Blue Ocean Opportunities In The Agentic Economy
Where to invest as AI goes agentic
Happy Sunday and welcome to Investing in AI. Be sure to check out the AI in NYC show, and our latest guest, Matt Mirman from Chat.dev. We analyze the weekly news in AI and also discuss NYC related startups and AI opportunities. Also if you are an OpenClaw user, check out the new Neurometric Clawpack. 30+ Small Language Models optimized for common OpenClaw tasks - unlimited tokens, just $8/mo.
Special Announcement: Before we go into the main content this week I wanted to highlight something I thought was pretty cool. My friend Charlie wrote a book about entrepreneurship and built a Claude AI companion guide with it. It’s a great new idea (I’m going to copy this for my book). Here’s the relevant info below, and here is the book.
Charlie O’Donnell’s Founder Unfriendly (Wiley) — the #1 Amazon bestseller in Business Entrepreneurship — is the no-BS guide to what investors actually think but won’t say out loud. The free companion guide at thankstoai.thisisgoingtobebig.com turns every chapter into Claude-powered exercises that help founders stress-test their pitch, decode investor signals, audit their own blind spots, and simulate investor conversations before the real thing. Buy the book and you unlock an AI coach trained on O’Donnell’s real founder feedback sessions — not a generic startup chatbot, but his actual judgment patterns applied to your specific fundraising situation. The book earns the AI’s authority. Worth stealing as a model for any author building in this space.
Blue Ocean Opportunities
This year has been the transition from chat based AI to agentic AI, primarily driven by OpenClaw and the hype behind related tools. It’s a structural discontinuity in AI.
The first wave of commercial AI (2022–2025) was Interface-centric: AI sat inside a chat window, returned text, and waited. Humans extracted value through interpretation and manual execution. That model produced real productivity gains—but it left the most expensive friction untouched: the gap between knowing and doing.
The second wave—already here as of Q1 2026—is Execution-centric. Agents don’t respond to queries; they complete objectives. They hold state across sessions, invoke tools via MCP (Model Context Protocol), delegate subtasks to specialized agents via A2A (Agent-to-Agent) protocols, and commit to outcomes without a human cosigning every step. The organizational implications of this shift are as significant as the move from mainframe to client-server, or from on-premise to SaaS.
The companies and VCs who misread this as “AI getting faster” will be in the same position as those who described the internet as “a faster fax machine.”
Part I: The Death of Middleware & the UI Abstraction Layer
The Core Mechanism of Disruption
Traditional SaaS created its moat through three stacked layers:
Data (stored in a proprietary schema)
Logic (workflow rules and business process automation)
Interface (the UI that made the above accessible to humans)
For two decades, Layer 3 (the UI) was where competitive differentiation lived—UX, design, adoption, and training. Salesforce didn’t win because of superior database architecture. It won because salespeople could actually use it.
MCP collapses Layer 3 as a competitive moat.
MCP is not a marginal improvement. It is a protocol-level bypass surgery. When an AI agent can connect directly to a CRM’s data layer, read pipeline state, update records, trigger workflows, and generate reports—all without a single pixel of UI rendering—the interface loses its function as the access mechanism. It becomes decorative.
A2A compounds this by eliminating human-mediated coordination. In a traditional enterprise, a sales operations manager “coordinates” between CRM, billing, ERP, and marketing automation—manually moving data and decisions across systems. With A2A, an orchestrator agent spawns a billing agent, a CRM agent, and a contract agent, passes structured context between them, and resolves the task. The “systems integrator” role—whether that’s a human or a middleware SaaS product—is bypassed.
The Specific Business Models at Risk
“UI-for-a-Database” SaaS — Companies whose core value proposition is a well-designed interface layered over data that could, with an MCP server, be accessed by any agent. Think: project management tools with no proprietary data network effects, basic CRM tools with shallow integration moats, legacy ITSM platforms.
The canary-in-the-coal-mine signal to watch: when enterprise buyers stop asking “does it have a mobile app?” and start asking “does it have an MCP server?” That shift happened in 2025 for technical buyers. It reaches procurement committees in 2026–2027.
The exception worth noting: SaaS companies with genuine data network effects (platforms where the data becomes more valuable as more users contribute to it—LinkedIn, Veeva, Toast) retain structural moats. The agent still needs the data; it just accesses it differently. The threat is to interface rent-seekers, not data asset holders.
