Agentic AI: Strip Away the Hype and Understand the Real Strategic Choice

In my conversations with Chartered Directors and Boards over recent months, I’m hearing consistent questions about how agentic AI will transform their businesses. The interest is genuine and the excitement palpable, agentic AI has become this year’s poster child, dethroning generative AI as the technology everyone wants to discuss. Directors want to understand whether autonomous agents represent the next wave of competitive advantage or simply another chapter in the ongoing AI narrative.

The challenge is that barriers to successful adoption are emerging from fundamental misunderstandings about what agentic AI actually does. When Boards don’t grasp the underlying reality, they can’t make fully informed decisions about where to deploy these capabilities and how to govern them appropriately. This confusion creates friction for organisations trying to capture value, for executives accountable for outcomes, and for teams tasked with implementation.

In this article, I want to demystify the agentic AI hype. I’ll explain what agentic AI actually is, examine the legitimate innovation it represents, and provide a practical approach for deciding where it creates value in your organisation. My goal is to help you cut through the terminology and focus on the strategic choice that actually matters: where should you transfer decision-making agency from humans to machines, and at what scale?

What Agentic AI Actually Is

To understand agentic AI, you need to see how work patterns evolve as organisations progressively delegate decision-making to AI systems. This progression unfolds across four distinct stages, each representing a different balance between human control and machine autonomy.

Stage 1: Human-centred work relies entirely on human cognition and expertise. People gather information through reading and interviews, decompose goals using mental models and checklists, execute actions in applications manually, and maintain memory through notebooks and shared drives. Governance happens through peer review, manager sign-off, and compliance processes. This is how most organisations have operated for decades, humans making all decisions with no AI involvement.

Stage 2: Human plus AI Model (single-shot) introduces AI as an on-demand assistant. The AI model acts as a copilot for generating ideas or drafts, but humans retain all judgement. People curate inputs and the AI model summarises or extracts information. Humans outline approaches whilst the AI model suggests steps, but humans choose every next move. The AI model drafts text or code, but humans execute all changes. Memory remains limited to chat session context with no persistence beyond human note-taking. Governance continues through human oversight and organisational policies. This stage describes most current generative AI usage — quick queries and single responses with full human control.

Stage 3: Human-in-the-loop enables iterative collaboration between humans and AI. The AI model gets invoked repeatedly with templated prompts whilst humans set objectives and success criteria. Both human and AI model parse documents together through structured ingestion. The AI model proposes plans and critiques its own suggestions, but humans review, revise, and accept each iteration. Semi-automated workflows emerge with tool calls requiring user approval before execution. Short-term session memory combines with retrieval over curated knowledge bases. Humans conduct the entire process, orchestrating multiple AI model interactions with review gates, logging, and escalation criteria. This stage represents sophisticated AI usage where humans drive the improvement loop.

Stage 4: Agentic AI transfers the loop to the machine. The system selects AI models automatically, choosing role-specialised models where needed and driving reasoning independently. Event-driven ingestion from APIs, databases, and sensors builds retrieval-augmented context autonomously. The system performs autonomous planning with chain-of-thought reasoning, self-reflection, and dynamic replanning against goals without human intervention. Direct API and tool execution happens under scoped permissions — the system creates transactions, raises tickets, and schedules actions. Both short-term scratchpad and long-term vector database memory persist across runs, building episodic task history. Automated evaluation and feedback loops drive policy updates and A/B tests. Multi-agent roles coordinate through message buses with parallelism and hand-offs occurring without human involvement. Governance shifts to policy engines with risk tiers, approval thresholds, sandboxes, immutable audit logs, and kill-switches, with human oversight reserved for escalations.

The progression reveals the fundamental truth: agentic AI is generative AI in a loop, but the machine runs that loop instead of a human. Stage 1 involves no AI at all, it’s purely human work. Stages 2 through 4 all use underlying AI models. What changes across these AI-enabled stages is the locus of control - who decides whether output meets objectives, what improvements to make, which tools to invoke, and when the work is complete.

A 12-minute visual walkthrough of the four stages of AI work patterns and the eight components that define where you're transferring decision-making authority.

This matters enormously for Boards because each stage represents a different delegation of decision-making authority. Stage 2 delegates idea generation. Stage 3 delegates iterative refinement under human supervision. Stage 4 delegates the entire judgement process about adequacy, direction, and completion. Understanding these distinctions helps you recognise what you’re actually approving when someone proposes “implementing agentic AI.”

The sophistication in stage 4 is real. Autonomous planning with self-reflection, direct tool execution, persistent memory across tasks, and multi-agent coordination represent genuine architectural advances. But these advances amplify a capability you already have, they don’t create fundamentally new intelligence. The strategic question isn’t whether the architecture is impressive. The strategic question is where delegating the entire loop creates advantage for your organisation versus where human judgement in the loop remains essential.

