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Agentic Utilities Always On, Never Prompted CITRINI MAR 25, 2026 ∙ PAID Here at Citrini Research, we’ve been talking about “Agents” for as long as we’ve been writing about AI. Back in May 2023, in our Artificial Intelligence: Global Equity Beneficiaries piece, we predicted that AI adoption (and its investing implications) would follow a three-phase process: Introduction Phase I: Global Data Center Hyperscaling (“infrastructure”) Phase II: The Democratization of AI/ML (“commodification”) Phase III: Integration & Specialization (you’re here now) Here’s how we described the vision at the time: “The third phase is marked by the deeper integration of AI and ML into various industries and aspects of society, as well as further specialization within the field of AI itself.

The technologies described as “artificial intelligence” may not resemble the LLMs we currently use, and one or more services will provide “agents” capable of carrying out tasks without being prompted directly (an “AI ” This roadmap has proven prescient, though perhaps a better name for Phase III would be the “Agentic Era”. We start today’s piece with this callback for a reason (not just to pat ourselves on the back). The reason is that we, like many others, have envisioned the coming of the agents pretty much straight out of the gate; AI was never going to be constrained to a chat bot.

In our follow-up piece, Less Deus More Machina, we zeroed in on “AI Losers” – companies and industries most vulnerable to disruption with the proliferation of AI (and, importantly, agentic AI). Our “standalone short” basket of 26 losers worked fairly well against a rising market for the following two years, albeit with a good deal of choppiness along the way. But, the waterfall really began late last year – just as the “agents” arrived. We’ve reached an inflection. AI Agents have gone from an abstract concept to an actual service with expansive real world capabilities that can be directed from a simple iMessage.

For the “losers”, the theoretical risks that were enough to stoke fear for investors have rapidly become tangible risks that fundamentally alter management outlooks. The concept of an “AI Agent” went from buzzword to reality in November 2025 – reaching escape velocity with the release of ClawdBot (now OpenClaw, thanks to Anthropic’s lawyers). Until recently, the coding tools suffered the same flaw as every other hacked-together “AI” SaaS app: the call and response model. Due to degrading context windows, models couldn’t store history of past events so there was only so much that a single run could do.

You were still limited by the command line, but not anymore. In January, OpenClaw – an open-sourced, self-hosted agentic library – became the poster child for agentic technology. As shown by GitHub’s “Star History”, the adoption has been astronomical in just two jpeg) Along with exploding Github references, OpenRouter data shows just how dramatically OpenClaw has accelerated token usage within its product jpeg) OpenClaw went viral because it had the bright idea of storing context in a big text file and looping through new AI calls. Early users were running their agents overnight, and a new ecosystem of skills and plugins boosted its network effects.

And, for just the cost of a Mac Mini, you could run it all via iMessage. We must reiterate that this is the core reason behind the ClawdBot/OpenClaw Mac Mini craze: it has nothing to do with the hardware. Most of these functions are run in the cloud rather than locally, although longtime readers know our thoughts on where that’s headed. In this new paradigm, Jensen Huang is floating the idea of providing “token based compensation” to attract engineering talent. Jensen also recently commented that he would be ‘deeply alarmed’ if one of his engineers earning $500,000 a year did not consume at least $250,000 in tokens.

While we’re cognizant of the fact that this is like a baker saying you should be eating a dozen donuts a day, there’s a lot of truth in it as well. Homebrew Agents are still in “move fast and break things” mode. Agents are vibecoding, answering emails, dumping crypto wallets, and folding proteins. And companies have taken notice… This tells us two things: First, “Agentic AI” is now widely acknowledged as a critical market narrative. Second, picking winners and losers in this space will be a whole lot harder than simply seeing who is talking about it on earnings calls.

Nevertheless, we expect this to be a powerful narrative for true “agentic winners” – particularly if it reshapes the investment case for companies in the dumps. As always, we love a great bottom. We have been slowly adding to our allocation names that are aligned with the “Agentic Utility” layer - we are currently long AKAM, FSLY, CRCL and NET in the Citrindex. We’ve bought all of these when they were widely viewed as being entirely separate from the AI trade. Now, we think the moves in these names speak to how quickly stories and sentiment can reverse when the market recognizes an AI angle.

To understand where other agentic winners may lie, let’s first establish a framework for where to look for any novel beneficiaries of the new era of supercharged token consumption. Let’s start with a metaphor. In the chatbot era of AI, ChatGPT could help you plan dinner. It could help you pick out a menu, find recipes, and create a shopping list. It might even serve you ads that help you buy the ingredients. All that may be super helpful, but at the end of the day, it couldn’t actually make you dinner. Ultimately, you must use your own agency to drive to the store, slice the onions, and plate the chicken parm.

But imagine a world in which it can. Imagine if you had a service at your fingertips that could hop in a car, buy your groceries, pick up your laundry, and stop at the bank on the way home. It wouldn’t just stop at one grocery store, it would send ten cars out to every single grocery store in town to make sure you’re getting your Cheerios at the lowest price. It’s not free (it still chews tokens – and the “juice” of price Framework: Agentic Utilities comparison must be worth the compute), but as far as services go, it’s pretty darn useful – and in fact, you might use it a lot.

But what does this world actually look like? First, the number of actual cars on the road explodes. Traffic patterns are reshaped and traffic jams become a big problem. Second, the businesses that cater and optimize for these new task rabbits will see an influx in volume. Meanwhile, those that cater solely to human touch will lose market share. Finally, these swarms of agents become a risk to individuals and businesses alike. Sheer overload might resemble a stampede even if intentions are good – even worse, they are just as easily directed towards nefarious ends.

