The Agentic Economy: Why the Next Tech Winners Are Beyond Legacy SaaS
The Agentic Economy: Why the Next Tech Winners Are Beyond Legacy SaaS
Author: Zion Zhao Real Estate | 88844623 | ็ฎๅฎถ็คพๅฐ่ตต | wa.me/6588844623
Author’s note: This essay is written for education and market literacy, not as financial advice or a solicitation to buy or sell any security. Markets can fall as well as rise, and past performance is not indicative of future results. Educational analysis only. Not financial advice, not a recommendation to buy or sell any security.
TL;DR: From SaaS Seats to AI Workers: The New Stack for Payments, Identity, and Security
Wall Street is debating whether legacy, seat-based SaaS leaders can recover after recent selloffs. That question matters, but it risks missing the larger shift: software is moving from “tools humans use” to “workers organizations deploy.” The next major opportunity is the agentic layer, where AI agents can plan, use tools, execute actions, and coordinate across workflows, changing the economic unit from seats to outcomes, transactions, and throughput.
This transition forces a rebuild of the digital stack around autonomous action. Payments must support delegated, constrained, auditable spending. Early signals include Visa’s Trusted Agent Protocol, aimed at verifying legitimate AI shopping agents for safer commerce, and Stripe’s agentic commerce initiatives such as Shared Payment Tokens, which enable permissioned payments without exposing underlying credentials. Identity must evolve beyond human logins to machine delegation, fine-grained authorization, and verifiable claims. Security must harden as autonomy expands the attack surface, increasing risks like tool misuse, prompt injection, and supply chain vulnerabilities, making Zero Trust controls and robust auditability more central.
The rollout is best understood in phases. Phase 1 is copilots replacing individual tasks (drafting, summarizing, coding assistance), boosting productivity without reorganizing firms. Phase 2 is workflow replacement, where agents own end-to-end processes such as onboarding, billing, procurement triggers, and portions of legal and customer operations, driving meaningful organizational redesign. Phase 3 is agent-to-agent commerce across firms, where supplier, manufacturer, and merchant agents coordinate continuously, compressing time and removing “business hours” constraints. In this phase, trust infrastructure becomes decisive.
The “new OS” opportunity is less about replacing consumer operating systems and more about enterprise agent runtimes: policy-driven execution, sandboxing, orchestration, telemetry, and audit logs. Hardware also re-enters the picture via hybrid deployments. Sensitive, latency-critical, or cost-optimized workloads can drive on-prem and desk-side AI compute alongside cloud scale.
Bottom line: the key debate is not whether old SaaS rebounds, but who captures value in the rebuilt trust, payments, identity, security, runtime, and compute layers that make an agentic economy viable.
AI agents are reshaping how companies hire, spend, secure data, and run operations. This shift matters for Singapore property buyers, sellers, landlords, tenants, and investors because it can influence employment trends, tenant demand from high growth sectors, office and industrial leasing needs, and capital flows into safe, well governed markets like Singapore. It also accelerates decision cycles, making pricing, timing, and negotiation strategy even more important in a fast moving environment.
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Thesis
Wall Street is currently debating whether legacy, seat based SaaS leaders can reignite growth after a selloff. That debate matters, but it risks missing the bigger structural shift: software is moving from “tools humans use” to “workers organizations deploy.” In other words, the next major profit pool is not just better SaaS. It is the agentic layer, plus the new trust, payments, identity, security, infrastructure, and operating primitives required to let autonomous software act safely at scale.
This essay reframes the opportunity through a systems lens: what changes when AI agents can plan, call tools, execute actions, and coordinate with other agents in continuous workflows (Yao et al., 2023; Schick et al., 2023). (arXiv)
1) Why the “Old SaaS” framing is incomplete
Traditional SaaS is fundamentally interface driven. Value is captured through:
Seats (per user pricing)
Workflows that assume a human in the loop
A GUI optimized for human attention, clicks, and permissions
This model was rational in a world where “work” meant humans operating software. But agentic systems invert the relationship. When agents perform tasks directly, the economic unit is no longer a seat. It is an outcome, a transaction, a workflow completion, or a measurable unit of operational throughput.
