When AI Agents Pay and Sellers Monetize: Building x402 Apps on AWS — Anil Nadiminti, AWS
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When AI Agents Pay and Sellers Monetize: Building x402 Apps on AWS
Anil Nadiminti explains how x402 turns a payment into access to a resource, how AgentCore Payments separates spending from the agent loop, and how AWS WAF lets sellers price AI requests at the edge.
From a talk by Anil Nadiminti
At a glance
Ideas worth remembering
x402 turns a resource request into a payment exchange: the server returns Payment Required, receives authorization, uses a facilitator for verification and settlement, then delivers content.
AgentCore Payments places session budgets, expiry, wallet integration and payment execution outside the agent's reasoning loop. Imported private keys remain in a KMS-protected store the agent cannot access.
Seller-side WAF rules can combine resource path, verified bot identity and classified intent to set prices at the edge without changing the origin.
The economic target is payment overhead small enough for individual resource purchases. A 25-cent fixed fee is 250 times a tenth-of-a-cent purchase; the closing examples extend these purchases to inference, compute and MCP tools.
A human paywall stops an autonomous task
A news portal works until the reader hits a paywall. A human can enter credit card details, choose a weekly, monthly or annual subscription, and obtain access. An autonomous agent encounters the same obstacle while completing a task, but the next step becomes a request for help: someone must supply payment details or an API key. Anil Nadiminti, a Senior Solutions Architect at AWS, starts with this familiar example because it exposes where autonomy breaks. The agent can find the content; it cannot finish obtaining it.
The urgency comes from a change in who requests resources. Nadiminti puts bot traffic above human traffic and says AI agents account for 95 percent of that bot traffic. The recording does not establish the population or measurement method behind those figures, so they serve as his framing rather than a universal traffic estimate. His forecast—about a billion agents performing tasks by 2027, with 60 percent of enterprises using agentic workflows—extends the same premise: more requests will arrive from software trying to complete work independently.
The news publisher has an uncomfortable choice:
- Block bots: Blocking can forfeit AI discovery, citations and potential licensing partnerships. Content that an agent cannot reach is harder for it to incorporate into an answer.
- Allow unpaid access: Large numbers of requests create infrastructure costs, while unrestricted reuse can weaken attribution and control over the content.
Paid machine access offers another option: let the agent obtain the resource and let the publisher earn from the request.
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Buyers need spending controls; sellers need payment at the edge
Agent ecommerce joins two different requirements. On the buy side, an agent needs access to premium or licensed resources, a wallet and the ability to make small payments. An enterprise also needs to prevent that agent from going on a spending spree. On the sell side, a publisher needs to understand the requesting bot and charge for its activity without rebuilding the origin—the infrastructure that stores and serves the content. A shared machine-to-machine payment protocol lets these sides meet at the request.
The proposed change is from approving each purchase to setting conditions under which purchases may happen. Humans move from being in the transaction loop to supervising it, or staying outside it once limits are configured. Pay-per-use and pay-per-execution can then follow the agent's work at request speed, rather than requiring a new human subscription workflow whenever it encounters a paid resource.
Small payments make the fee structure decisive. Nadiminti uses a card-payment example with a 25-cent minimum fee plus 2.5 percent. For a purchase worth a tenth of a cent, the fixed fee alone is 250 times the purchase: $0.25 ÷ $0.001 = 250. The percentage fee barely matters beside that floor. Charging separately for each tiny resource request therefore requires a payment path whose overhead fits the size of the purchase.
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x402 makes payment part of the resource request
x402 builds on HTTP status code 402, reserved for “Payment Required.” Nadiminti presents the protocol introduced by Coinbase as a way for machines to negotiate access through HTTP. Return to the news portal: instead of sending the agent into a human checkout, the server responds that payment is required. The client chooses a payment method and sends payment authorization back to the server.
Authorization is followed by verification and settlement. The server uses a facilitator to verify the payment authorization and complete the transaction. In the flow described here, the server releases the content after settlement completes on chain. The observable change is simple: the resource request that previously stalled now reaches the content response through a payment exchange.
