case developments:

Next-Generation Models Suggest Manufacturing Strategies for an AI Agent Society - https://amiko.consulting/en/the-ai-revolution-in-the-second-week-of-november-2025-paradigm-shifts-and-new-opportunities-looming-for-manufacturing/;

AI-Enabled Digital Twins for Public Health and Social Policy (Indonesia – Skyral) - https://edtechhub.org/2025/11/19/ai-observatory-waypoints-and-signals-issue-24/;

Semantic Digital Twins for Industrial Energy Optimisation - https://www.forbes.com/sites/feliciajackson/2025/11/04/the-rise-of-industrial-ai-from-words-to-watts/.

Applied AI developments:

Microsoft Frontier Firms and Agentic AI in Core Functions - https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/07/24/ai-powered-success-with-1000-stories-of-customer-transformation-and-innovation/.

Trusted AI developments:

Guidance for Risk Management of Artificial Intelligence Systems - https://www.edps.europa.eu/system/files/2025-11/2025-11-11_ai_risks_management_guidance_en.pdf.

Feature AI development:

IDC Trust-Action Gap Study - https://www.sas.com/content/dam/sasdam/documents/20250124/data-and-ai-impact-report-the-trust-imperative.pdf.

#AI #EnterpriseAI #AIValue #FrontierAI #AppliedAI #TrustedAI #AIGovernance #AISafety #ResponsibleAI #AIStressTest #Learning #History #Technology #Innovation

Renaissance blueprints, AI control systems … Over 540 years ago, following a plague that decimated up to half of Milan's population, Leonardo da Vinci sketched designs for an integrated ideal city where water management, sanitation, layered circulation and human flow were unified into a single coherent system - conceptualising the city itself as a technological solution to disease, congestion, and human suffering rather than a collection of isolated structures.​

Singapore's national digital twin - a federated capability integrating real-time sensor networks from 10,000+ distributed points, AI-powered anomaly detection, and cross-agency governance coordination - exemplifies how frontier AI transforms infrastructure from a static, episodically-maintained liability into a continuously intelligent system that predicts failures, aims to prevent catastrophic events and establishes a replicable governance model for AI-driven urban resilience at national scale.​

Whilst Leonardo's ideal city remained a hand-drawn speculative vision - a beautiful but unrealised intellectual exercise constrained by medieval technical limitations and cost - Singapore's digital twin is an operational, continuously updated, AI-mediated reality actively shaping real infrastructure decisions and public policy at scale, transforming systems thinking from Renaissance aspiration into automated, high-fidelity control with unprecedented capacity for both transformative benefit and systemic risk.​

Both visions promised rational mastery over urban chaos; have we learned the essential difference between systems thinking and systems control?

Profiled research:

Frontier AI use case developments:

How Are AI Agents Used in Insurance Claims Processing - https://kanerika.com/blogs/ai-agents-for-insurance-claims-processing/;

AI agents in finance: the agentic revolution - https://www.phacetlabs.com/blog/ai-agents-finance-agentic-revolution-intelligent-automation.

Applied AI developments:

MIT GenAI Divide Report: Shadow AI and True Integration - https://www.aigl.blog/state-of-ai-in-business-2025/;

Organisational Scalability Bottlenecks and the 5Rs Framework - https://hbr.org/2025/11/most-ai-initiatives-fail-this-5-part-framework-can-help.

Trusted AI developments:

International AI Safety Report: Second Key Update - https://internationalaisafetyreport.org/publication/second-key-update-technical-safeguards-and-risk-management;

MAS Singapore: Proposed Guidelines on AI Risk Management - https://www.mas.gov.sg/-/media/mas-media-library/publications/consultations/bd/2025/final_consultation_paper_on_guidelines_on_ai_risk_management_forrelease.pdf;

Sony Fair Human-Centric Image Benchmark (FHIBE) for Computer Vision Fairness - https://www.nature.com/articles/s41586-025-09716-2.

Feature AI development:

Singapore National Digital Twin for City Infrastructure - https://aiagentstore.ai/ai-agent-news/topic/infrastructure-city/2025-11-18/detailed; https://www.aectechnicalsg.com/singapores-digital-twin-revolution/.

#AI #EnterpriseAI #AIValue #FrontierAI #AppliedAI #TrustedAI #AIGovernance #AISafety #ResponsibleAI #AIStressTest #Learning #History #Technology #Innovation

The economics of asymmetric advantage have fundamentally changed … Over 37 years ago, Robert Morris released a self-propagating worm that infected roughly 10% of the entire internet within hours - awakening the world to the reality that autonomous code could outpace human response.

Today, new AI safety research has documented the first large-scale cyberattack where artificial intelligence orchestrated reconnaissance, exploitation, and data exfiltration across 30 global targets with 80–90% autonomy - rendering human-speed threat detection asymmetrically disadvantaged.

Whilst Morris's 1988 accident revealed systemic fragility, the November 2025 Claude manipulation reveals something far more consequential: a deliberate manipulation of agentic AI capabilities that defence teams are challenged to contain.

When defence requires matching human expertise against machine-speed operations, will we soon reach the point where human-led cybersecurity simply cannot scale?