For the immediate future, this metaphor applies mostly to the digital realm. The physical embodiment of AI via autonomous driving and robotics may be closer than we think, but we’ll hold off on this for now. The entire digital landscape of infrastructure, commercial interface, and security must adapt to a new paradigm. We classify the winners into three categories. 1) Infrastructure: The Agentic Internet has a different structure than the one we are currently used to, both in terms of pathing and bandwidth. Digital plumbing needs to accommodate a boom in agentic traffic. This has unearthed legacy names that are already seeing inflections in both revenue and guidance that prove the demand has already arrived, and is rapidly growing.

2) Ecosystem: Simultaneously, a new customer category has emerged. Instead of selling to consumers (B2C) or businesses (B2B), companies will offer Agent (B2A) services. Agentic Utilities are the first vendors in the B2A vertical – these are companies offering infrastructure and services to be utilized by agents that can’t be “vibecoded” away – for regulatory, operational, or path-dependent reasons. Agents need ways to interact with the real world, and that includes payment rails. Stablecoins and agentic wallets are being quickly adopted, with non-humans making financial transactions using both crypto and the SWIFT system. Even robots can’t escape the dollar’s reserve currency status.

The evolution of the ecosystem will wind through the buildout of agentic plumbing (we’re seeing that now with the moves in the CDNs) and then begin pricing in their implementation. Value will accrue first to the picks and shovels, and then to the APIs in demand, the agentic harnesses e. where the attention is) and the agents themselves. 3) Governance: And, the Agentic Internet needs new protections. Current AI capabilities make it possible for a rogue agent to break the internal systems at Meta, and that is the dumbest that agent will ever be. Every single company is asking “where is the killswitch, and how do we know when to use

” This puts observability and counter-AI solutions in play. These names were thrown out with the bathwater when the entire software industry developed a new risk premium in under a quarter. The companies that address these three problems today are laying the groundwork for the agentic future – we call them Agentic Utilities. We’ve escaped the command line interface and it’s going to change the structure of the internet. Cisco (CSCO US) estimates Agentic AI generates up to 25x more network traffic than a chatbot. That figure compounds as more robust agents run in an “almost always on” fashion.

Or in the words of A10 Networks (ATEN US), “AI is a traffic problem before it’s a compute problem”. 1) Infrastructure Slide from ATEN Deck Training and inference determine what a model knows. Traffic determines whether it’s useful at scale. Once AI starts doing real work, every answer becomes a routing problem. This traffic growth is not evenly distributed. Demand is shifting from centralized hyperscaler training complexes towards edge compute for real-time inference. As more agentic work happens behind the scenes without any user interaction, the pathing of internet traffic is fundamentally changing. The City of Austin shows exactly how this will work.

Traffic in Austin is terrible, and it’s baked into the structure of the city. There are two major “north-south” roads that run through town: Mopac and Interstate 35. The infrastructure failure is that there are no major “east-west” roads that go through the city, which turns the road system into a hellscape of flyways and on-ramps. This causes major congestion on both sides of the city as there’s no easy way to cross. The same bottlenecks will appear in our digital infrastructure and will demand new solutions. The traditional internet was designed with human-machine interaction in mind.

This is north-south traffic: you type in a URL, your browser sends a request to a server, and then the server responds with content. The agentic shift causes a different pathing of internet traffic. When AI agents are given a task, they then operate “under the surface” through API access, negotiating with other agents, while maintaining persistent sessions. Agentic traffic will have more “east-west” movement, where 80% of traffic will move laterally between servers, GPUs, and data centers. As inference scales across Orthogonal OSI (Open Systems Interconnection) multiple data centers, the demand for “inter-server” traffic and corporate networks will multiply.

“The global agentic-AI M2M [machine to machine] traffic is expected to increase from 66 EB/month in 2025 to 537 EB/month in 2034” - Nokia Global Traffic Report The types of packets aren’t going to be the same. Human-machine interactions tend to be short sessions that open and close. Agentic traffic demands a different kind of internet: always-on connections, higher bitrates, and continuous access to inference clusters. Cisco Slide Highlighting Change in Traffic Patterns Post-Agents The demand for east-west traffic is creating a structural change to the internet’s plumbing, and a structural demand shift for managing that bandwidth.

This shift should continue to benefit the physical players that make up the backbone of the internet. Cisco is involved in multiple layers of the OSI model, with leadership focused on the emergent “always on” model of the agentic internet. They are responding to a fresh cycle of enterprise networking upgrades just as investor enthusiasm is waning on whether AI networking tailwinds have peaked. Cisco has developed both the SiliconOne G300 – a 102.4 Tbps switch and an AgenticOps platform. They’re leveraging their acquisition of Splunk with a new observability offering while building a security wrapper for the new agentic attack surface.

Ciena (CIEN US) is another standout in this area, as it is the optical backbone of the east-west traffic expansion. All of those API calls add up. When you include the demand for persistent memory and stateful interactions between servers, then you’ve got a new source of demand for optical networking that’s outside of the lumpy orders from hyperscalers. Ciena recently increased capex by about 2.5x the historical average to build out capacity; the company explicitly states that it’s meeting demand rather than anticipating it. There’s also an agentic platform kicker – the company’s Blue Planet division has built out an Agentic AI framework for network operations, which has already been adopted by Lumen as of October 2025.

Hewlett Packard Enterprise (HPE US) also has clear inroads into the AI network infrastructure trade through its recent acquisition of Juniper Networks. HPE Earnings Slide Deck HPE’s networking segment grew 152% YoY, boosted by the Juniper acquisition, and is now sitting at 30% of total revenues and half of operating profits. That’s not even touching their existing “on-prem” business line. HPE Private Cloud AI offers a full stack subscription (down to the chips) so that clients can run inference without exposing proprietary data to any public networks. To extend the Austin metaphor (sorry), you can build all the east-west highways you want…but they won’t accomplish their goal if nobody driving on them can read the street signs or look up an address.