Part II: The Sunset List
Five business archetypes facing structural obsolescence by 2027.
Part III: The Agentic Alpha — Three High-Growth Sectors
1. Agent Governance & Compliance Infrastructure
The problem being solved: In a world where an AI agent can execute a $500,000 vendor contract, provision cloud infrastructure, or initiate a wire transfer—without a human in the loop—the question of accountability becomes load-bearing infrastructure, not a compliance checkbox.
The current legal and technical architecture was not designed for this. Corporate authority matrices assume humans. Financial controls assume humans. Audit trails assume humans logged actions. None of those assumptions hold in a fully agentic environment.
The market being created:
Agent Identity & Authorization Protocols: Cryptographically signed agent credentials that encode scope of authority—what systems an agent can access, what transaction limits it carries, what data classifications it can touch. Think PKI certificates, but for autonomous decision-making authority. This is not a feature; it is foundational infrastructure.
Immutable Agent Audit Trails: Real-time, tamper-proof logging of agent reasoning chains, tool invocations, and decision points. When an agent makes a wrong procurement decision, the CFO’s legal team needs a reconstructible chain of custody. Companies building this infrastructure are positioning for the same role that SIEM vendors played in the cybersecurity market—mandatory, regulated, and defensible.
Autonomous Compliance Agents: Agents that monitor other agents in real-time for regulatory violations—GDPR data handling, SEC material information boundaries, HIPAA access controls. This is a meta-layer that major regulated industries (finance, healthcare, legal) cannot operate without. The irony and the opportunity: you need agents to govern agents.
Agentic Insurance Underwriting: A nascent but rapidly developing market for underwriting the financial risk of autonomous agent errors. As agents take on consequential financial actions, traditional errors & omissions policies don’t map cleanly. New products are needed.
Who is positioned to win: Startups with roots in identity infrastructure (not AI companies that add governance as a feature). Regulatory-native companies with existing relationships in financial services compliance. The risk is incumbents (Workday, ServiceNow) bolting this on—but bolt-ons historically lose to purpose-built solutions when the problem is structurally new.
VC signal: Any company that can credibly say “our product is required for a Fortune 500 to deploy agents in production” is in a mandatory spend category. That is a high-conviction investment thesis.
2. Vertical Agent Enablement Platforms
The insight: General-purpose foundation models (GPT-5, Claude, Gemini) are increasingly capable but carry generic context. The competitive moat of the next five years will not be built on which model you use—model performance is commoditizing faster than anyone predicted. The moat will be built on domain-specific context, tools, and compliance infrastructure layered on top of those models.
This is the “picks and shovels” play of the agentic economy, but more defensible than it sounds because the picks and shovels here require genuine domain expertise to build.
Three illustrative verticals where this is already crystallizing:
Healthcare Agent Enablement: Building agentic infrastructure for clinical and administrative workflows requires HIPAA-compliant data handling, integration with HL7/FHIR standards, clinical decision support guardrails that satisfy FDA oversight frameworks, and liability structures appropriate for medical contexts. A general-purpose AI company cannot drop a Claude API key into a hospital and call it a product. The company that builds the compliance wrapper, the EHR integration layer, and the liability-aware guardrails is the product—and the model is a commodity input.
Legal Agent Enablement: Jurisdiction-specific procedure knowledge, court filing integration, privilege-protection protocols, and bar association compliance requirements create structural barriers that cannot be replicated quickly. Agents that can draft, review, and file legal documents—but within a governance framework that satisfies malpractice standards—represent a defensible vertical platform.
Financial Services Agent Enablement: Fiduciary-grade reasoning guardrails, real-time regulatory constraint checking (Reg NMS, Basel III, FINRA rules), and immutable transaction audit trails turn agent deployment from a liability into a compliant workflow. The companies that crack this for mid-market financial services (below the tier that builds custom) will capture significant SaaS-replacement value.
The business model: These are not consulting firms. They are platforms—agent-ready data connectors, compliance-enforcing middleware, pre-built agent templates with domain expertise embedded, and the ongoing maintenance of those templates as regulations evolve. Recurring revenue, high switching costs, genuine expertise moats.
