The Most Successful Agentic AI Systems in the World? You're Already Using Them...
Introduction
When executives discuss "Agentic AI", the conversation typically centres on futuristic-sounding concepts: intelligent digital workers, autonomous teams, and real-time orchestration across complex enterprise systems.
Yet the most sophisticated agentic systems are already operating at massive scale across the economy. They simply don't market themselves as such.
Financial Markets: The Original Agentic AI
Consider algorithmic trading systems. For over a decade, these platforms have processed real-time market data, executed autonomous trading decisions, managed risk parameters, and completed transactions within milliseconds—all without human intervention.
Today, algorithmic systems execute more than 70% of U.S. equity trading volume, managing trillions in daily transactions. These platforms don't merely assist human traders; they have fundamentally replaced them across numerous functions.
Global Commerce: Autonomous Operations at Scale
Supply chain optimization systems operate with similar autonomy. Amazon, Maersk, and Walmart deploy platforms that independently manage procurement, routing, inventory balancing, and global logistics networks.
When disruptions occur—weather delays, demand spikes, supplier issues—these systems reroute shipments, adjust orders, and trigger manufacturing responses without human involvement, optimizing for delivery performance and margin simultaneously.
Real-Time Risk Management
Fraud detection engines demonstrate agentic AI in real-time risk management. Banks and fintechs use ML-based systems that monitor, flag, and block suspicious transactions before human analysts are even aware of potential threats.
These systems continuously adapt to evolving fraud patterns while balancing loss prevention against customer experience—a complex optimization that happens millions of times daily.
Customer Acquisition at Scale
Meta's advertising platform represents another powerful example. Businesses define an outcome ("acquire new customers"), set a budget, and connect their payment method. The platform handles everything else autonomously: audience targeting, creative generation, campaign optimization, performance measurement, and iterative improvement.
The result: over $135 billion in revenue during 2023, with the majority originating from small businesses that never engaged directly with Meta personnel.
What Makes These Systems Truly Agentic?
These examples share crucial characteristics. They:
Operate asynchronously from human oversight,
Navigate complex and dynamic environments,
Produce goal-aligned results.
They are goal-seeking systems, not merely rule-following automation. This distinguishes genuine agentic AI from traditional enterprise software.
Reframing the Enterprise AI Transformation
This perspective fundamentally reshapes how organizations should approach their AI transformation journey.
If leadership teams continue thinking primarily in terms of generative AI tools that augment human capabilities—helping underwriters draft documents more efficiently or deploying chatbots for inquiry routing—they're likely underestimating the transformation ahead.
Agentic AI transcends helping people execute work more effectively. It's about constructing systems that assume ownership of entire workflows and deliver complete outcomes autonomously.
Insurance's Hidden Agentic Revolution
Let's look at one sector in more detail. Insurance already employs 'agentic AI', though many organizations don't recognize it as such.
Consider automated claims decision engines. Leading insurers currently use AI to autonomously settle straightforward claims—such as €75 baggage delay refunds or no-fault windshield repairs. These systems ingest structured first notice of loss data, validate policy coverage, apply business rules and thresholds, trigger pay-outs, and flag anomalies for human review.
This represents a clear agentic pattern: the system receives transaction inputs, processes them autonomously, and delivers completed outcomes—often within seconds.
However, most carriers still categorize these capabilities as "advanced automation" rather than AI agents. This mindset requires evolution.
Different Types of Agentic AI
Organizations should consider 3 different types of agentic AI:
Individual Autonomous Agents
The current state across the insurance industry involves rules engines and machine learning models settling low-complexity claims without human input (typically auto-refunds under €100). These systems handle only clear, structured claims and require human intervention for ambiguous or poorly documented cases.
Multi-Agent Workflows
Emerging capabilities feature specialized AI agents handling distinct claim stages—triage, evidence extraction, liability assessment, and pay-out processing—with seamless handoffs between agents. Large language models enable these agents to reason across documents, access contextual memory, coordinate with APIs, and escalate only when confidence thresholds aren't met.
For example, a travel claim agent could process PDFs, emails, and chat logs to verify delays, match against policy terms, calculate refunds, and trigger payments through coordinated agentic microservices.
Agentic Teams
Advanced implementations feature LLM-powered agents collaborating on complex decisions including subrogation, edge-case underwriting, and liability disputes. Individual agents evaluate policy coverage, examine claimant history, assess fraud risk, and recommend resolutions. Human oversight focuses on outcomes rather than processes, providing auditability, context memory, and explainability without requiring adjusters to manage every case.
See an amazing case study of this in action here.
From Process Automation to Outcome Ownership
The transition from generative AI to agentic AI fundamentally changes human roles within organizations:
From task executor to outcome overseer
From process participant to escalation handler
From executing work to determining when agents shouldn't execute work
Both Meta's advertising engine and modern claims systems share a core insight: these systems don't suggest—they act. They don't await prompts—they have defined responsibilities and execute them autonomously.
This represents agentic AI's fundamental value proposition across all these domains—from trading floors to supply chains to fraud prevention: think, decide, act, then report results.
Strategic Implications for Leadership
For executives managing claims, underwriting, or customer service operations where teams continue investing significant time gathering information, drafting decisions, or coordinating workflow steps—these represent processes that could already operate 'agentically'.
Implementation doesn't require beginning with full agentic teams. Start by identifying processes characterized by:
Clear, measurable objectives
Available, structured data
Repeatable business rules
Minimal human value-add until final review
Then construct multi-agent workflows, integrate existing systems, and enable agents to handle reasoning between process steps. Measure success not by speed improvements, but by intervention frequency—or the lack thereof.
The Path Forward
Organizations have successfully built intelligent tools. The next phase involves building intelligent workers.
Agentic AI is already operational at scale. It simply requires clearly defined outcomes and the autonomy to achieve them.
AI Risk's Agentic AI Accelerator provides the structural clarity to enable forward-thinking enterprises to create an autonomous synthetic workforce at scale. Our team has been implementing advanced Agentic Teams for some time now—ahead of the wider industry—with systems already handling complex knowledge work and even running entire operational functions.
Contact us to learn how our popular and proven approach can help your organization realize immediate value from Agentic AI.
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