What must happen between the first request and delivery of the content? The diagram separates the HTTP exchange from the facilitator's work. Payment authorization starts the transaction process; completed settlement is what precedes content delivery.
This is the force of the phrase “payment is the … credential”: the purchase itself enables access, without setting up a subscription or a separate API key for that resource. Nadiminti describes no protocol fee and nominal merchant gas fees. His description of “zero wait time” means an automated request-speed experience, rather than literal zero latency; the later figures include a 200-millisecond average settlement time on Base.
The protocol is also presented as extensible and open to implementation. Nadiminti dates its introduction to May 2025 and describes Linux Foundation open governance, with support from Coinbase, AWS, Google, Stripe, Anthropic, Cloudflare and Circle. That shared protocol supplies the connection between the AWS buyer and seller services introduced next.
Requests a resource, chooses a payment method and sends authorization.
The server uses a facilitator for verification and settlement, then returns the requested content.
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AgentCore Payments gives the buyer a wallet and a budget
AgentCore Payments, part of Bedrock AgentCore, supplies the buyer's payment machinery. The service described in the recording supports Coinbase wallets and a Stripe preview wallet option, with payment connectors orchestrating transactions. It supports x402 at the time of the presentation; support for additional protocols is planned. Protocol independence is a design goal, while x402 is the concrete path shown.
A payment session constrains spending programmatically. It can specify a maximum amount and an expiry expressed in minutes. Nadiminti's example is permission to spend $5 over 30 or 60 days. The amount limits how much the session can spend; the expiry limits how long that permission lasts. Built-in observability is intended to let developers trace the activity across the stack.
The agent continues using tools, MCP servers and other resources to complete the user's task. When one of those requests returns 402, AgentCore Payments handles the transaction and tells the agent that settlement has happened. The agent can then continue toward its response. Applied to the opening example, the news paywall becomes a payment event handled by the configured service, rather than a reason to ask the user to enter card details mid-task.
Discovery is another part of the buyer's path. AgentCore Gateway exposes internal APIs through MCP, making them available as agent tools. Its integration also gives AgentCore Payments access to a Coinbase discovery service that Nadiminti describes as offering more than 10,000 endpoints available for transactions. Finding a purchasable resource and paying for it are thus separate capabilities connected through the gateway and payment service.
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Keep keys and payment execution outside the agent loop
The wallet integration does not give the agent its private keys. Imported secret keys are stored in a secure token wallet protected by KMS, and the agent cannot access them. That distinction matters: an agent may request a paid resource without receiving the secrets that enable the payment infrastructure to transact.
Payment execution is also decoupled from the agent's reasoning loop. Skills and inputs can be poisoned by malicious actors, so the payment path runs in a deterministic layer responsible for connectors, orchestration, limits and third-party wallet integration. The model can continue choosing tools and pursuing the task, while spending controls operate outside its nondeterministic decisions. This design contains payment authority; it does not make poisoned agent inputs harmless.
Where does a resource request cross into payment execution? The diagram shows the 402 response triggering the separate payment layer, with session controls and protected wallet keys attached to that layer. The return path communicates settlement to the agent rather than passing private keys into its loop.
The separation also supports reuse of existing agent models and frameworks. Nadiminti describes payment handling as a layer that can operate without changing the agent's code. In the narrated demo, an agent discovers a secured resource, AgentCore Payments uses an already integrated wallet, and the transaction completes. The change from the opening paywall is the removal of the manual payment interruption, with the wallet and controls configured beforehand.
Uses tools and resources with the chosen model and framework.
Resource access triggers payment handling, but wallet secrets remain in the protected payment infrastructure.
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WAF prices the request before it reaches the origin
The seller needs enough information about a request to choose a price. Nadiminti describes AWS Web Application Firewall bot detection as recognizing more than 650 bot types, verifying bots by signature and identifying intent, such as model training or retrieval-augmented search. Identity and intent answer different questions: which organization is requesting the content, and what kind of use is the request associated with? The talk does not explain the intent-classification method or its accuracy, so intent-based pricing depends on that classification working.
