Are We Sleep-walking into an Agentic AI Disaster?

Artificial intelligence has always arrived in waves, but the swell we’re feeling in mid-2025 is different. What began as narrow, data-driven models, morphed into creative large language models (LLMs), and is now surging toward agentic AI, self-directed systems that can chain tools together, make decisions, and even negotiate with other agents. It’s thrilling, yes, but also nerve-jangling, because most organisations are still using risk playbooks written for yesterday’s technology. Reid Blackman calls the gap between our optimism and our preparedness “The Ethical Nightmare Challenge.” He asks leaders to identify worst-case scenarios, build internal guard-rails, and upskill their people before the nightmares crash through the door .

The hidden complacency of “narrow” AI

For fifteen years we treated narrow AI like a reliable but limited assistant. It predicted loan defaults, spotted fraudulent claims, and ranked CVs. Its risks, while real, were familiar: bias, privacy leaks, and opaque logic. Crucially, narrow models lived inside clearly defined contexts. A credit-scoring tool was used in lending, a face-matcher in security; experts sat “in the loop,” reviewing outputs at human speed, and if a model misbehaved we could simply switch it off with minimal disruption . That containment bred a subtle complacency: most firms built their responsible-AI programmes around static check-lists, annual audits, and gatekeeper data-science teams.

Generative AI blows the fences wide open

Enter LLMs. Suddenly the same model that drafts marketing copy can also write code, answer legal questions, or generate medical advice. Contexts of deployment explode. Thousands of un-imagined use cases appear overnight, and developers cannot pre-test them all . Monitoring AI “in the wild” becomes mission-critical: without real-time oversight, hallucinations, privacy breaches, or IP violations flow straight into customer emails and board reports. Worse, good practice now extends to every employee who can type a prompt. If staff are not trained to fact-check, redact sensitive data, and write safe instructions, the corporate perimeter is riddled with holes .

Multi-model and agentic AI: complexity on a rocket sled

Generative models are merely the Lego-blocks for the next jump: multi-model and agentic AI. Picture an LLM wired to dozens of databases, fifty narrow models, several image or video generators, and permission to move money. Then imagine it chatting with sibling agents inside your firm, and finally bargaining with agents in a supplier’s network. Blackman sketches this as a five-stage curve…Stage 1 is a single LLM plus one model; Stage 5 is an inter-organisational swarm described, only half-jokingly, as “a head-spinning quagmire of incalculable risk.” Few enterprises have the staff, tools, or governance to survive even Stage 2 .

Why old risk programmes collapse under agentic weight

Once agents begin to act autonomously, six fault-lines appear :

1. Risk assessment overload. Every node, API call, and model interaction is a potential failure point. Mapping and testing them all is mathematically and financially impossible.
2. Human-in-the-loop erosion. Outputs arrive faster than any analyst can vet, and causal chains stretch across systems no single person understands.
3. Blindfolded go/no-go decisions. Most leaders lack rigorous pre-deployment evaluation frameworks, yet must sign off on systems that could move millions in seconds .
4. Real-time monitoring gaps. Without streaming observability, a rogue agent can wreak havoc before an alert dashboard even loads.
5. Brute-force shutdowns. Pulling the plug on an entire network mid-crisis may halt revenue or critical operations; selective circuit-breakers are rarely designed in advance.
6. Chronic skills deficit. Continuous, role-specific training—not a compliance video—is required so employees “smell smoke” early and intervene wisely .

Meeting the Ethical Nightmare Challenge

So, what does resilience look like? Blackman’s prescription is refreshingly practical: honestly plot where you are on the complexity curve, then build capabilities for that stage, not the hype headline. That means funding sandbox evaluations before launch, fitting telemetry into every agent interaction, and designing graceful-degradation modes so the whole factory doesn’t stop if Model #23 goes rogue . Above all, invest early in people…data scientists, domain experts, frontline staff, so they share a common language of prompts, safeguards, and escalation paths. The firms that thrive, he notes, “invest heavily before deploying the technology, not after problems emerge” .

A human-centred risk blueprint

1. Purpose first. Can every agent’s mission be voiced in one plain-English sentence a non-tech colleague understands?
2. Context mapping. List every database, external API, and downstream process. Unknown dependencies are ticking bombs.
3. Layered evaluation. Combine red-team stress tests, scenario simulations, and small-scale pilots. An agent that fails “flight school” stays grounded.
4. Live dashboards, fast kills. Expose latency, decision provenance, and data lineage in real time; embed tiered circuit-breakers that throttle or sandbox, not just kill.
5. Relentless education. Turn training into a culture: weekly fire-drills, cross-department “ethics pairs,” and reward mechanisms for spotting anomalies.

This blueprint isn’t theoretical; it is already emerging inside banks, insurers, and healthcare networks racing to contain Stage 2 complexity before Stage 5 arrives at their firewall.

Innovation ≠ recklessness

Agentic AI promises breathtaking productivity and new business models. But leaping from chatbot experiments to autonomous finance or supply-chain orchestration without scaffolding is not visionary; it is reckless . The smartest organisations treat risk work as an enabler of speed, not a brake. They know that brand-defining disasters – mistaken payouts, falsified audit trails, defamatory content…cost far more than the time it takes to build robust guard-rails.

Choose your future

We stand at an inflection point. We can rise to the Ethical Nightmare Challenge while the complexity is still tractable, or we can “charge ahead recklessly,” foot hovering over the accelerator, until catastrophe forces a U-turn . The roadmap is clear: map your stage, fortify the basics, upskill every seat, and monitor like your reputation depends on it—because it does.

If this exploration sparked new questions, share it with your risk officer, your data team, and that enthusiastic product manager wiring GPT-4 to production. Agentic AI isn’t waiting, and neither should we.