Agentic Era Part 4: The Exponential Economics of Agentic Applications

Orchestration enables new applications that will reshape software company valuations in the age of synthetic colleagues. The Agentic Value Framework addresses the challenge of a post-Rule of 40 world.

Credit: Milad Fakurian

The marketing department at a mid-sized software company recently made a remarkable decision. Instead of hiring three new specialists to handle content creation, lead nurturing, and campaign analytics, they deployed an agentic AI system for content orchestration and customer interaction management.

The system goes beyond augmenting the human team to increase productivity. It has enabled the creation of “Synthetic Colleagues” that effectively become the marketing team, managing everything from brand-compliant content creation to personalized customer journey orchestration.

Synthetic Colleagues are an emerging pattern that appears likely to reshape software markets over the next decade. In the Agentic Era, we are observing a transition from applications that enhance human productivity to applications that can replace entire organizational functions and assume responsibility for complete workflows.

Traditional AI applications compete for software budgets, typically representing 2-5% of enterprise spending. Agentic applications potentially address much larger budget categories—the salaries, benefits, and operational costs of entire teams. They also facilitate the creation of teams that do not exist because the labor is hard to find. When software can effectively perform the functions of a marketing department or customer service division, the addressable market extends significantly beyond traditional software spending toward the broader costs of organizational cognitive work.

This layer of Agentic AI Applications represents one of the most enticing targets for investors because the economic implications are substantial. A recent report from Market.us forecasts the Agentic AI market to grow from approximately $5.2 billion in 2024 to almost $200 billion by 2034. At its recent Build conference, Microsoft unveiled a series of initiatives to give developers the tools to build Agentic AI applications, a push that places agents at the center of the company’s roadmap.

The result of this phenomenon is a Discontinuity that renders obsolete such classic benchmarks as the Rule of 40. Navigating this turbulent period of transformation requires a new framework. I take the first steps toward addressing the impact of Agentic applications by introducing the Agentic Value Framework. This is my starting point for rethinking valuations and metrics.

The Architecture of Synthetic Colleagues

What makes agentic applications fundamentally different from previous software generations is their architectural sophistication. To understand this distinction, we must first clarify what separates individual AI agents from true agentic systems—and why only the latter qualifies as a "synthetic colleague."

Individual AI Agents: The Specialized Assistant

Traditional AI agents are purpose-built for narrow, well-defined tasks. Think of them as highly specialized assistants: a customer support agent that handles tickets, an email filtering agent that manages your inbox, or a scheduling agent that coordinates meetings. These agents exhibit three core characteristics: autonomy within their specific domain, task-specificity that allows high-performance optimization, and reactivity to environmental inputs.

However, individual agents face fundamental limitations. They operate in isolation, lack persistent memory across interactions, and cannot decompose complex, multi-step objectives that require coordination across different skill sets. A customer support agent can resolve individual tickets efficiently, but it cannot simultaneously analyze support patterns, update knowledge bases, and coordinate with product teams—the kind of integrated workflow that defines actual customer success departments.

Agentic Systems: The Synthetic Colleague

Agentic systems represent a categorical leap from isolated agents to collaborative ecosystems. These are distributed networks of specialized agents that share persistent memory, coordinate through structured communication protocols, and pursue decomposed goals under orchestrated supervision.

Consider a real-world example from recent research: an automated grant proposal system that functions as a synthetic research department. The system consists of four specialized agents—a literature retriever, a compliance alignment agent, a methodology designer, and a formatting specialist—coordinated by a meta-agent orchestrator.

The retriever agent scans funding databases and extracts successful proposal patterns. The alignment agent ensures methodology matches specific NSF solicitation requirements. The methodology agent designs research approaches based on extracted patterns and requirements. The formatting agent structures documents per compliance guidelines. Throughout this process, a persistent memory layer stores evolving drafts, funding agency templates, collaborator feedback, and iterative improvements across multiple sessions.