WAF AI traffic monetization applies that context at the edge. With CloudFront, the content distribution network, a publisher can attach WAF and configure monetization through the console or infrastructure as code. Nadiminti also describes applying WAF to internal APIs exposed through AgentCore Gateway as MCP tools. In both cases, the seller adds payment handling in front of an existing resource rather than moving the resource into a new application.
The seller-side flow detects and categorizes the bot, identifies its intent, verifies it and uses x402 to monetize the request. The service is presented as requiring no SDK or origin changes, with publishers retaining 100 percent of revenue and no transaction or subscription fees. Those claims concern the monetization service; they do not remove the infrastructure costs that motivated the seller's dilemma or the gas fees described in the protocol discussion.
Pricing can use several independent dimensions:
- Path:
/blog,/researchand an API endpoint can have different rates because they expose different resources. - Identity: A verified bot from an organization with an existing commercial relationship can receive a different price from an unverified bot. Anthropic is the example partner in the talk.
- Intent: Training access can have a different rate from search access.
WAF rules combine these conditions with AND/OR logic. A seller can therefore price a request using several attributes together, rather than charging every bot the same amount.
The news portal can now distinguish human readers, verified bots and unverified bots. Human access might remain free or carry its own price, while bots follow different payment rules. Revenue dashboards aggregate results by bot and show the paths being accessed, giving the publisher a way to inspect which resources and requesters contribute to its revenue model.
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The transaction examples extend beyond content
Paid news access is only one use. The closing examples include LLM inference, compute, web scraping, research agents, agent-to-agent transactions and monetized MCP services. Each gives an agent a resource or capability it can purchase while performing work. The same request-and-payment pattern can therefore apply to executing a tool as well as reading a page.
For the preceding 12 months of Coinbase marketplace activity, Nadiminti reports $50 million in transaction volume across 170 million transactions. He also gives an average settlement time of 200 milliseconds on Base and a cost of about a tenth of a cent per transaction. These figures describe the cited activity and settlement setting, rather than a benchmark of the AWS services. They make the economic target concrete: many small purchases need both low payment overhead and short settlement delays.
Payments sit within the larger Bedrock AgentCore ecosystem. Builders can bring their own model and framework, add memory, use managed knowledge bases and web search, expose internal APIs through MCP, and evaluate agent performance. AgentCore Runtime hosts the agentic application at scale; Nadiminti describes each request as running in its own isolated micro virtual machine. The final architecture keeps these jobs distinct: the runtime serves the agent, the gateway connects resources, and the payment layer handles purchases when access requires them.
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Resources
Related talks
- Why Your AI Agent Needs a Wallet: USDC and Nanopayments — Harshal Bhangale, Circle
A companion topic for the wallet and small-payment economics behind paid resource requests.
- x402 isn’t good (yet) — Jan Curn, Apify
A contrasting perspective on the same protocol, useful alongside this presentation's adoption case.
- Building Deterministic Infrastructure for Non-Deterministic AI Agents
Extends the architectural question raised by keeping payment execution outside a nondeterministic agent loop.
Read the complete timestamped transcript
- 0:01
[music]
- 0:12
Hello all, welcome to uh the agent
- 0:14
e-commerce track and uh I'm Anil Lminti.
- 0:17
I'm a senior solutions architect here at
- 0:19
AWS. Uh I'm I'm here to talk to you
- 0:21
today about how AWS is innovating and uh
- 0:25
how you can build apps on uh AWS to
- 0:29
support the agent e-commerce. So uh
- 0:32
welcome to the session. Uh just to get
- 0:34
you started, let me set the stage with
- 0:36
something that you're very familiar
- 0:37
with. Uh imagine that you are your
- 0:40
organization is building uh a news
- 0:43
portal like this, right? So you're all
- 0:45
familiar with something where you're
- 0:46
accessing the news content and then
- 0:48
suddenly you hit a payw wall, right? So
- 0:50
this is where uh you pull out your
- 0:52
wallet or you try to uh figure out how
- 0:55
to make the payments, set up your uh
- 0:57
credentials, access keys in the sense
- 0:59
that you you make a credit card
- 1:01
transaction weekly, monthly or annual
- 1:03
subscription and then get started to
- 1:05
access the content. Right? So this is
- 1:06
all the content that is behind a payw
- 1:08
wall. But uh what we see now is that uh
- 1:11
much of the traffic that is actually
- 1:13
being uh sent to these uh uh portals now
- 1:16
on the internet is all coming from bots.