Before a single packet of agentic traffic traverses CIEN’s optical backbone or reaches an inference cloud the agent needs to resolve a domain name and the connection needs a routable IP address. This brings us to an interesting angle – two distinct chokepoints sitting on legacy internet infrastructure names. A10 Networks (ATEN US) started out providing Application Delivery Controllers (ADCs), balancing loads between servers and users. These are rapidly being The Street Signs and the Roadmap displaced by virtualization and programmable silicon. Their upside is embedded in the carrier grade network management infrastructure that sits directly in the path of key agentic demand: unique sessions for agents and on-device inference.

Every agent that wants to maintain a persistent connection with another server, call an API or coordinate with a physical device in the real world requires an addressable endpoint. The problem is there are only so many IP addresses to go around - roughly 4.3 billion. We actually ran out years ago. The workaround is NAT (Network Address Translation), which lets thousands of devices share a single public IP address by managing sessions behind the scenes. A10 handles the NAT table at the scale of entire telecom networks, and they’re simultaneously building out IPv6 transition infrastructure for the day (still years away) when the internet moves to a larger address space.

A10’s Thunder CGN helps manage the (now scarce) IPv4 addresses while building out the scale for IPv6. The company is focused on growing the cybersecurity wrapper around their hardware and projects 65% of revenue will come from this domain. While A10 manages scarce IPv4 addresses and builds out IPv6 scale for the explosion of agentic endpoints, VeriSign (VRSN US) is the other side of that same coin. If A10 manages the street signs, VRSN manages the roadmap. VeriSign operates the authoritative registry com net and processes 450 billion DNS queries a day. Every web request an agent makes starts with DNS resolution.

CEO James Bidzos has explicitly flagged agentic AI as a driver of DNS utilization on the last two earnings calls, and the company is teasing new security services for the first time in a decade. The traditional internet required one DNS lookup per page load. Agents becoming more commonplace also means their workflows fan out across ten APIs, generating ten lookups before it’s even started reasoning. Multiply that by the always-on, persistent session model described throughout this piece and you can see the bull case for VRSN – a structural increase in DNS query volume compounding with every new agent deployed.

VeriSign has positioned domain names as globally unique, stable, human-readable identifiers for verifying digital content, which they argue is critical for agentic AI and valuable for combating misinformation and deepfakes. VRSN is an amazing business model – 67% operating margins with virtually zero customer acquisition costs. Now, imagine if the business gets priced for growth again (it’s not that hard, VRSN went from about six dollars to $240 during the dot com bubble). Agentic AI priorities are shifting from raw compute to speed. Agents don’t have the patience of a human waiting for a web page to load – workflows require quick answers to maintain a constant reasoning loop, whether that loop is to review code or ensure two robots don’t collide.

What about content that hasn’t yet been generated? What if you need a conversational voice interface? What if you need to coordinate a fleet of robots? You move compute closer to the endpoint. The AI infrastructure narrative has been primarily focused on centralized compute hubs, where hyperscalers are building massive data centers close to cheap energy Location, Location, Location inputs. The other extreme and somewhat speculative end of the spectrum is inference-on-device, where AI models can run directly on local devices. (As we’ve long argued, we do see an eventuality in which meaningful compute can be held in your own hands.

Developing technologies like that of Taalas (private) is taking model weights and placing them in the silicon alongside on-chip memory, and could accelerate this future.) But between “big ass data center” and “pocket AGI” lies a middle ground of pre- existing infrastructure that has been retooled for the agentic revolution. And, with the AI narrative focused on the ends of the barbell, these names haven’t yet seen a large markup in response to agentic traffic demands. When the World Wide Web hit the mainstream in the mid 90’s, it revealed the flaw in a centralized internet.

If a website’s images were hosted on a single server 2000 miles away, then the end user would have to wait entire seconds for the page to load. There was the “hug of death” if your site went viral. Thousands of users would flood a website from a Slashdot link, causing the server to overload, then crash. It only got worse – when the Ken Starr Report was released in 1998, major media and government websites went down for the rest of the business day. Content delivery networks (CDNs) were created to help decentralize the internet.

Website owners could “cache” images on servers close to the end-user’s location, allowing faster load times and steadier connectivity. Each step in the internet’s capabilities led to a new investment in CDN infrastructure. DDoS and cyberwarfare put CDNs on the physical frontline, originally with physical firewalls. Video streaming prompted increased bandwidth and capacity of CDN networks to meet booming consumer demand. CDNs: “Compute” Delivery Networks Now, with the exponential growth of Agentic traffic, CDNs are having another follow-on cycle of demand because of pre-existing infrastructure that sits in close proximity to their end users. Each of these waves eventually enters a commoditization cycle, and share prices still reflect that recent carnage.

Pricing wars turned the space into a graveyard: StackPath and Lumen exited the business in 2023-24 while Edgio filed for bankruptcy protection in September 2024. The surviving firms with location-advantaged infrastructure are becoming increasingly valuable providers of low-latency AI compute. The coming surge of agentic traffic should further solidify this market, as “CDN” transforms from Content Delivery Networks to Compute Delivery Networks. Akamai (AKAM US) stands to benefit from a double pivot – repurposing its content delivery footprint for AI compute, while also rapidly expanding its cybersecurity business. The cyber side is already seeing tailwinds from AI security as both agents and applications are hitting APIs hard.

The demand for API security has been explicitly highlighted by AKAM management as a large tailwind, with revenue for this segment growing 100% YoY. The company is now leveraging their existing CDN footprint to build out AI services. In October 2025, Akamai launched the Akamai Inference Cloud, bolstered by $250 million in capex tied to their cloud buildout. Investors panicked following the company’s earnings release, although concerns were largely tied to opex discipline, rather than any forecasted drop in demand. A similar turnaround is rapidly unfolding at Fastly (FSLY US). The company was left for dead following its run in 2021, which we feel gives it the highest convexity among CDN plays.