That shift matters because pricing, distribution, and moats change:
Pricing migrates from seats to usage and outcomes (metered actions, completed tasks, verified transactions).
Distribution migrates from end users to orchestration layers (the agent runtime becomes the customer, not the human).
Moats shift toward trust infrastructure (identity, authorization, auditability, payment rails, policy enforcement, and secure execution).
Economically, this resembles task reallocation at scale: automation displaces labor from specific tasks while creating new task categories and complementary demand (Acemoglu & Restrepo, 2019). (American Economic Association)
2) What the “agentic layer” actually is (and what it is not)
“Agentic” is often used loosely, so it is worth grounding it technically.
Copilot vs agent
A copilot:
Suggests content, code, or summaries
Improves a human workflow
Usually requires explicit human confirmation for critical actions
An agent:
Plans toward goals
Uses tools (APIs, databases, payment rails, enterprise systems)
Acts and then observes results, iterating until completion
Can be deployed as a persistent worker with delegated authority
Research frameworks such as ReAct (reasoning plus acting) and Toolformer (models learning to use tools) formalize the core capabilities behind agentic behavior (Yao et al., 2023; Schick et al., 2023). (arXiv)
Surveys of LLM based autonomous agents and multi agent systems highlight rapid progress, common architectures, and practical challenges (Wang et al., 2023; IJCAI survey, 2024). (arXiv)
The key insight for investors and operators
Once agents can reliably execute workflows, they stop being “features inside SaaS” and become an economic layer that demands its own:
Trust and identity primitives
Payment and settlement mechanisms
Security controls and monitoring
Compute and deployment patterns (cloud and on premises)
Agent optimized operating environments
3) The stack that must be rebuilt: trust first, then scale
My strongest point is not “vibe coding.” It is that autonomous action requires trustable delegation.
If agents can buy inventory, negotiate contracts, provision cloud resources, or initiate refunds, then the central questions become:
Who authorized this agent?
What is it allowed to do, under what limits?
Can the merchant or counterparty verify the agent is legitimate?
Can the organization audit decisions, prevent fraud, and resolve disputes?
These are not optional details. They are the rails of the agent economy.
4) Payments for agents: why Visa and Stripe are early “tell” signals
Payments is a useful case study because it forces clarity: money movement is where autonomy meets accountability.
Visa: Trusted Agent Protocol (TAP)
Visa announced the Trusted Agent Protocol as an ecosystem led framework to help merchants distinguish legitimate AI shopping agents from malicious automation and rogue bots, and to enable safer agent driven checkout (Visa, 2025). (investor.visa.com)
Visa also publishes developer facing materials and a reference implementation, emphasizing cryptographic verification and standardized mechanisms for agent authentication (Visa Developer Platform, 2025). (developer.visa.com)
Visa’s public announcement was dated October 14, 2025 (Visa, 2025). (investor.visa.com)
What TAP signals strategically:
A recognition that agent traffic is rising and merchants need bot differentiation tools
A shift from “fraud prevention for humans” to “fraud prevention for autonomous actors”
A move toward cryptographic attestations as a commerce primitive
Stripe: Shared Payment Tokens and the “agentic commerce suite”
Stripe has been explicit that agentic commerce needs new payment primitives. It describes Shared Payment Tokens (SPTs) as a way for agents to initiate payments using a buyer’s permission and preferred payment method without exposing underlying credentials, with constraints like scope, time, and amount (Stripe, 2025a; Stripe, 2025b; Stripe, 2025c). (Stripe)
This is important because it shifts payment enablement from:
“User enters card details”
to“User delegates spending authority, bounded and auditable, to an agent”
That is not a minor UX tweak. It is an authorization architecture.
5) Identity and authorization in an agentic world: from “human login” to “machine delegation”
If agents are workers, they need identities, roles, permissions, and governance.