- 1:18
We see that we at a infliction point
- 1:20
where uh the bot traffic is more than
- 1:23
the human traffic right. So it's just uh
- 1:25
actually in fact surpassed that and uh
- 1:28
95% of that bot traffic is coming from
- 1:30
AI agents. So uh essentially we are also
- 1:35
looking at the rise of autonomous agents
- 1:37
right so where we all started using LLMs
- 1:41
asking questions asking for
- 1:42
summarization being able to uh get help
- 1:45
with using them as co-pilots getting
- 1:47
them to do agentic work to uh do
- 1:49
multi-step tasks and now we're in the
- 1:51
phase of autonomous agents where agents
- 1:54
are using the reasoning powers of large
- 1:56
language models to complete a task and
- 1:58
completing a task means that uh it it
- 2:00
has to go do whatever you're asking it
- 2:03
to do. And that's kind of where we are
- 2:04
in the uh journey. And we see that by
- 2:07
2027 about a billion agents will be
- 2:10
running uh performing tasks and 60% of
- 2:13
the enterprises will already be uh using
- 2:15
agentic workflows. So uh what happens
- 2:18
when agents hit these pay walls that uh
- 2:20
we just saw? When agents hit the pay
- 2:22
walls, they stall, they can't operate
- 2:24
and you see those messages that hey I
- 2:26
cannot access content. Uh right? So at
- 2:28
that point humans get in the loop. They
- 2:31
try to enter and put the credit card
- 2:34
details or API keys do the transactions
- 2:37
for the AI agents. But all of that is
- 2:39
manual friction, right? So essentially
- 2:41
bringing in a human in the loop. So
- 2:43
autonom autonomous agents actually break
- 2:45
[clears throat] at that point where the
- 2:47
uh friction is now building up. So uh
- 2:50
now sellers of the content have uh you
- 2:53
know couple of options right. So block
- 2:55
all the bot traffic but by blocking all
- 2:57
the traffic they lose this AI powered
- 2:59
discovery they miss this uh partnership
- 3:02
licensing options and AI also now
- 3:06
supports citations right so the
- 3:07
responses so they lose all of that uh
- 3:09
powered citations as well if they can't
- 3:10
sell the content essentially they lose
- 3:13
uh revenue generating options and if you
- 3:15
allow the bots to access those uh uh the
- 3:18
content what it means is that you know
- 3:20
hundreds of thousands of bots or
- 3:22
millions of bots could be hitting your
- 3:23
uh uh infrastructure which means that
- 3:25
the infrastructure costs will also raise
- 3:27
and uh you need to be able to support
- 3:29
all of that right so you also uh when
- 3:31
you allow bots to access content you
- 3:33
lose attribution the IP itself right so
- 3:35
because content is now freely available
- 3:37
so uh both these decisions are probably
- 3:41
not not a good option they're not ideal
- 3:43
so there should be another ideal option
- 3:45
where uh you would want to have your AI
- 3:48
agents being able to get and pay for the
- 3:50
content that they are looking for and
- 3:53
monetize on
- 3:54
So uh now we look at the next phase of
- 3:57
uh uh rise in autonomous agents where
- 4:00
agents should be able to transact and
- 4:02
make uh discover other agents resources
- 4:05
and essentially make payments. Right? So
- 4:07
this is the definition of agent
- 4:08
e-commerce where AI agents can
- 4:10
essentially discover uh you know it's a
- 4:12
form of e-commerce where autonomous
- 4:14
agents can uh discover independently and
- 4:16
make those uh uh settlements and then
- 4:18
access content. So let's look at uh the
- 4:22
agent e-commerce the two sides of agent
- 4:23
e-commerce the buy side and the sell
- 4:25
side. So uh when we talk about the buy
- 4:27
side the agents are making these
- 4:29
transactions and on the sell side the
- 4:31
sellers of the content are trying to
- 4:33
monetize on the content. So on the buy
- 4:35
side when you look at things AI agents
- 4:37
want to access these premium paywalled
- 4:39
content licensed content they want to be
- 4:41
able to hold wallets which they do not
- 4:43
have the option today and they want to
- 4:45
be able to make these microtransactions
- 4:46
you just heard in the prior talk as
- 4:48
well. But enterprises when they come to
- 4:50
this point they want more guardrails and
- 4:52
they do not want agents to go on
- 4:54
spending spree. Think about it right?