Agentic traffic helped push the company into positive free cash flow territory, with GAAP losses being driven heavily by SBC dilution. In February 2026, the company rolled out an AI Accelerator, where they provide semantic caching that decreases both latency and inference costs. This offering complements their Compute platform, using WebAssembly (WASM) as the foundation. This choice signals that they are attempting to live up to their brand name: optimizing for speed surrounding high-density networks instead of trying to maximize overall network coverage. API Gateways This CDN build-out creates a subtle problem in that getting traffic to the edge faster is only useful if what’s waiting at the other end can handle it.

Every API call an agent makes passes through a load balancer before it ever reaches a backend application. F5 Inc (FFIV US) owns NGINX, the most widely deployed reverse proxy on the internet. Their NGINX One platform unifies all NGINX deployments into a single control plane, and management has been explicit in their repositioning of it as the API orchestration layer for agentic traffic. On their Q1 FY2026 earnings call, the CFO noted that AI-generated API call volumes are running “meaningfully ahead” of prior projections, with non-human traffic now the faster-growing segment of throughput metrics.

In a world where a single agentic workflow can generate hundreds of API calls (while a human interaction generates just one), the bear case of FFIV as a legacy load balancer with a commoditized core appliance business is less appealing. More agents mean more calls, more rate limiting, session management, and security enforcement at the gateway. Data Center REITs & Cloud Providers While perhaps not the sexiest category, we also think that data center REITs should continue to benefit from the tailwinds of both the shape and latency needs of agentic AI. Equinix (EQIX US) owns the buildings that hold the data centers, with about 70% of revenue attributed to this business line.

The bull case is about as first-order as you can get: more data centers leads to more colocation demand, which drives more sales and higher rents. Company Positioning Diagram From Equinix The convexity comes from its Interconnect revenue. This is a recurring service that Equinix offers to connect servers – effectively building extra lanes on the east-west highway. They offer physical connects as well as a virtual interconnect layer called Equinix Fabric, which allows “interconnect on ” The company recently reported 500,000 interconnects and 60% of their large new contracts are from AI workloads. Digital Realty Trust (DLR US) is the other long-standing DC REIT with similar positioning to EQIX.

DLR is rapidly closing the gap in high-margin connectivity via their ServiceFabric offering, a software-defined orchestration layer built to rival Equinix’s Fabric product. Overseas, we highlight VNET Group (VNET US) which operates as a carrier- and cloud-neutral internet data center services provider in China. We see China leading in real-world agentic adoption but lagging the US on compute buildout. VNET has shown significant business reacceleration in the past two years that has few signs of slowing. On the splashier side, the market has been rapidly re-rating DigitalOcean (DOCN US). Where hyperscale clouds win business on up-time, price, and peace-of-mind, DigitalOcean acts as more of a high-touch partner rather than a DIY vendor.

While this may lead to slightly-higher unit costs of compute, the value-add services that DigitalOcean provides for small- and medium-sized enterprises and startups tend to drive cost savings in other areas of the budget, say in compliance, security, and even financial planning. Larger clouds like AWS and Azure claim to offer similar support, but good luck getting world-class service if your company doesn’t have an eight-figure-plus bill at the end of each month. Managed-service providers like DigitalOcean exist in many industries to lower barriers to entry and decrease friction for emerging businesses. With the velocity of ideas throughout the economy reaching a fever pitch, DigitalOcean provides a platform that greases the wheels between idea and implementation.

This shift towards digital native enterprises (DNEs), a fancy term for AI/agentic startups, has resulted in a dramatic re-acceleration in the topline, and surprisingly compelling cash flow vs. other neocloud platforms. Another interesting, if more risky, play is Rackspace (RXT US). Until recently, it appeared to be following the Wolfspeed (WOLF US) playbook: a debt-laden company brought public by Apollo with the equity heading towards a full writedown. The company managed a stick save in February, inking a partnership with Palantir, where Rackspace would provide hosting and systems integration services (the latter of which being a new revenue category).

Of course, there’s still a risk that their core hosting business continues to shrink, even with their new agentic offerings. The stock offers high convexity to agentic demand, though the leverage is still there with $2.75 billion in long term debt. Cloudflare (NET US) is evolving into the most comprehensive play on agentic demand. While they have the structure of a traditional CDN, they use that footprint to sell services on top of their infrastructure. Those services fit agents like a glove. They released Workers in 2017 that allow users to run operations on Cloudflare’s distributed network rather than on a single server in Northern Virginia.

Five years later they added Durable Objects, which provided a “state” to their Workers. When agentic demand hit, the scaffolding was already there. The Agentic Conglomerate: Cloudflare Cloudflare is also a first mover in agentic ecommerce. They created NET dollar, a stablecoin for agentic transactions. They co-founded the x402 Foundation with Coinbase, and are working with major payment companies to develop the authentication layer for agentic ecommerce. Finally, the company also is a critical governance player, providing a unified security layer that governs how autonomous AI agents interact with corporate data and external APIs. By integrating tools like Firewall for AI and AI Audit, Cloudflare enables organizations to enforce least-privilege access and real-time guardrails, ensuring that agentic workflows remain secure and compliant without stifling their autonomy.

But you shouldn’t imagine agents living in clean APIs and structured tool environments. Real world enterprises run on terrible web UIs, fragile workflows, vendor portals with session timeouts & systems that only work because a human is squinting at a screen clicking through four confirmation dialogs. So, agents will need browser infrastructure the same way knowledge workers need laptops. Cloudflare is already pushing Browser Rendering, signed agent traffic, remote MCP plumbing, and something they call “Markdown for Agents,” which is basically the web learning to speak machine-first. Zscaler (ZS US) just acquired SquareX to secure browsers for the AI era.