This connects directly to a challenge I’ve been exploring for several years: as AI systems take on more autonomous decision-making, Boards shift from overseeing hundreds of decisions made per day to millions made per second. When I wrote about transforming Board oversight through decision analytics, the Board in the machine, and AI’s impact on governance priorities, the core question was how Boards could remain confident that each autonomous decision remains transparent, explainable, and correct at that scale. Agentic AI makes this governance challenge immediate and concrete - stage 4 systems are making those millions of decisions per second right now, and your Board needs frameworks to oversee them effectively.

Common Perspectives on Agentic AI

The discussion around agentic AI encompasses several important perspectives that help Boards understand where real value emerges. These viewpoints illuminate different aspects of what makes agentic systems strategically significant beyond simple automation.

The “emergent intelligence” perspective highlights architectural sophistication. Modern agentic systems incorporate reflection mechanisms where the AI critiques its own outputs, planning modules that break complex goals into subtasks, and multi-agent frameworks where different AI instances collaborate with specialised roles. These patterns mirror how skilled humans approach complex problems. The sophistication is real and the engineering represents genuine innovation. However, sophisticated system design doesn’t change the fundamental nature of what’s happening: a machine is running an improvement loop using the same generative AI capabilities available in simpler implementations. The architecture amplifies capability but doesn’t create new forms of intelligence.

The “autonomy versus automation” distinction raises valid points about decision-making scope. When humans drive iteration loops, we make countless micro-decisions: Is this answer complete? Does it address the question properly? What specific improvements would help? Should I search for additional context? These judgements require understanding nuance, assessing quality, and determining next steps. When systems automate these decisions, they’re exercising genuine autonomy within their operating domain. This autonomy matters for Boards because it represents transferred decision rights. The system isn’t simply executing pre-defined steps, it’s making judgements about adequacy, direction, and completion.

Scale proponents emphasise operational advantages that human-in-the-loop approaches cannot match. Agentic systems operate continuously without fatigue, process multiple tasks in parallel, and maintain consistent quality across thousands of iterations. A customer service operation might handle ten thousand enquiries simultaneously, each following an autonomous improvement loop until resolution. A document analysis workflow might process an entire repository overnight, with each document receiving iterative refinement until extraction meets quality thresholds. These scale advantages unlock economic value in high-volume scenarios where human iteration would create impossible bottlenecks. The capability is real and the business case can be compelling for appropriate use cases.

Production reliability advocates correctly note that enterprise deployment requires more than research demonstrations. Systems operating autonomously at scale need robust error handling, graceful degradation when encountering edge cases, integration with existing business processes, audit trails for regulatory compliance, and kill-switches for human intervention when needed. Enterprise-grade agentic platforms invest heavily in these operational capabilities. The resulting systems offer more than raw AI capability, they provide governed automation infrastructure. This reliability matters enormously for Boards evaluating operational risk.

Here’s where the concept of minimum lovable governance becomes essential. The governance challenge isn’t building comprehensive controls that cover every conceivable risk, that approach paralyses adoption and defeats the purpose of autonomous systems. Instead, Boards need just enough structure to demonstrate good faith oversight whilst preserving the agility and speed that make agentic AI valuable. This means establishing clear operating boundaries, defining escalation triggers for edge cases, ensuring autonomous decisions can be audited, and implementing kill-switches for human intervention, but designing these mechanisms to enable safe delegation rather than prevent it. Minimum lovable governance acknowledges that perfect oversight is impossible at the scale and speed where agentic AI operates, so the focus should be on managing consequential risks whilst accepting tolerable ones.

What unifies these perspectives is their focus on legitimate technical and operational advances. The architectural sophistication is real, as is the genuine autonomy these systems exercise and the scale they unlock. Production reliability makes enterprise deployment viable rather than theoretical. Yet none of this contradicts the central insight that agentic AI is generative AI in a loop, these perspectives simply illuminate why the loop delegation question matters so strategically. When sophisticated architecture amplifies the impact of each delegation decision, when genuine autonomy increases the importance of governance frameworks, when scale magnifies both opportunity and risk simultaneously, and when reliability determines whether delegation can happen safely at all, the choice of where to transfer agency becomes more consequential, not less.

For Boards, the strategic implication is clear: value comes from scale and reliability in contexts where autonomous iteration outperforms human oversight. The question isn’t whether the technology is impressive, it demonstrably is. The question is where delegating the improvement loop creates advantage for your organisation versus where human judgement remains essential. The sophistication of agentic systems makes this delegation decision more consequential, not less.

The Strategic Questions for Boards

Five questions help Boards evaluate where agentic AI creates value and where human control remains essential.