The modern baseline: risk based digital identity
NIST’s Digital Identity Guidelines Revision 4 (SP 800-63-4) emphasizes risk based identity assurance, authentication, and federation requirements (NIST, 2025a). (NIST Pages)
This matters because agents will force organizations to treat identity as a continuous control system, not a one time login.
Delegation primitives already exist, but must be re composed
Many of the building blocks are familiar:
OAuth 2.0 authorization framework (Hardt, 2012). (IETF Datatracker)
JWT as a compact, signed claims container (Jones et al., 2015). (IETF Datatracker)
OpenID Connect for identity on top of OAuth (Sakimura et al., n.d.). (OpenID Foundation)
Passwordless authentication standards such as WebAuthn (W3C, 2019). (W3C)
But agents introduce new requirements:
Non human principals acting at high frequency
Fine grained policy constraints (purpose limitation, spend caps, allowed counterparties)
Continuous verification and anomaly detection
Auditability for disputes and compliance
This is where verifiable credentials and cryptographic attestations become attractive, because they allow machine verifiable claims across organizations (W3C, 2025). (W3C)
6) Security: why the agentic layer expands the attack surface
Autonomous systems multiply risk because they compress time and increase the number of actions taken per unit of human oversight.
“More autonomy” means “more ways to be exploited”
Agent systems are vulnerable to:
Prompt injection and tool misuse
Insecure output handling (agents executing untrusted content)
Supply chain attacks (models, plugins, tool APIs)
Denial of service via expensive tool calls or looping behavior
The OWASP Top 10 for LLM Applications catalogues these emerging classes of vulnerabilities and mitigations (OWASP, 2024/2025). (OWASP)
Zero Trust becomes the default posture
In an agentic enterprise, you cannot assume internal traffic is safe. NIST’s Zero Trust Architecture (SP 800-207) outlines a paradigm where each access request is continuously authenticated, authorized, and evaluated for risk (Rose et al., 2020). (NIST Computer Security Resource Center)
Secure development and supply chain controls become strategic moats
Because agents orchestrate many tools, the integrity of dependencies matters:
NIST SSDF (SP 800-218) provides secure software development recommendations (Souppaya et al., 2022). (NIST Computer Security Resource Center)
NIST SP 800-161 Rev. 1 addresses cybersecurity supply chain risk management (Boyens et al., 2024). (NIST Computer Security Resource Center)
In an agentic economy, these controls become part of the product, not just internal hygiene.
7) “Agents do not need human operating systems” is directionally right, but incomplete
Human oriented operating systems are not optimized for agents. The deeper point is that GUI centric design becomes less important relative to:
API access
Policy engines
Sandboxed execution
Telemetry and audit logs
Orchestration runtimes (schedulers, workflow engines, tool routers)
In practice, agents can run on Linux, containers, serverless, or specialized runtimes today. The “new OS opportunity” may look less like a consumer OS replacement and more like:
An enterprise agent runtime layer
A policy driven execution environment
A standardized inter agent protocol stack
Multi agent research and surveys emphasize communication, coordination, memory, and tooling as central engineering problems (IJCAI survey, 2024; Wang et al., 2023). (IJCAI)
8) The three phase rollout: a practical adoption curve
My three phase framework is useful because it maps technological capability to organizational change.