Of these, Cloudflare is doing the most interesting work. The “bear case” is that NET’s strong positioning in the agentic landscape is well known and already baked into today’s valuation, unlike some of the “turnaround” stories mentioned prior. Nevertheless, we think that owning high quality, highly exposed names is worth the price of admission. If infrastructure provides agents with the digital foundation to perform human-like workflows, then the ecosystem brings together the tools that agents will use to work with humans. After all, the objective of this innovation is to harness agentic capabilities to improve real-world outcomes and solve for human inefficiency.

SaaS businesses are not blind to the existential risks facing their business models. To contend against this looming threat, every software company under the sun is coming to market with their own agentic offering. Of course, some (if not most) of these products are too little, too late. On the other hand, the “moat” in software is often unrelated to the sophistication of the technology itself. Software companies with valuable data silos, physical infrastructure, and regulatory advantages are likely to succeed in an agentic world. The companies that function as a toll on agent-to-human interaction are harder to “vibecode” away.

After all, every human-agent interaction must begin somewhere: A human prompts an agent. An agent then accesses an API, pays for something it will use, dispatches a phone call, email blast, or iMessage to the end user or any number or permutation of tasks. 2) Ecosystem Payments We spoke about the reasoning behind stablecoins as an agentic payment rail in our “2028 Global Intelligence Crisis” piece. We find agentic payment rails and agentic commerce very interesting, because they mean that payment rails can actually compete. When consumers get a card from the bank, they don’t care whether it’s Mastercard or Visa, they just use it.

This can change significantly with AI agents making switching between rails more dynamic. After all, we still live in a capitalist society – agents are much less useful if they’re unable to transact on our behalf. Pulling out your credit card every time you make a purchase online feels like a prehistoric practice – there must be some sort of improvement. These agentic tailwinds come at a moment when stablecoin adoption has already seen dramatic growth as regulatory clarity has allowed for institutional buy-in. Early agentic adopters have discovered that it’s much easier to provision an agent with a crypto wallet than with a credit card.

That has opened up an entire sector-wide effort to create agentic payment workflows. According to Visa’s onchain analytics dashboard, we see USDC transaction volume excluding crypto exchanges surging post the passing of the GENIUS Act in July 2025. We rarely comment on crypto, with the exception of our well-known bullishness on Hyperliquid as they integrate 24/7 markets for commodities, FX, and stocks. However, it’s important to understand what’s being built to enable agentic commerce if it’s going to impact stocks. x402 If you’ve ever seen the error “404 not found”, then you have experience with an HTTP status code.

There’s a lot more from this category, including “301 - redirect”, “500 - internal service error”, and “403 - forbidden”. While all different, they’re all incredibly frustrating to see on your computer screen. Some of the response codes have been reserved, but are not commonly found. One instance of note is “402 - payment required”, a response that was once reserved for a future use case. That use case is now here. The similarities between HTTP and agentic payment protocols are significant. We feel that understanding agentic payment protocols is important to one day understand the winners of it, but it’s equally important to understand that value won’t accrue to

the protocol layer that enables agents (rather, they will accrue to APIs in demand, agentic harnesses and the agents themselves). So, with that being said, let’s look at one of the protocols - x402. The “x402 open standard” is a crypto-native payments standard that uses the existing hypertext protocol for agentic transactions. x402 was jointly developed by Coinbase (COIN US) and is intended to serve as the payments protocol for AI agents, Cloudflare (NET US) has also joined the foundation responsible for its development and implementation. It jpeg) As shown below, the daily x402 package downloads have coincided with the rise in the open source agentic revolution - although they remain pretty small relative to AI libraries.

This architecture comes into play amidst the ongoing battle between AI web scrapers and digital content creators. If you have a website that is used in AI prompts, you end up with no monetizable traffic as your product is synthesized into LLM outputs. Bot detection is a constant moving target, so instead of playing whack-a-mole on these agents, why not charge the AI for content access? In July 2025, Cloudflare developed “pay per crawl,” allowing content owners to charge a fee for an AI to access its content. A few months later, Cloudflare announced NET Dollar, a USD-backed stablecoin built for agentic commerce.

It’s clear that NET is positioning itself across agentic infrastructure to be a clear cut winner. Circle Internet Group (CRCL US) has released Circle Nanopayments, which “enables gas-free USDC transfers as small as $0.000001”, positioning the x402 protocol to serve as the payment rail for agentic transactions, abetted by the proliferation of stablecoin acceptance and infrastructure.]agent-to-agent transactions need a reliable, low cost, trusted medium of exchange. And so virtually, all of the AI payments infrastructure that we’re seeing, the agent-to-agent type activity is happening with blockchains, it is happening with USDC. “ - Circle Internet Group Q4 2025 Earnings Call

While the stock has been relegated to the doldrums of Value Investor Club threads, PayPal (PYPL US) has also issued a USD-backed stablecoin – PYUSD, whose circulation grew significantly in 2H25, reaching over $4 billion. While still far, far behind USDC and USDT (Tether) in terms of market adoption, we view PYPL as a longer-shot stablecoin play given its single-digit earnings multiple. To get more in depth on why those micro transactions are necessary, let’s look at another protocol being developed for agentic payments: Backed by Stripe, Paradigm and Visa, Tempo is attempting to solve how agents manage their balance sheet continuously in order to complete a task using an L1 blockchain explicitly designed for agentic stablecoin transactions.

At the highest level of abstraction, you can think of MPP as a way to make payments a first class citizen in the HTTP request response cycle. MPP is designed to be an IETF approved standard that works with any payment method. Leveraging alliances with incumbents like Visa and Lightspark, MPP facilitates settlement across both legacy credit card infrastructure and crypto as a “rail-agnostic” primitive for the agentic economy. In a traditional workflow, payments are discrete and user-initiated. In an agentic workflow, they are continuous, conditional, and embedded inside decision trees. Tempo abstracts this into a programmable “payment clock”, allowing agents to stream value, escrow funds, or dynamically re-route spend based on real-time outcomes.