3. The Orchestrator Model: Outcome-as-a-Service
This is the most structurally radical business model emerging from the agentic transition. It deserves careful analysis because it looks superficially like outsourcing—and it is categorically different.
The traditional model: You license software. You staff the software with humans. You own the execution risk.
The Orchestrator Model: You purchase a guaranteed outcome. The orchestrator owns a fleet of specialized agents, manages their coordination, monitors for quality, handles exceptions, and charges based on successful outcome delivery—not on seat licenses or usage.
Concrete examples already taking shape:
Recruiting Orchestration: Instead of paying a recruiter 20% of first-year salary, you pay an outcome-based fee for a qualified hire—with an SLA on time-to-fill and a replacement guarantee. The orchestrator runs sourcing agents, screening agents, scheduling agents, and assessment agents. The buyer doesn’t care about the workforce composition; they care about the hire.
Revenue Operations Orchestration: Instead of a CRM license + SDR team + marketing automation + RevOps headcount, you pay a monthly fee for a qualified-meetings SLA. The orchestrator owns pipeline generation, qualification, and handoff quality.
Compliance Monitoring Orchestration: Instead of a compliance team + monitoring software + audit engagement, you pay for a “zero material regulatory violations” SLA backed by continuously operating agent infrastructure.
Why this is structurally new:
The risk transfer is fundamental. In a SaaS model, the vendor’s risk ends when the software works. In the Orchestrator Model, the vendor’s risk extends to the outcome. This requires the orchestrator to have genuine operational competence—not just technical competence. They must manage agent performance, handle edge cases, absorb errors, and improve the system continuously. It selects for operators, not just builders.
The incumbent threat: Accenture, McKinsey, and the major systems integrators see this clearly and are moving toward it. The question is whether an AI-native orchestrator can out-operate them before scale advantages accrue to incumbents. The window is 18–36 months.
Part IV: The Trust & Governance Gap — Who Wins the Principal-Agent Problem at Scale
The Structural Problem
Classical principal-agent theory describes the tension between a principal (who delegates) and an agent (who acts). Human organizations spend enormous resources on this problem: employment contracts, performance management, oversight hierarchies, compliance departments.
The agentic economy creates a principal-agent problem at machine speed and scale. A single enterprise might run thousands of AI agents simultaneously—each making micro-decisions, each potentially creating legal exposure, each representing the company to external parties. The oversight infrastructure for this does not exist.
The Human-in-the-Loop to Human-on-the-Loop Shift
“Human-in-the-loop” was the safety architecture of the first wave: humans approve every significant AI action. It was the right answer when AI was a recommendation engine. It is operationally unscalable when AI is an execution engine. A single agent workflow might invoke 50 decisions in 30 seconds. No human can maintain meaningful “in-loop” oversight at that speed.
“Human-on-the-loop” is the emerging architecture: humans set policies, define authority boundaries, review exception queues, and audit outcomes—but are not co-signatories to every action. This requires:
Policy-as-code: Authority limits encoded in machine-readable form, enforced at the agent level.
Exception routing: Agents that recognize when they’re outside their authority envelope and halt for human review.
Outcome monitoring: Statistical sampling and anomaly detection on agent behavior streams.
Accountability trails: Reconstructible records of why an agent took a specific action, what context it had, and which policy it was operating under.
The New Market Categories
Agentic Auditing Firms: As regulatory frameworks mature (the EU AI Act’s requirements for high-risk AI systems already gesture at this), enterprises will need third-party attestation of their agent governance practices. This is the SOC 2 market for agentic AI—a certification and audit industry that does not yet exist in structured form but will be required for regulated industries within 24 months.
Agent Policy Platforms: Governance tooling that allows enterprises to define, version, enforce, and audit agent authority policies—analogous to IAM (Identity and Access Management) in cloud security. The company that builds the “Okta for agent authority” occupies a mandatory infrastructure position.
Cross-Organizational Agent Trust Networks: When Company A’s agent needs to interact with Company B’s agent to complete a transaction (e.g., an AI procurement agent negotiating with an AI sales agent), there is no established trust infrastructure. What credentials does each agent carry? How does each company verify the other’s agent is authorized? The protocols here (nascent extensions of A2A) will require trust intermediaries—analogous to certificate authorities in the SSL ecosystem.