What makes this agentic rather than simply multi-agent is the emergent behavior: the system exhibits decision-making, learning, and adaptation that exceeds any individual component. It doesn't just execute predefined workflows—it develops new strategies based on success patterns, adjusts approaches based on feedback, and maintains institutional memory that improves over time.

While still early, this architectural sophistication is visible across emerging agentic platforms. Within the field of high-growth startups: Writer, Glean, Sierra at horizontal level - Harvey, Hippocratic.ai at vertical level. Amongst incumbents: AgentForce, NowAssist. And the list continues across hundreds of companies racing to develop this new form of software.

The Orchestration Layer: Where Synthesis Happens

The critical innovation enabling synthetic colleagues is the orchestration layer—sophisticated coordination systems that manage task decomposition, dependency resolution, and conflict arbitration across agent networks. Modern orchestrators don't just route requests; they maintain shared context, evaluate agent performance, and dynamically reassign responsibilities based on workload and capability.

The emergence of protocols like Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) represents the infrastructure breakthrough that makes this orchestration possible. MCP standardizes how agents access external resources and share context, while A2A enables direct agent-to-agent communication with task management and multi-modal coordination. These protocols solve the fundamental coordination challenges that prevented true multi-agent collaboration: standardized communication at machine speed with persistent state management.

This architectural sophistication is what transforms individual agents into synthetic colleagues. Where traditional software requires human coordination between different tools and systems, agentic applications coordinate themselves, maintaining the persistent context, institutional memory, and collaborative decision-making that characterizes effective human teams.

The Evolution Beyond Data Moats

The emergence of applications based on agentic systems coincides with a fundamental shift in competitive dynamics. Traditional software companies built moats through proprietary data accumulation, believing that exclusive datasets created sustainable advantages through better model performance and user experiences.

Agentic platforms operate under different principles. Foundation models trained on broad datasets and accessible via APIs have commoditized basic intelligence capabilities. The competitive advantage no longer stems from possessing unique data, but from orchestrating how synthetic colleagues access, combine, and act upon information in real-time workflows.

This shift is becoming evident in strategic partnerships across the industry. Glean's integration with Snowflake, announced in May 2025, demonstrates the new approach. Rather than building proprietary datasets that would require massive capital investment and years of accumulation, Glean connects directly to existing enterprise data infrastructure. The partnership enables synthetic colleagues to access and reason over data warehouses in real-time without requiring data duplication or custom model training.

This partnership model reveals the new competitive landscape. Data itself becomes infrastructure—valuable but not differentiating. The strategic advantage migrates to the orchestration layer that enables synthetic colleagues to understand context, maintain memory across interactions, and coordinate actions within specific workflow environments.

However, this does not mean all data advantages disappear. Proprietary behavioral data, unique process insights, and customer interaction patterns retain strategic value when they enable better synthetic colleague coordination or workflow optimization. The distinction lies between raw information assets and actionable intelligence that enhances agentic system performance.

The Context Moat: Controlling the Semantic Layer

The context moat emerges from controlling the semantic layer that agents use to understand, remember, and personalize their interactions. Context-rich agents fundamentally outperform generic alternatives because they can make decisions based on accumulated understanding rather than isolated inputs. An agentic system embedded in Notion doesn't just access documents—it understands project relationships, team dynamics, and workflow patterns that inform every interaction.

The strategic advantage belongs to platforms that sit closest to decision-making and real-time workflows. Notion for knowledge work, Microsoft for productivity workflows, Salesforce for customer relationship management—these platforms have natural context moats when they orchestrate correctly.

The Workflow Moat: Embedding into Actual Tasks

Workflow moats arise from deep integration into the specific processes where value gets created. Consider the difference between an AI agent that generates marketing content versus one that drives complete revenue operations—managing lead qualification, nurturing sequences, and deal progression. The content generator provides utility; the revenue operations agent becomes infrastructure.