The Machine Payments Protocol extends this further by standardizing how agents negotiate price, authorize spend, and settle transactions across counterparties without human intervention. What does this mean? In layman’s terms, you tell an agent you want to spend no more than $5 on a task - it goes out and draws from that $5. Essentially, Tempo allows a cryptographically signed message to authorize an amount held in non-custodial escrow, which an agent can incrementally debit. The reasoning behind this is that agents can potentially require thousands of micropayments per task. The human pays once, the agent pays thousands of times (without latency or waiting for block confirmations).

You can see Tempo & MPP how this would be impractical with a credit card, where transaction costs would almost immediately exceed the actual value of transactions. Whereas right now you would go to a restaurant and order a meal that you’d pay $50 for, Tempo allows an agent to essentially pay $1 per bite and never go above $50. This opens up a whole new market - for example, if CitriniResearch wished to allow people querying an agent to do DeepResearch on the investment implications of agentic payments, that agent could theoretically do an API call and pay a fraction of the monthly cost just to access this specific segment of this specific article.

This is a topic deserving of its own primer, probably written by someone more crypto-competent than we are, but there will be a plethora of problems and solutions proposed inherent to how machines will pay each other. For example, what happens when a wallet has stablecoins but runs out of the native blockchain’s token and doesn’t have enough gas for the transaction? For the uninitiated, in order to send USDC on a blockchain such as Ethereum, you also need a nominal amount of ETH to pay for the transaction. That can interrupt a workflow and result in needing human involvement.

Those Circle nanopayments mentioned earlier do not work on Ethereum or Solana chains, because the chains themselves charge for gas. One example of a solution is Stable, a blockchain built for stablecoin payments. Stable’s single-token architecture directly addresses this by using USDT, the world’s largest stablecoin, for both gas and settlement. In this model, an agent’s cost accounting becomes fully deterministic in dollar terms with no gas conversion spread, no exposure to native token volatility, and no need to maintain a separate balance solely to execute transactions. Notably, this is just one pillar of “agentic commerce”.

Again, the tech is still in its skeuomorphic phase – we expect agents to transact like a human might. Looking ahead, don’t be shocked when there are agent-to-agent (A2A) marketplaces where prices are dynamically quoted with little human input. It’s worth a caveat that this is all extremely early. It seems trivial to predict that agents will have to pay for things, and almost assuredly correct, but there’s a long way to go both in terms of tech and adoption. It will be top of mind for us to keep track of how some of the bigger players in payments (like V, MA, GPN, FIS, FISV, SHOP etc.)

integrate and interact with agentic payments. All of these companies have at least mentioned agentic commerce. Fiserv, in January, announced their platform meant to let banks identify and authorize AI-initiated transactions while adding fraud protection and preserving top-of-wallet positioning – attempting to position themselves as a picks-and-shovels play for banks that do not wish to be disintermediated when software starts shopping. Still, while important to understand, we feel it’s far too early to begin investing in payments solely on agentic upside before these companies truly demonstrate their prowess and ability to adapt. According to the Communication Workers of America (CWA), 3.6 million people are employed by call centers, an estimated 2.5% share of the US workforce.

At $15/hour, we estimate that the US spends more than $100 billion annually on call center labor. The magnitude of this expense has lured many software businesses into the call center as a service, or CCaaS marketplace. However, these businesses are not immune to the headwinds facing the software ecosystem. An agentic competitor or a savvy voice AI startup can seemingly disrupt an incredible product in the blink of an eye. An agentic voice agent, however, cannot effectively replicate telephone infrastructure on its own. You need public switched telephone network (PSTN) interconnects, carrier licenses, local number portability, and actual physical infrastructure in order to get a phone number.

That is the business that Bandwidth (BAND US) operates. The world’s largest technology companies – Microsoft, Google, Amazon’s AWS – all pay Bandwidth for access to their telephone infrastructure, signaling that it’s easier to pay this toll to Bandwidth than building out your own telephone infrastructure. Last Mile Telephony Bandwidth Investor Presentation – February 2026 In September 2025, Bandwidth announced full integration into OpenAI’s Realtime API, allowing enterprises to “bring their own AI” and quickly spin up conversational voice agents using Bandwidth’s edge infrastructure. “Enterprises can now deploy AI voice agents powered by the technology behind ChatGPT, with the reliability and global scale of our Communications Cloud.

By offering many different options to integrate conversational AI, Bandwidth is becoming the AI orchestration leader for the global ” - John Bell, Bandwidth Chief Product Officer Agentic communication, of course, will not be limited to phone calls. Knowing the communication patterns of Gen Z consumers – text will be the predominant interface of agent-to-human interaction. This is already evident from the popularity of LLM chatbots. However, without proper context, agents fail to address the acute needs of the customer while risking churn and missed opportunities in re-selling, upselling, or retaining customers. Twilio (TWLO US) rounds out the communication layer for agents with Conversational Intelligence.

While Bandwidth largely tackles the customer service “voice” angle of AI communications, Twilio’s edge hails from fifteen years of customer data embedded within their Segment product. This context-rich corpus of data empowers Twilio’s customers to harness agentic AI – across voice AI and text – to understand their customers and achieve optimal sales outcomes. Ironically, Twilio’s Segment acquisition was lambasted by the street as a huge overpay in the heat of 2020-21 valuations. Activists lobbied for Twilio to spin out the Segment division in 2023-24 as it dragged on profit margins and detracted from the company’s core business model.

Founder and CEO Jeff Lawson ultimately stepped down amidst this activist quarrel, although the Segment division was never sold. This acquisition, while expensive at the time, may prove to be prescient. Twilio Conversational Intelligence In May 2025, Twilio reached a pivotal milestone with the general availability of ConversationRelay, a product designed for developers to build human-like AI voice agents. ConversationRelay can be viewed as an “out-of-the-box” offering for agentic voice AI – bundling the fragmented functions of speech-to-text, analytics, and audio – all into one product. Both Bandwidth and Twilio’s products are HIPAA compliant, meaning that medical records can be transmitted using programmable SMS and Voice.