Part V: Buy / Hold / Sell — VC Guidance for the 2026 Agentic Landscape
BUY — High Conviction
Agent Governance & Identity Infrastructure Mandatory spend for any enterprise that deploys agents in production. The regulatory pressure is real and accelerating. Companies solving cryptographic agent identity, authority scoping, and immutable audit trails are in the “you can’t ship without this” category. Look for teams with backgrounds in PKI, IAM, or financial compliance technology—not just AI.
Vertical Agent Enablement Platforms in Regulated Industries Healthcare, legal, and financial services represent the largest TAMs with the deepest compliance moats. The companies that build domain-specific agent infrastructure (data connectors, guardrails, liability frameworks) in these verticals will have switching costs that rival legacy EHR or core banking systems. The key diligence question: does the founding team have domain credibility, or are they AI engineers with a research tab open?
Outcome-as-a-Service Orchestrators with Proven Unit Economics The category is right, but the variance is enormous. The diligence focus must be on operational competence—what happens when the agents fail? How do they handle exceptions? Do the SLA commitments have defensible economics? The companies that crack the operational discipline of outcome delivery will become the next generation of professional services giants. Those that don’t will produce spectacular failures.
MCP/A2A Tooling & Developer Infrastructure Protocol adoption creates tooling demand. The companies building developer experience, observability, and optimization layers for MCP and A2A workflows are in a picks-and-shovels position in a protocol land-grab. Be early, be selective—protocol consolidation will happen—but the winners here will be infrastructure cornerstones.
HOLD — Wait for Clarity
Major Cloud Platforms (AWS, Azure, GCP) They will win agent infrastructure spend at the commodity layer—compute, model hosting, storage. They will lose the value-added layers to specialized competitors. Net position: hold existing positions, watch whether their agent-native services (Amazon Bedrock Agents, Azure AI Studio) gain traction with enterprises or get disrupted from below.
Established Enterprise Software with Agentic Pivot Narratives (Salesforce, ServiceNow, Workday) These companies have the distribution, the data relationships, and the enterprise trust to compete in the agentic layer—if they execute. Their risk is architectural conservatism and integration debt. Watch for signs of genuine agentic capability (not AI wrapper features) by Q3 2026. If they show it, upgrade to hold or buy. If they’re still selling “AI copilots,” sell.
Foundation Model Providers (excluding frontier research leaders) Model performance is commoditizing faster than the pricing models have adjusted. The middle tier—models that are good but not the best—faces compression. Hold positions in frontier leaders; the second tier faces a difficult value proposition to articulate.
SELL — Exit or Avoid
Pure-Play SaaS UI Companies Without Proprietary Data Moats If the core product is “a well-designed interface for data that lives elsewhere,” the MCP bypass threat is existential and the timeline is short. The question for each company: what would a customer lose if they replaced the UI with an agent that talked directly to the database? If the answer is “mostly just habit,” the company is a sell.
Legacy RPA Platform Vendors (Business Model Defense) The underlying technology is being disrupted by something architecturally superior. The customer relationships and distribution may retain value—but only if acquired by or merged with agentic AI companies quickly. Standalone legacy RPA as an investment thesis has a deteriorating runway.
“Response-Only” AI Product Companies Companies whose entire value proposition is generating text responses—without action capability, tool integration, or workflow completion—are selling the first wave of AI in the second wave. The product category has value; the standalone company does not. These are acqui-hire targets, not growth investments.
Generalist AI Chatbot Consultancies Firms whose business model is “we will build you a ChatGPT wrapper” have a 12-month window at most. The capability to deploy conversational AI is becoming a commodity faster than any consulting margin can withstand. Avoid.
Closing Thesis
The strategic insight that separates the winners from the losers in this transition is not technical—it is architectural. The organizations and investors who understand that agentic AI is an organizational design problem as much as a technology problem will be positioned correctly.
The shift from Human-in-the-loop to Human-on-the-loop is not about removing humans from decisions. It is about moving humans up the abstraction stack—from approving individual actions to designing the policies, authority structures, and exception frameworks that govern thousands of autonomous actions. That shift requires new infrastructure, new governance frameworks, new liability structures, and new professional disciplines.
The companies building those foundations—governance tooling, vertical compliance infrastructure, outcome-delivery operations—are building the load-bearing walls of the next enterprise technology era. Everything else is interior decoration.
Thanks for reading.