Phase 1: task copilots
Agents assist with bounded tasks:
Drafting, summarizing, searching
Code suggestions and refactors
Customer support triage
This phase mostly improves productivity without reorganizing the firm. It aligns with early evidence that AI can raise productivity, though impacts differ by task, industry, and worker role (OECD, 2024a). (OECD)
Phase 2: workflow replacement
Agents begin to own end to end workflows:
Customer onboarding
Billing and collections
Basic legal drafting and review (with human oversight)
Inventory planning and procurement triggers
Marketing operations and experimentation loops
This is where the economic model changes: organizations redesign processes around autonomous execution. That is exactly the “task content of production” reallocation described in task based labor economics (Acemoglu & Restrepo, 2019). (American Economic Association)
Phase 3: agent to agent commerce across firms
This is the most disruptive phase:
Supplier agents negotiating replenishment
Distributor agents coordinating fulfillment
Merchant agents responding to demand signals
Service agents provisioning capacity and settling payments
Here, the trust layer becomes decisive. Protocols like Visa’s TAP and Stripe’s agentic payment primitives are early attempts to build cross firm verification and delegation (Visa, 2025; Stripe, 2025a). (investor.visa.com)
9) Hardware and the “on premises comeback”: why local compute returns
I would point to a “dark horse” idea: agents may require dedicated hardware, sometimes on premises.
This is credible for three reasons:
Data sensitivity and governance: regulated industries and critical workflows may require tighter control.
Latency and reliability: some processes cannot depend on external calls.
Cost optimization: predictable inference workloads can be cheaper when amortized.
We now see purpose built local AI systems marketed explicitly for development, fine tuning, and inference, including NVIDIA’s DGX Spark positioned as a desk side AI workstation with a full software stack (NVIDIA, n.d.). (NVIDIA)
Similarly, Apple markets high memory, high bandwidth desktops like Mac Studio for pro workloads, including local AI capabilities (Apple, 2025). (Apple)
The strategic implication is not “everyone leaves the cloud.” It is hybrid deployment: cloud for scale and burst, local for sensitive, latency critical, or cost optimized steady state workloads.
10) Who wins, by phase: an investment framework without stock picking
To stay compliant and objective, the correct output is not “buy these tickers.” It is a category map of where value accrues.
Phase 1 winners: distribution and augmentation
Foundation model providers and developer platforms
Productivity layers embedded in existing suites
Data access and retrieval infrastructure
Phase 2 winners: workflow ownership and trust control points
Agent orchestration platforms (tool routing, memory, workflow engines)
Observability and audit layers (logs, replay, evaluation)
Identity, authorization, and policy enforcement products
Security platforms aligned to Zero Trust and AI specific threat models (NIST, 2020; OWASP, 2024/2025). (NIST Computer Security Resource Center)
Phase 3 winners: cross firm protocols and settlement rails
Payments networks and tokenization primitives for delegated spend (Visa, 2025; Stripe, 2025a). (investor.visa.com)
Verifiable credential ecosystems for machine verifiable claims (W3C, 2025). (W3C)
Supply chain integrity and secure software provenance controls (NIST, 2022; NIST, 2024). (NIST Computer Security Resource Center)
This is why “old SaaS vs new SaaS” is the wrong debate. The larger prize is the full stack rebuild around autonomous action.
11) A disciplined conclusion: Wall Street debates the past while the stack shifts underneath
Legacy SaaS is not dead. Many incumbents will adapt. But the market’s current narrative often underweights the deeper structural change: the unit of labor is expanding to include software agents that can operate continuously, transact, and coordinate.
When that happens:
Payments must support delegated, constrained, auditable spend
Identity must expand from human login to machine delegation and attestation
Security must harden against AI specific attack patterns
Compute must flex across cloud and on premises deployment
Operating environments must prioritize policy, orchestration, and audit over GUI
Visa’s Trusted Agent Protocol and Stripe’s Shared Payment Tokens are not isolated product announcements. They are early indicators that the agentic layer is becoming real infrastructure, not just a feature (Visa, 2025; Stripe, 2025a). (investor.visa.com)
And once the infrastructure is being built, the direction of travel is no longer speculative, even if timelines and winners will be uneven.
Disclaimer: This essay is for informational and educational purposes only. It does not constitute investment advice, a recommendation, or a solicitation to buy or sell any security. Any forward looking views are uncertain and should be evaluated alongside primary sources, professional advice, and your personal risk tolerance.
In an agentic economy, capital moves faster, narratives change quicker, and risk can reprice in weeks, not years.