- 4:56
Would you allow your AI agents to get
- 4:57
handled on your wallets or credit cards
- 4:59
to be able to do that transactions and
- 5:01
where they could go rogue as well right?
- 5:03
So that's what the buyer side is looking
- 5:05
at. And on the seller side uh the there
- 5:08
are again billions of transactions that
- 5:10
be happening with these AI bots. So
- 5:11
sellers really want to be able to
- 5:13
understand what kinds of bots are
- 5:15
operating uh what kinds of transactions
- 5:17
they're making and uh really uh do this
- 5:20
at the edge. The sellers don't want to
- 5:22
change their entire infrastructure and
- 5:24
origins where the content is sitting.
- 5:26
They want to be able to do this at the
- 5:27
edge without changing much of this.
- 5:29
Right? So there is again uh one common
- 5:32
thing here on the buyer side and the
- 5:33
seller side which is a standardized
- 5:35
approach or a protocol to be able to
- 5:36
solve for this uh machine to machine
- 5:38
payments at the edge. So uh bottom line
- 5:41
buyers are saying that they want their
- 5:42
agents to be able to pay for content uh
- 5:45
and uh not have humans approving this
- 5:48
and then the sellers are saying that
- 5:49
they want to be able to earn from the AI
- 5:51
traffic. So bottom line the subscription
- 5:54
model is going to change with humans in
- 5:56
the loop to becoming humans on the loop
- 5:59
or out of the loop and that's kind of
- 6:01
what we are building towards. uh the the
- 6:03
traditional one-sizefits model does not
- 6:06
work anymore because of the fact that uh
- 6:08
again we look at that in the next slide
- 6:10
where uh the transactions cost will not
- 6:12
really work right all of this needs to
- 6:14
happening at realtime speed and the
- 6:17
paper use and paper execution is what
- 6:19
the f future is going to look like so if
- 6:22
you're a seller you would have come
- 6:23
across this right so there is a 25 cent
- 6:25
minimum transaction fees as well as 2.5%
- 6:29
on top of that and all of these
- 6:31
microtransactions are uh you know in the
- 6:33
in in like a cent subsend or you know
- 6:36
micro cents is what we are calling them.