The firms that control the orchestration layer—where agents are created, connected, and evaluated across workflows—will be valued like infrastructure companies rather than software vendors.

Evolving Beyond the Rule of 40

The rise of synthetic colleagues and Agentic Applications brings us back to the question of how to rethink the metrics that matter when it comes to measuring success and valuation.

The Rule of 40 has served the software industry exceptionally well for over a decade. By establishing that Revenue Growth % + FCF Margin % should exceed 40%, it provided a clear framework for evaluating SaaS company efficiency and balancing growth investments with profitability. This metric guided countless investment decisions and strategic choices that built the foundation of today's software economy.

However, the Rule of 40 was designed for a different economic model, built on three assumptions that agentic platforms challenge:

- Linear Scaling Assumption: More revenue requires proportional increases in costs (sales teams, support staff, infrastructure). Synthetic colleagues scale exponentially. Each additional agent doesn't just add its individual capacity, it enhances the entire network's capability through shared learning and coordination.

- Growth-Profitability Trade-off: Traditional SaaS faces a choice between investing in growth or optimizing for profitability. Agentic systems eliminate this trade-off—network effects simultaneously drive growth and improve margins as coordination reduces operational costs.

- Human-Mediated Value Creation: The Rule of 40 assumes human bottlenecks limit scaling velocity. Synthetic colleagues operate at machine speed with perfect coordination, removing traditional scaling constraints entirely.

Introducing the Agentic Value Framework

Agentic platforms require a completely different approach to revenue calculation and growth measurement. Traditional ARR calculations assume linear scaling where each customer contributes proportional revenue. Agentic platforms generate exponential value through network effects that compound as synthetic colleagues coordinate and learn from each other.

As I argued previously, the advent of GenAI was already making the Rule of 40 obsolete, replacing it with the Rule of 55 that captures the potential productivity gains from AI. The Agentic Era explodes the Rule of 55 because of the potential scope of activities these applications can perform.

The challenge now is establishing a new benchmark formula at a time when the parameters, technology, protocols, and use cases are still in the embryonic stages.

As I am trying to grapple with this moment of Discontinuity, I’ve created the Agentic Value Framework, a starting point that I intend to refine in the coming months as we gather more empirical evidence:

(Network-Enhanced ARR Growth Percentage) + (FCF Margin Percentage) = Agentic Performance Threshold

Let’s look more closely at the critical components of the formula.

Base ARR Calculation for Agentic Platforms:

Base ARR = (Outcome per Synthetic Colleague) × (Platform Value Capture Percentage) × (Synthetic Colleagues per Customer) × (Number of Customers)

This foundation reflects outcome-based economics rather than seat-based pricing. If each synthetic colleague generates $200,000 in annual business outcomes and the platform captures 20% of that value, the revenue per synthetic colleague equals $40,000. A customer deploying one finance synthetic colleague and one marketing synthetic colleague contributes $80,000 to Base ARR. With 1,000 customers, Base ARR reaches $80 million.

Network-Enhanced ARR Growth:

The agentic evolution necessity lies in measuring how network effects accelerate ARR growth beyond linear customer acquisition. Traditional software grows ARR proportionally with customer additions. Agentic platforms experience compounding growth as existing synthetic colleagues become more valuable through coordination and cross-customer learning.

Network-Enhanced ARR Growth = (Traditional ARR Growth Percentage) × (Agent Network Compounding Factor)

The Agent Network Compounding Factor measures how much faster ARR grows due to synthetic colleague interactions versus linear scaling. Early-stage agentic platforms might demonstrate a 1.3x compounding factor, meaning ARR grows 30% faster than customer additions due to coordination benefits. Mature platforms could achieve 2.0x or higher compounding factors as synthetic colleagues develop sophisticated interaction patterns and cross-customer learning accelerates capability improvements.

Like Rule of 40, this formula does not aim to replace DCF metrics which remain the core foundation but rather measure one of the components of an agentic company’s moat.