That is exactly why Singapore property remains a cornerstone asset for families and institutions seeking stability, rule of law, and durable demand. But stability does not mean simplicity. Whether you are investing, relocating, or planning a child’s education journey, the best outcomes come from decisions grounded in macro awareness, portfolio construction, and legal precision.
I support international clients from China, Southeast Asia, and Singapore, including ultra high net worth families, family offices, and institutions, with a disciplined, research-led approach. I dedicate hours daily to studying geopolitics, macroeconomics, interest rate cycles, and cross-asset markets, and I publish these essays to share frameworks, not hype. This matters because property pricing, rental demand, and timing are increasingly linked to global liquidity, policy shifts, and investor risk appetite, not just local headlines.
What you gain by working with me is clarity and control across the full decision chain:
Portfolio level thinking to position Singapore real estate alongside equities and other assets, balancing volatility while targeting capital appreciation and dividend-like rental income.
Market timing and risk management informed by macro signals, not emotions.
Strong execution and negotiation, supported by deep knowledge of Singapore land law, business law, and statutory considerations.
Professional, compliant documentation and clear guidance, especially important for cross-border buyers, family offices, and immigration or education related needs.
If you value a real estate advisor who thinks like an investor, operates with military-grade discipline, and executes with legal rigor, let us connect. Share your objectives, timeline, risk profile, and preferred districts, and I will propose a clear, data-backed plan for buying, selling, renting, or investing in Singapore.
References (APA style)
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. (American Economic Association)
Apple. (2025, March 5). Apple unveils new Mac Studio, the most powerful Mac ever, featuring M4 Max and new M3 Ultra. (Apple)
Boyens, J., et al. (2024). Cybersecurity supply chain risk management practices for systems and organizations (NIST SP 800-161 Rev. 1). National Institute of Standards and Technology. (NIST Computer Security Resource Center)
Hardt, D. (2012). The OAuth 2.0 authorization framework (RFC 6749). Internet Engineering Task Force. (IETF Datatracker)
International Joint Conferences on Artificial Intelligence. (2024). Large language model based multi-agents: A survey of progress and challenges. (IJCAI)
Jones, M., Bradley, J., & Sakimura, N. (2015). JSON Web Token (JWT) (RFC 7519). Internet Engineering Task Force. (IETF Datatracker)
National Institute of Standards and Technology. (2020). Zero trust architecture (NIST SP 800-207). (NIST Computer Security Resource Center)
National Institute of Standards and Technology. (2022). Secure software development framework (SSDF) version 1.1 (NIST SP 800-218). (NIST Computer Security Resource Center)
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). (NIST)
National Institute of Standards and Technology. (2025a). Digital identity guidelines (NIST SP 800-63-4). (NIST Pages)
NVIDIA. (n.d.). NVIDIA DGX Spark. (NVIDIA)
OECD. (2024a). The impact of artificial intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges. (OECD)
OWASP. (2024/2025). OWASP Top 10 for large language model applications. (OWASP)
Sakimura, N., Bradley, J., Jones, M., de Medeiros, B., & Mortimore, C. (n.d.). OpenID Connect Core 1.0. OpenID Foundation. (OpenID Foundation)
Schick, T., Dwivedi Yu, J., Dessรฌ, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. (arXiv)
Stripe. (2025a). Introducing our agentic commerce solutions. (Stripe)
Stripe. (2025b). Developing an open standard for agentic commerce. (Stripe)
Stripe. (2025c). Introducing the agentic commerce suite. (Stripe)
Visa. (2025, October 14). Visa introduces Trusted Agent Protocol: An ecosystem-led framework for AI commerce. (investor.visa.com)
Visa Developer Platform. (2025). Trusted Agent Protocol. (developer.visa.com)
W3C. (2019). W3C and FIDO Alliance finalize web standard for secure, passwordless logins (WebAuthn). (W3C)
W3C. (2025). Verifiable credentials data model v2.0. (W3C)
Wang, L., et al. (2023). A survey on large language model based autonomous agents. (arXiv)
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. (arXiv)
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