Where should we transfer agency? Not all tasks suit autonomous iteration. Routine processes with high volume and clear success criteria represent strong candidates. Data processing tasks that require multiple refinement steps benefit from continuous operation. Customer enquiries with established resolution patterns can scale through autonomous handling. Document analysis workflows improve when systems iterate until extraction meets quality standards. These scenarios share characteristics: the work is repeatable, success measures are definable, and volume creates bottlenecks for human iteration.

Conversely, creative work requiring original thinking, high-stakes decisions with significant consequences, and judgements involving ethical nuance all warrant human control. Strategic planning demands contextual understanding that extends beyond pattern recognition. Complex negotiations require reading unstated signals and adapting to human dynamics. Ethical decisions about fairness, privacy, or societal impact need human accountability. These scenarios share different characteristics: the work is non-routine, success requires contextual judgement, and consequences of errors extend beyond operational efficiency.

The strategic filter asks: where does scale outweigh expertise? When commodity tasks arrive in high volume, autonomous iteration delivers clear advantage. When proprietary knowledge or high consequences dominate, human oversight remains superior. The boundary between these scenarios defines your agentic AI opportunity space.

How will we govern delegated decisions? Transferring agency without governance amplifies risk rather than managing it. Boards need clear answers about how they’ll understand what autonomous systems are doing, when those systems should escalate to humans, and how to intervene when needed. What decisions is the system authorised to make independently? When must it escalate to human review? How do we trace the path the system took to reach its conclusion? Where are the kill-switches if autonomous operation produces unexpected results?

These governance questions connect directly to established Board concerns about risk management, ethical responsibility, and stakeholder confidence. Autonomous systems making customer-facing decisions affect reputation and regulatory compliance. Financial process automation requires audit trails and approval workflows. Operational systems need resilience mechanisms when automation fails. The governance framework should define clear boundaries for autonomous operation whilst enabling the scale advantages that justify adoption.

What’s our subject matter expertise boundary? Expert humans often outperform automation when proprietary knowledge matters. Your organisation’s specialists understand nuances that general-purpose AI systems miss. They recognise patterns specific to your industry, customers, or operations. They apply judgement shaped by years of experience with your particular challenges. This expertise represents competitive advantage that shouldn’t be surrendered carelessly.

The framework for this decision weighs knowledge type against operational characteristics. Commodity processes using generally available knowledge suit automation. Proprietary processes requiring organisation-specific expertise warrant human control. High-volume commodity work becomes an obvious candidate for agentic AI. Low-volume proprietary work should retain expert human oversight. The difficult decisions involve high-volume proprietary work where scale advantages conflict with expertise requirements. These scenarios might suit hybrid approaches where systems handle initial processing and experts review edge cases or validate outputs.

Build versus buy? When evaluating agentic AI options, clarity about what you’re actually acquiring helps focus the decision. You’re not procuring intelligence, you’re acquiring loop execution at scale with enterprise reliability. This reframing shifts evaluation criteria toward operational capability rather than technological impressiveness.

For purchased solutions, assess integration capability with your existing systems, governance tooling for audit and control, operational resilience including error handling and escalation, and deployment support for production readiness. The sophistication of the underlying AI capability matters, but how effectively the solution enables you to delegate agency safely within your operational context matters more. Strong vendors understand this distinction and can demonstrate operational maturity alongside technical capability.

Building internally offers control and customisation but requires significant capability investment. Engineering expertise to implement agentic architectures reliably, capacity to maintain production systems as underlying AI capabilities evolve, and understanding of operational complexity at scale all factor into the build decision. For most organisations, enterprise platforms provide faster paths to production than building from scratch, but the evaluation should balance operational fit against strategic differentiation requirements.

How do we pilot responsibly? Research consistently shows that most enterprise AI pilots fail to reach production. The much-discussed failure rates include Gartner’s prediction that thirty per cent of GenAI projects will be abandoned by end of 2025, and MIT’s analysis suggesting ninety-five per cent of integrated AI pilots extract zero return. Whilst the precise figure is debated — and some argue pilots are meant to fail fast to generate learning — the pattern remains clear: isolated, project-based AI adoption consistently underperforms strategic expectations. Agentic AI pilots face even higher risk because autonomous operation introduces additional failure modes beyond basic AI capability. Define success metrics before starting. What specific business outcome justifies the investment? How will you measure whether autonomous iteration outperforms human-driven processes? What governance mechanisms need validation during the pilot?

Start with bounded domains where failure is recoverable. A pilot processing internal documents poses less risk than one making customer-facing decisions. An experiment augmenting expert analysis allows comparison against human performance. A trial handling routine enquiries can escalate complex cases to human agents. These bounded experiments let you learn about delegation safety whilst limiting downside risk.

Avoid treating the pilot as a purely technical exercise. Agentic AI success requires operational integration, staff adaptation, and governance maturity as much as technical capability. Pilot programmes should validate the complete system—technology, people, and processes—rather than simply demonstrating that autonomous iteration functions.