This too, is an enormous regulatory advantage as healthcare customers are extremely sensitive about sharing patient information. LLMs remain somewhat unreliable from a cybersecurity standpoint, but we’ll touch upon this later. Of course, the risk of frontier models emulating telephony solutions still remains. However, a major advantage of both Bandwidth and Twilio is at the orchestration layer: These companies have long-standing relationships with carriers that manage interconnects and regulatory compliance. There are deep integrations with phone systems and existing enterprise partners wherein these features can be quickly accretive to the company.]we are moving beyond being a provider of communications channels and data toward becoming a foundational infrastructure layer in the age of AI.

Revenue from our voice channel continues to accelerate, aided in part by voice AI, which we believe is just the beginning, as these use cases will evolve to be more conversational and cross-channel, an area where Twilio is uniquely ” - Khozema Shipchandler, Twilio CEO Like Bandwidth, Twilio is fully integrated into OpenAI’s Realtime API. Twilio itself already handles 85% of its own inbound sales calls through AI agents, proving that this model can work. Last month, Twilio launched the Agent-to-Human (A2H) protocol, designed to complement MCP and Agent-to-Agent (A2A) architectures by standardizing how AI agents interface with humans across voice and SMS.

Again, we are not arguing that agentic voice startups can’t compete with legacy players. Instead, we see these companies employing moats stemming from the local number inventory necessary for agents to speak to humans. On top of that – in Twilio’s case – voice startups don’t have the customer context to make this a customer-friendly, market viable service. Contrary to popular opinion, we understand that software company “moats” often do not come from the sophistication of their technology, but rather from operational, regulatory, and compliance advantages. In this vein, we think DocuSign (DOCU US) stands as a valuable member of the Agentic Ecosystem, given data and regulatory advantages.

The company has embarked on a strategic shift from pure, seat-based E-Sign subscriptions to their Intelligent Agreement Management (IAM) platform. DocuSign Earnings Presentation – March 2026 The IAM platform was created to provide enterprises with AI tools that manage the entire contract lifecycle: Document creation, review, negotiation, signature, and analysis can be managed all through DocuSign’s IAM platform. Notably, this offering generated north of $350 million in annual recurring revenue for fiscal 2026, Signed, Sealed, Delivered comprising roughly 11% of ARR. The company expects this segment to nearly double in fiscal 2027, reaching a high-teens share of total ARR.

The IAM product has also garnered partnership interest from agentic vendors such as Anthropic and OpenAI, who have integrated IAM through the model context protocol (MCP) or, in Anthropic’s instance, the Claude Skills connector. “Last month, we partnered directly with Anthropic to make IAM available as part of Claude Cowork. The DocuSign MCP connector is available in beta today through Anthropic’s Connectors Directory. It enables DocuSign customers to use Cowork’s natural language prompts to automate agreement workflows and securely create, review, send and manage agreements in IAM, all with DocuSign’s trusted security and access controls.

In addition to Cowork, IAM also connects via MCP server to OpenAI’s ChatGPT, Google Gemini, GitHub Copilot Studio and Salesforce’s ” - DocuSign Q4 FY2026 Earnings Call DocuSign’s advantage stems not just from over 200 million signed contracts and agreements, but also from its regulatory moat. DocuSign is both FedRAMP and GovRAMP certified, earning them valuable contract awards from bodies such as the US Department of War. What’s more, agentic workflows on DocuSign’s platform are underpinned by the US ESIGN Act, the Uniform Electronic Transactions Act (UETA), and Europe’s eIDAS, which permit digital signatures through DocuSign’s platform.

All agents create data and expend resources, widening the surface area of the enterprise. As agentic adoption takes hold, however, the question in the boardroom shifts from “can they do ” to “should they be doing ” and “who is ” An unharnessed agent can be outrageously expensive – both in what it consumes and what it exposes. An agent running 24/7 could potentially vacuum up a month’s worth 3) Governance of AWS spend in a single week, creating unexpected costs and distractions. Similarly, a rogue prompt injection can direct an agent to leak data, poison records, and abuse integrated tools.

The ramifications of these attacks, if left unguarded, could cost an organization millions. Volume without guardrails is a catastrophe waiting to happen. That’s where Governance becomes the focus. What was historically a bifurcated market between Observability vendors (what is it doing?) and Security vendors (Is it allowed to do that?) has begun to converge into a single discipline – which we’ve coined Agentic Governance. The same telemetry that tells you an agent is underperforming is the same telemetry that tells you it’s been compromised. The identity function that authorizes agent access is the same function that will revoke access when behavior appears anomalous.

This convergence is the most important structural shift in the infrastructure software market today. Observability (collecting and analyzing logs, metrics, and traces) is more than just a monitoring and troubleshooting tool in the agentic world. As agents become more Observability: Through the Looking Glass autonomous and ubiquitous, observability becomes the financial plumbing of modern architectures and an operational harness for a fresh breed of business models. In traditional software, you might purchase licenses and pay a fixed fee. But in an era where AI agents spin up and down on demand, if you can’t measure precisely who’s using what and when, you’ll never get an accurate handle on costs or revenue.

Ironically, turning on more detailed observability often raises your bills: just like installing a high-precision meter can reveal usage you didn’t know existed. Platforms like Datadog (DDOG US), New Relic, or Prometheus can generate significant data volume, which directly translates to higher cloud bills. Yet these costs typically pale in comparison to what you can save (or earn) by catching inefficiencies early, billing customers for actual usage, or preventing unplanned downtime. The ROI is unlocked the moment you spot a costly flaw – or the moment you can precisely meter a usage-based service. Observability platforms will increasingly help product and development teams discover how models and agents are actually being used in the wild.

