Raphaëlle d’Ornano, Founder of Decoding Discontinuity, an institutional research platform delivering rigorous analysis of tech disruptions.
Technological evolution is typically marked by gradual optimization, such as faster processors, smarter interfaces and more efficient workflows. Occasionally, a major disruption happens that significantly accelerates these dynamics.
Only very rarely does a true discontinuity emerge: a change so profound that it redefines not just performance boundaries, but the foundational assumptions of how systems operate.
Agentic AI represents such a leap. Agentic AI does not simply enhance outputs or interfaces. It reconfigures the nerve center of decision making (known as the “locus of coordination”), shifting software from reactive tools into autonomous orchestrators capable of initiating, planning and executing complex workflows.
This discontinuity occurs because agentic AI decouples coordination from human operators. This reduction of human dependency and increased automation redefines the constraints and possibilities of entire systems. In doing so, it resets the financial and economic boundaries for determining valuations and the metrics for measuring success.
My views are shaped by more than a decade of experience in complex financial analysis and strategic advisory for private investors, allocators and C-level executives. My work now focuses on the structural impact of generative and agentic AI, bridging technological understanding with capital markets expertise.
Traditional benchmarks are obsolete. To adapt to the agentic AI era, investors and executives must change how they think about problems, valuations and competitive moats.
From Generation To Orchestration
A clear taxonomic progression of AI system architectures highlights why agentic systems represent discontinuous rather than evolutionary change. The progression spans three distinct paradigms:
1. Generative AI systems operate as sophisticated content producers, reactive entities that synthesize novel outputs (text, images, code) based on input prompts. These systems exhibit remarkable generative fidelity but remain fundamentally stateless, lacking persistent memory, autonomous goal-setting or environmental interaction capabilities. Think of them as extraordinarily sophisticated typewriters that can produce content on demand but cannot initiate or coordinate activities independently.
2. AI agents represent the first evolutionary step toward autonomy, integrating large language models with external tool access and basic reasoning loops. These systems can retrieve real-time information, execute multistep workflows and maintain limited contextual memory across interactions. However, they remain architecturally constrained by single-agent operation, reactive initiation patterns and bounded autonomy within predefined task domains. They function as intelligent assistants that are capable and adaptive, but depend on human direction and oversight.
3. Agentic AI transcends these limitations through multi-agent collaboration, persistent memory architectures and autonomous goal decomposition. These systems dynamically construct coordination strategies, adapt to changing environments and operate with distributed intelligence across specialized agent networks. The architectural distinction is profound: Where previous systems optimized for human-computer interaction, agentic systems optimize for computer-computer coordination.
The Architectural Discontinuity
The discontinuous nature of this transition becomes evident when examining the fundamental shift in architectural assumptions.
Traditional software architectures optimize for human-initiated interactions through carefully designed interfaces, predictable workflow patterns and centralized control structures. Users remain the primary coordinators, directing software through explicit commands and managing complex multistep processes through sequential interface interactions.
Agentic architectures invert this relationship. The coordination intelligence migrates from human operators to software orchestrators, creating systems where autonomous agents initiate tasks, decompose complex objectives, allocate resources and manage interdependencies without human intervention. The human role shifts from operational coordination to strategic oversight and exception handling.
The Universal Coordination Revolution
This discontinuity extends beyond the technological sector into every industry that depends on complex coordination, such as healthcare systems coordinating patient care across multiple specialists and facilities, or financial institutions managing risk and capital allocation across diverse portfolios.
The common thread across these domains is coordination complexity, the exponentially difficult task of optimizing resource allocation and adapting to dynamic environments across multiple simultaneous objectives. Traditional approaches rely on hierarchical human coordination, rules-based automation or hybrid systems that still require substantial human oversight for complex decisions.
Agentic systems promise to dissolve these coordination bottlenecks by creating distributed intelligence networks capable of autonomous decision making, real-time adaptation and multi-objective optimization.
The Value Migration
The discontinuous nature of this transformation manifests most clearly in the migration of value capture from interface optimization to orchestration control. Traditional competitive moats centered on user experience design or feature comprehensiveness represent advantages that could be defended through superior human-computer interaction design.
Agentic architectures shift competitive advantage toward orchestration capabilities: the ability to coordinate autonomous agents and enable seamless integration between specialized agent networks. Companies that excel in these domains will capture disproportionate value not through superior interfaces but through superior coordination intelligence.
While headlines often focus on the power of new LLM models, this orchestration layer is where the real moats and valuations are being created.
Agentic AI Lessons For Investors
As artificial intelligence evolves from stateless tools to autonomous “synthetic colleagues,” investors must recalibrate how they assess opportunity and risk. Executives must rethink where their true moat lies.
This transformation is happening so rapidly, there is not a single static playbook I can offer that will guide you. Instead, this moment calls for constantly reassessing all your assumptions along with carefully understanding the evolving technology.
That said, let’s look briefly at a company that offers insight into the kinds of questions we should be asking: Salesforce.
The company is facing negative market sentiment (stock down over 28% YTD, as of this writing) despite trying to position itself at the forefront of the of agentic AI revolution, especially with the release of Agentforce 3, which includes many of the coordination and orchestration tools I referenced earlier.
Investors still likely value Salesforce on the traditional SaaS Rule of 40 benchmark. Because its growth rate has been slowing, the company has been penalized. But are investors missing the bigger value transformation?
Going forward, the company and investors should be digging into the new metrics that matter for agentic transformation. These focus on coordination complexity and workflow substitution rather than conventional software adoption patterns:
• Revenue Per Synthetic Colleague: This reflects outcome-based economics rather than seat-based pricing.
• Network-Enhanced ARR Growth: Consider traditional ARR x Agent Network Compounding Factor (how much faster ARR grows due to synthetic colleague interactions versus linear scaling).
• Context Moat: This measures a platform’s control over the semantic layer that enables intelligent decision making. This includes access to behavioral data, customer interaction histories and the contextual information that makes orchestration valuable rather than merely functional.
• Workflow Moat: This evaluates how deeply embedded a platform becomes in operational processes, particularly the degree to which removal would require fundamental business process redesign rather than simple vendor substitution.
Understanding the implications of agentic AI discontinuity requires that investors embrace a radical new mindset. To thrive in this new autonomous world, it is critical to move beyond traditional analytical frameworks toward new models that recognize coordination intelligence as the primary driver of competitive advantage in an agentic AI future.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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