Making the Strategic Choice

Agentic AI adds value where scale or speed deliver advantage over expertise. Customer service operations benefit from continuous availability and parallel processing. Document analysis workflows improve through consistent application of extraction rules. Data processing tasks gain from iterative refinement without human bottlenecks. These scenarios share a common pattern: the work is high-volume, success criteria are clear, and autonomous operation enables capabilities impossible with human-driven loops.

Human-in-the-loop remains superior where expertise is irreplaceable. Strategic decisions require contextual understanding that extends beyond pattern recognition, drawing on organisational history, market dynamics, and stakeholder relationships that no system can fully capture. Creative work demands original thinking rather than refinement of generated options—the spark of innovation that distinguishes breakthrough thinking from competent iteration. Ethical judgements need human accountability for societal impact because machines can optimise for outcomes but cannot bear moral responsibility for consequences. High-stakes situations justify the time investment in human oversight, particularly where errors carry reputational, regulatory, or safety implications. What unifies these scenarios is that deep expertise matters more than operational efficiency, consequences of errors extend beyond correctable mistakes, and human judgement adds irreplaceable value rather than simply adding time to the process.

Governance isn’t optional—delegation without audit amplifies risk. Autonomous systems need clear operating boundaries, escalation mechanisms for edge cases, audit trails for decision review, and kill-switches for human intervention. The governance framework should enable safe delegation rather than prevent it. This philosophy of minimum lovable governance builds just enough structure to demonstrate good faith whilst preserving the agility that makes agentic AI valuable.

The decision isn’t whether to adopt agentic AI, but where to transfer agency consciously. Different parts of your organisation will have different delegation readiness. Some functions face high-volume commodity work perfect for autonomous iteration. Other functions deal with proprietary expertise or high-stakes decisions requiring human control. Most organisations will operate with multi-speed adoption where agentic AI scales specific processes whilst human-in-the-loop approaches handle others. This diversity is appropriate rather than problematic—strategic delegation matches approach to context.

Beyond the Hype

Agentic AI is generative AI in a loop—nothing mystical, nothing magical. The technology underneath remains the large language models you’ve been evaluating for two years. What’s changed is who drives the improvement loop: human or machine. This shift matters enormously because it represents transferred decision-making authority from people to systems.

The true innovation lies in safe, scalable delegation of iterative judgement. Sophisticated architectures amplify capability. Genuine autonomy enables continuous operation. Scale unlocks economic value in high-volume scenarios. Production reliability makes delegation viable for enterprise deployment. These advances are real and consequential, but they don’t change the fundamental strategic question: where should we transfer agency and how will we govern autonomous decisions?

The strategic imperative for Boards is conscious delegation. Evaluate where scale advantages outweigh expertise requirements. Define governance boundaries before approving autonomous operation. Pilot in bounded domains where learning is possible and failure is recoverable. Build capability gradually rather than pursuing transformation through procurement. This measured approach enables you to capture agentic AI value whilst managing the amplified risks that autonomous systems introduce.

Agentic AI will multiply both opportunity and risk in the coming years. Market projections suggest explosive growth, with the agentic AI market expected to grow at 44.6% CAGR from $7.06 billion in 2025 to $93.20 billion by 2032, but Gartner predicts that over forty per cent of agentic AI projects will be cancelled by end of 2027. This divergence between promise and reality reflects the fundamental challenge: organisations that treat agentic AI as a technology purchase will struggle, whilst those that approach it as strategic delegation of decision-making will succeed. Wisdom lies in understanding the difference.

Your AI amnesty programme may have discovered autonomous systems already operating in shadow. These informal agentic implementations need the same governance thinking as formally deployed systems. The loop is running—the question is whether you’ve consciously decided to transfer that agency and put appropriate guardrails in place. That decision, more than any technical capability, determines whether agentic AI creates value or amplifies risk in your organisation.

About the Author

Mario Thomas is a Chartered Director and Fellow of the Institute of Directors (IoD) with nearly three decades bridging software engineering, entrepreneurial leadership, and enterprise transformation. As Head of Applied AI & Emerging Technology Strategy at Amazon Web Services (AWS), he defines how AWS equips its global field organisation and clients to accelerate AI adoption and prepare for continuous technological disruption.

An alumnus of the London School of Economics and guest lecturer on the LSE Data Science & AI for Executives programme, Mario partners with Boards and executive teams to build the knowledge, skills, and behaviours needed to scale advanced technologies responsibly. His independently authored frameworks — including the AI Stages of Adoption (AISA), Five Pillars of AI Capability, and Well-Advised — are adopted internationally in enterprise engagements and cited by professional bodies advancing responsible AI adoption, including the IoD.

Mario's work has enabled organisations to move AI from experimentation to enterprise-scale impact, generating measurable business value through systematic governance and strategic adoption of AI, data, and cloud technologies.