Despite advances made in AI development over the last several years, one still doesn’t know how a model and its users will behave until it is deployed. That is, of course, only if behaviors, usage, and outcomes are observed and analyzed. Historically, cybersecurity was built around a stable set of assumptions: humans use devices, devices connect to networks, and the sprawl of devices dictates the “perimeter” of the organization. This was known as “Castle and Moat” architecture – once users were inside the perimeter, they were granted a high degree of implicit trust. The surface area of the enterprise was large, but it was fairly intuitive to protect and monitor.

The first crack in this model came with the explosion of cloud workloads and remote workers. Employees logged on from home networks, coffee shops, and personal devices. Meanwhile, data migrated from on-prem servers to SaaS applications built Security in a Zero Trust World upon AWS, Azure, and Google Cloud. This shift ushered in a class of “next- generation” firewall companies built for the hybrid era. These businesses were able to survey traffic at the application layer, not just at the port level. This architecture was sufficient, although a fundamental loophole still remained: Once you were inside the firewall, you were trusted.

Zero Trust emerged as the successor architecture to the hybrid era. Rather than implicit trust based upon network access, Zero Trust operates under a “Never Trust, Always Verify” ethos. Every request from a human, device, application – must be continuously verified. The essence of Zero Trust boils down to: “Who are ” and “Are you authorized to access this ” This turns out to be structurally compatible for the Agentic Era. Every meaningful agent inside an enterprise will need something resembling an employee file: who created it, what systems it can access, what secrets it is authorized to hold, which tools it can invoke, how it gets suspended, and what its chain of action looked like after an incident.

We have been long-standing Okta (OKTA US) bears – since October 2023, we’ve maintained that long NET / short OKTA would be long term winning trade (and it has been). However, Okta is already extending identity controls to non-human identities. It is “cheap”, and could see some rerating off of this narrative – although long term upside will be an execution issue and the company has not executed all that well. We’re keeping an eye on it, but we’re not convinced yet. CyberArk (now PANW) has rolled out agent-specific privilege controls. Cisco is building AI BOM (bill of materials), MCP cataloging, and runtime protections around agentic tool use.

Agentic identity management will become increasingly important. A compromised agent doesn’t need to “break in”. Unlike a phishing attack – in which a cybercriminal spoofs an email or text message with the objective of gaining credentials – the agent is already within the bounds of the perimeter. All of the workflows undertaken by the agent are deemed to be normal unless someone, or something, flags it as anomalous. So who operates the “something”? The answer is taking shape through a combination of platformization and M&A, as incumbent cybersecurity businesses aim to reinforce their product suite with a comprehensive toolbox of security capabilities.

Cloud-security giant Palo Alto Networks (PANW US) stands to benefit from this structural shift. Management’s recent acquisition spree clearly outlines where they see threats clustering in the Agentic Era. Chronosphere, the more recent of the two pick-ups, expands the company’s reach into observability. The differentiation of the combined platform lies in handling high- cardinality data sets, framework-ready metrics, distributed tracing, and cost-aware telemetry pipelines that let customers filter and route data before observability spending gets out of hand. In an agentic environment, where workloads appear and disappear continuously, that is not just useful for debugging but a fundamental layer of cost control, revenue attribution, and automated response.

If Chronosphere helps Palo Alto observe what is happening, CyberArk helps determine who, or what, should be allowed to do it. The core competency of PANW’s new ~$25 billion toy is in privileged access management – or controlling and monitoring the highest-value credentials, accounts, and permissions inside an enterprise. That strikes us as a sensible, cohesive strategy. Rather than betting on which specific customer workflows win, Palo Alto is positioning itself to support the instrumentation, enforcement, and governance required for all of them. While CyberArk secures the “keys to the kingdom” for privileged accounts, broader identity security and governance across the entire enterprise falls to pure-play leaders like SailPoint (SAIL US).

SailPoint acts as the central nervous system for identity lifecycle management. It ensures that every identity (human or agent) is provisioned with the exact right level of access. Furthermore, it ensures they are automatically stripped of those permissions the moment they are no longer needed. Zscaler (ZS US) is another pioneer of the Zero Trust architecture, having blended secure web gateways, data loss protection (DLP), and cloud access security into a single, inline proxy that brokers every connection between user and application – a posture known as SASE, or secure access service edge. The company’s Zero Trust Exchange (ZTE) acts as a virtual security escort for agentic traffic – ensuring that an agent is authorized to access a particular resource, at a particular time, for a particular reason.

If an agent is making API calls to external services, apps, or circulating data between tools – Zscaler’s inline security tool is the real time enforcement layer of this sequence. It may be easy to read this and say, “that’ll be cool... in 2030”. But the Agentic Era is not a newly contrived sci-fi meme. This sort of agentic explosion has long been assumed to be the natural evolution of AI in action, and one that has more far- reaching and disruptive implications than the chatbot era. Now, it’s not hypothetical – it’s happening. At the same time, a number of yet-to-be-considered mines remain laid from threats to existing business models from artificial intelligence.

While it’s simple to throw together a basket filled with software names and say “well, these probably will be used by AI”, we’ve attempted to navigate that minefield by narrowing this down to names that have limited risk and a clear angle for upside to this trend. Conclusion Judging by the explosive uptake and usage of agentic platforms in just the past several months, we expect that implications will come faster than most expect – the train has left the station. Merely putting “agentic” on a slide deck won’t mean much, but the companies that truly enable this structural shift should see meaningful long- term growth.

Our basket below captures broad thematic exposure to these Agentic Utilities. The Agentic Utilities Basket md) You can find the **Agentic Utilities** basket on com**. This article is for informational purposes only and does not constitute investment advice. By accessing this material, you agree to our **Terms of Service**.