- 6:38
So if you add like a 25 cents on top of
- 6:40
that it's essentially like 250 times to
- 6:42
what you know they are essentially
- 6:44
paying for. So the all of this model
- 6:46
does not work and uh while we are trying
- 6:49
to solve for that a very brief history
- 6:50
uh of this is every HTTP call
- 6:53
essentially responds back uh you know
- 6:54
there's a response for that you've seen
- 6:56
200 status codes 404 uh you know and the
- 6:59
301 these are all like status codes that
- 7:01
you're familiar with and then there is
- 7:03
one status code which is 402 which has
- 7:05
uh not been used it was reserved for
- 7:07
payment required and now finally uh
- 7:10
Coinbase has introduced this uh as uh
- 7:13
transactions over 402 which is also
- 7:15
called as X42 where they uh you know the
- 7:17
protocol talks about how you can do
- 7:19
machine to-achine transactions uh using
- 7:21
this protocol right so we'll take a
- 7:23
closer look at that but uh what happens
- 7:25
within the protocol is uh you know if
- 7:27
you look at this uh flowchart here a
- 7:29
client makes a request to the server and
- 7:31
then the server responds back with the
- 7:33
payment required uh the client then
- 7:36
figures out what is the payment method
- 7:37
that it wants to operate and then it
- 7:39
sends the payment authorization to the
- 7:41
server the server then utilizes a
- 7:43
facilitator to complete the verification
- 7:46
and then also utilizes the same
- 7:48
facilitator to complete the transaction
- 7:50
and once the settlement is completed
- 7:52
onchain essentially the server will then
- 7:54
respond back with the content right so
- 7:56
this is what's happening under the X42
- 7:58
protocol I thought I'll pick one of the
- 7:59
protocols and just uh explain this to
- 8:01
you but uh why this is compelling is uh
- 8:03
you know essentially there is no
- 8:04
protocol fees uh or the fees that a
- 8:07
consumer is paying for uh you know these
- 8:10
microcent transactions and the merchant
- 8:12
is paying very nominal gas fees is uh
- 8:14
again there is zero wait time this is
- 8:16
happening at the speed of internet and
- 8:19
uh there is no friction there is no API
- 8:21
keys to set up no subscriptions and the
- 8:23
payment is the essentially the uh
- 8:25
credential to be able to get the content
- 8:27
so there is no centralization it's uh
- 8:30
x42 can be extended as well and you can
- 8:32
implement it and there are no
- 8:33
restrictions as well so some key
- 8:35
milestones here are you know it was
- 8:37
introduced last year uh May 2025 it
- 8:41
explored is not part of the Linux
- 8:43
Foundation under open governance and
- 8:46
it's backed by Coinbase, AWS,
- 8:49
Google, Stripe, Anthropic, Cloudflare
- 8:52
and Circle right so many more folks in
- 8:55
there that are supporting that
- 8:56
organizations in there. So uh from
- 8:58
Amazon we have also released agent core
- 9:00
payments uh under the bedrock uh suite
- 9:03
so where uh agents will make be able to
- 9:06
make payments and we'll go into some of
- 9:07
the details here. So let's talk about
- 9:09
the buyer side here and what is involved
- 9:11
right? So we uh we understood from the
- 9:13
developers that they really want to be
- 9:15
able to get this uh agents to have
- 9:16
wallet support. They want to be able to
- 9:18
have real-time settlement uh have the
- 9:21
budget and guardrails which enterprises
- 9:23
really want and observability throughout
- 9:26
the stack where they would want to have
- 9:28
uh the full stack trace of everything
- 9:29
that's happening uh under the hood. So
- 9:32
I'm excited to share with you that we've
- 9:34
launched agent core payments and this is
- 9:35
a service that allows AI agents to
- 9:38
autonomously discover uh authorize and
- 9:40
execute payments with a few lines of
- 9:42
code. Uh now we've launched this in
- 9:44
partnership with Coinbase and Stripe
- 9:46
where you can bring wallets from
- 9:49
Coinbase and Stripe preview to be able
- 9:50
to do these operations and we'll go into
- 9:53
some of the details but uh the core
- 9:55
capabilities to start with are wallet
- 9:56
support where you can bring the the
- 9:59
wallets from Coinbase and Stripe. you're
- 10:01
able to orchestrate the payments using
- 10:03
payment connectors and uh today we
- 10:05
support X42 with many more protocols to
- 10:08
uh you know that are in the pipeline.