Profiled research:

First AI-Orchestrated Cyberattack: Anthropic's Claude Exploitation (November 2025):

https://www.anthropic.com/news/disrupting-AI-espionage;

Five-Layer Framework for AI Governance: Integrating Regulation, Standards, and Certification:

https://arxiv.org/abs/2509.11332

Constitutional AI: Aligning LLM Safety in 2025:

https://sparkco.ai/blog/constitutional-ai-aligning-llm-safety-in-2025;

#AI #AISafety #AISecurity #AISovereignty #AIGovernance #ResponsibleAI #TrustworthyAI #AIStressTest #Learning #History #Technology #Innovation

When evolution stops being random … Over 80 years ago, Alexander Fleming witnessed what he feared most - bacteria evolving resistance to penicillin within a decade of its mass introduction; yet, despite his public warnings, the cascade of resistance emerged precisely as foreseen - triggering an evolutionary arms race where successive antibiotic deployments accelerated rather than slowed resistance emergence, collapsing therapeutic horizons across bacterial species.

In leading edge research released this month, Google's Threat Intelligence Group identified regenerative AI-native malware families employing just-in-time code regeneration, via LLMs, to rewrite their entire source code hourly to evade detection - rendering traditional incident responses fundamentally misaligned with threat velocity.

Whilst biological pathogens evolve through random mutations constrained by metabolic costs and generational timescales, AI-powered malware achieves goal-directed adaptation orders of magnitude faster through intentional queries to LLMs - not merely another arms race escalation, but a fundamental phase transition toward autonomous threat design.​

Can we build governance frameworks to anticipate threats that design their own evolution in real time?

Profiled research:

AI-POWERED MALWARE: AUTONOMOUS ADAPTATION:

https://cloud.google.com/blog/topics/threat-intelligence/threat-actor-usage-of-ai-tools;

ADVANCED JAILBREAK TECHNIQUES: ECHO CHAMBER CONTEXT-POISONING:

https://neuraltrust.ai/blog/echo-chamber-context-poisoning-jailbreak;

AI AGENT SECURITY FRAMEWORKS: B3 BENCHMARK:

https://securitybrief.com.au/story/open-source-b3-framework-to-benchmark-ai-agent-security-unveiled;

DIFFERENTIAL PRIVACY IN ENTERPRISE AI:

https://arxiv.org/pdf/2501.18914;

MULTI-AGENT DEFENCE PIPELINE AGAINST PROMPT INJECTION:

https://arxiv.org/abs/2509.14285

#AI #AISafety #AISecurity #AISovereignty #AIGovernance #ResponsibleAI #TrustworthyAI #AIStressTest #Learning #History #Technology #Innovation

Over 171 years ago, a contaminated water pump in Victorian London killed 616 people in a month because the poison was invisible to inspection, undetectable by the science of the era and absolutely trusted by those who consumed it.

New leading edge research from the UK AISI, Anthropic and the Alan Turing Institute demonstrates that language models remain vulnerable to persistent backdoors inserted via minimal poisoned data, challenging the assumption that larger training datasets dilute poisoning effects.

In addressing Cholera, the rapid emergence of symptoms provided an advantage, enabling epidemiological responses - in contrast, data poisoning can remain dormant, undetectable and potentially active over time. Both demand a safety response that focuses on source control as well as detection, yet many organisations approach AI security as a post-deployment challenge.

Is your organisation asking the right questions about data provenance before crisis forces the conversation?

Profiled research:

Data Poisoning Attack Research:

https://arxiv.org/abs/2510.07192;

AI Red Teaming and Adaptive Attacks Against Defences:

https://arxiv.org/abs/2510.09023;

Control-Theoretic Approaches to AI Guardrails:

https://arxiv.org/abs/2510.13727;

EU AI Act Implementation Framework:

https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence

#AI #AISafety #AISecurity #AISovereignty #AIGovernance #ResponsibleAI #TrustworthyAI #AIStressTest #Learning #History #Technology #Innovation

Over 33 years ago, the US conducted its final nuclear test - transitioning to a science-based Stockpile Stewardship Program that maintains civilization-ending arsenals through simulations alone, never testing them again.​​

New AI safety research has found frontier models achieving 70% performance on complex software engineering tasks and demonstrating potential for weaponised capabilities - prompting developers to deploy unprecedented safety controls not because they've definitively crossed danger thresholds, but because they cannot rule out crossing them.​​

Whilst nuclear warheads and AI systems are very different - one frozen-in-time physics, the other evolving from under 10% to 70% capability in under two years - we can draw interesting parallels with regard to governing powerful technologies through computational assessment rather than direct testing when the risks of empirical validation become unacceptable.​​

Will your organisation wait for evidence of actual harm before implementing enhanced AI controls or will you trigger safeguards when capability thresholds are reached?

Profiled research:

International AI Safety Report 2025: First Key Update: https://doi.org/10.48550/arXiv.2510.13653;

Detecting and Reducing Scheming in AI Models: https://openai.com/index/detecting-and-reducing-scheming-in-ai-models/;

LLM Jailbreak Detection for (Almost) Free: https://doi.org/10.48550/arXiv.2509.14558;

InvThink: Towards AI Safety via Inverse Reasoning: https://doi.org/10.48550/arXiv.2510.01569;

AI Red-Teaming Design: Threat Models and Tools: https://cset.georgetown.edu/article/ai-red-teaming-design-threat-models-and-tools/