However, this framework replaces the Rule of 40's linear assumptions with exponential network effects measurement. The Agentic Performance Threshold should exceed traditional benchmarks—potentially 60% to 100%—reflecting the superior economics available to platforms that successfully coordinate synthetic colleague networks.

Early market signals suggest investors are already recognizing the potential for exponential value creation in agentic platforms. Harvey AI, a legal technology startup, is reportedly in discussions for funding at a $5 billion valuation, according to recent reports. This round, along with similar high valuations across AI-enabled platforms, indicates growing investor recognition that companies deploying coordinated AI capabilities in specific workflow domains may generate value through different mechanisms than conventional software businesses.

Whether these valuations reflect sustainable agentic platform economics or broader market enthusiasm for AI capabilities remains to be demonstrated through operational performance over time.

The Orchestration Platform Imperative

The Agentic Value Framework reveals why orchestration platform control becomes the ultimate strategic imperative. Traditional software companies optimize for user acquisition and retention. Agentic platforms must optimize for coordination dominance.

Orchestration platforms capture value through four compounding mechanisms:

· Direct Outcome Capture: Revenue from synthetic colleagues deployed on the platform

· Network Coordination Premiums: Additional value from coordinated synthetic colleague networks

· Integration Rents: Fees from other platforms routing through the orchestration layer

· Learning Network Effects: Competitive moats from cross-customer synthetic colleague improvement

The winner-take-all dynamics are stronger than traditional software markets because coordination complexity creates natural monopolization. Customers consolidate on dominant orchestration platforms to maximize synthetic colleague coordination efficiency.

This explains why we're likely to see multiple companies achieve $1 trillion valuations in the agentic era—not through incremental software improvements, but through orchestration platform dominance that captures exponential value from network-coordinated synthetic colleagues.

The Strategic Implications

The transition to agentic platforms creates complex competitive dynamics. Incumbent software companies possess significant advantages through existing customer relationships and substantial resources, but face architectural constraints from legacy systems designed for human-centric workflows. Foundation model providers control core reasoning capabilities but often lack workflow integration.

The most compelling long-term position may belong to AI-native startups that can design their entire technology stack from first principles for agentic coordination. These companies can optimize data structures specifically for persistent cross-agent memory, build communication architectures for high-frequency agent interactions, and structure their business models around outcome-based value creation rather than seat-based licensing.

This architectural advantage compounds as system complexity increases, suggesting that ultimate competitive success will flow to companies building agentic systems from their foundational architecture upward.

This transition isn't without risks. As agentic systems scale, they face amplified coordination challenges, error cascades where one agent's mistake propagates through the entire system, and emergent behaviors that weren't explicitly programmed. Unlike traditional software failures, agentic system failures can be subtle, systemic, and difficult to diagnose.

The distributed nature of these systems creates accountability gaps when multiple agents interact to produce outcomes, making it difficult to assign responsibility for errors or unintended consequences. This creates both liability and trust challenges, particularly in high-stakes domains.

However, these risks are architectural problems, not fundamental limitations. Solutions are emerging: retrieval-augmented generation for grounding, tool-based reasoning for accuracy, memory architectures for continuity, and governance frameworks for accountability. The companies that solve coordination at scale will capture disproportionate value.

Software Redefined

The agentic application represents more than an evolutionary step in software development. It's a categorical redefinition. We're moving from tools that amplify human capabilities to systems that replace human functions entirely.

This isn't simply about efficiency gains or cost reduction. It's about fundamentally restructuring how work gets done, who (or what) does it, and how value gets created and captured in digital systems. When applications become co-workers, the entire software industry resets.

The companies that recognize this discontinuity and rebuild their architectures accordingly won't just capture market share. They'll redefine what markets are possible. Those that treat agentic capabilities as incremental features will find themselves competing against synthetic departments that never sleep, never quit, and continuously improve.

The agentic era has begun. The question isn't whether software will replace human cognitive work. It's which companies will orchestrate that replacement most effectively.