- 10:09
The service is designed to be protocol
- 10:11
agnostic. So as new protocols emerge, we
- 10:14
are going to be adding the support for
- 10:16
those protocols as well. And uh you know
- 10:18
the the settlement is going to be
- 10:20
instantaneous uh instant essentially and
- 10:23
uh the payment limits can be set uh
- 10:25
which is the most important thing that
- 10:26
we spoke about where enterprises are
- 10:28
looking to put some payment limits and
- 10:30
guards on how these transactions can
- 10:31
operate. So uh observability is builtin
- 10:35
and uh essentially all of this uh
- 10:37
operates with uh you know security as
- 10:39
the uh layer that is operating uh the
- 10:41
whole model right. So with that let's
- 10:43
look at uh some of these uh details on
- 10:46
how the payments limit can be set up
- 10:47
right. So you can create payment
- 10:48
sessions where you can set the
- 10:51
programmatically set the maximum amount
- 10:53
of uh uh value that can be used for
- 10:56
transactions or you can also set expiry
- 10:59
time in minutes. think where uh you are
- 11:01
able to set that uh I can the agent can
- 11:04
actually spend maybe $5 in 30 days or 60
- 11:08
days right so that's kind of the
- 11:09
operation uh model that you can set with
- 11:11
many more uh you know details that are
- 11:13
available I'm only going over a few
- 11:15
features but uh let's look at what
- 11:18
happens on the buyer side when the user
- 11:20
is asking an agent to make a particular
- 11:23
uh you know requesting for resources
- 11:25
right so agent completes the request by
- 11:28
accessing tools MPPP servers other
- 11:30
resources as well. So at that point of
- 11:32
time if the agent uh is uh looking at
- 11:36
you know it it finds that the there is a
- 11:38
response from one of the tool calls or
- 11:40
requests with a 402 agent core payments
- 11:43
is going to handle the request uh to
- 11:45
complete the transaction and then let
- 11:47
the AI agent know that uh essentially
- 11:49
the settlement happened and the AI uh
- 11:52
agent will be able to respond back with
- 11:54
the users. So in this process when the
- 11:57
wallets are uh wallet support is
- 11:59
imported the the the secret keys that
- 12:02
you use to import the wallets actually
- 12:04
are stored in a secure token wallet that
- 12:06
is uh secured by KMS where you know
- 12:10
that's there. So essentially the agent
- 12:12
does not have access to the private
- 12:13
keys. This is most important uh to note.
- 12:16
And uh next thing is that uh agent core
- 12:18
payments is also integrated uh through
- 12:20
uh a gateway which is also part of uh
- 12:22
which is another service that we have to
- 12:25
mp5 your internal APIs. Uh through agent
- 12:28
core gateway uh the agent core payments
- 12:31
can get access to discovery service uh
- 12:33
in coinbase where there are 10,000 plus
- 12:36
endpoints that are available to transact
- 12:38
and then u again there is a per session
- 12:40
budget that we just discussed as well.
- 12:43
So uh there is a decoupling of agent
- 12:46
infrastructure and uh the payment
- 12:48
infrastructure by design where the agent
- 12:50
can operate it it in its own loop and
- 12:52
whenever it sees the payment the payment
- 12:55
uh connectors orchestration payment
- 12:56
limits and integration with third party
- 12:58
wallets can happen right so it's
- 13:00
important to decouple them because again
- 13:02
skills can be poisoned inputs for the
- 13:05
agents can also be uh you know poisoned
- 13:07
by inputs as well right so where
- 13:09
malicious actors could uh try to do that
- 13:12
so By decoupling and making this uh by
- 13:16
design a agents can essentially have a
- 13:18
secure path for these transactions and
- 13:20
payments do not touch the you know
- 13:24
undeterministic path but this is more on
- 13:25
a deterministic uh uh layer as well. So
- 13:28
why this is important is that uh again
- 13:30
agents uh the code of the agents does
- 13:33
not have to change. You can bring your
- 13:35
own model frameworks and then the
- 13:36
payment itself uh can flow through uh in
- 13:39
the payment uh uh layer itself. So again
- 13:43
the controls the policies pending
- 13:45
controls can be outside of the payment
- 13:48
stack itself and again it's this is
- 13:50
built to be protocol agnostic. So this
- 13:52
is uh one of the console screens where
- 13:54
it shows how you can import uh the
- 13:56
payment connector uh and it shows that
- 13:59
you know you can select the the coinbase
- 14:01
wallet and the stripe preview wallet
- 14:03
from the console and uh this is a demo
- 14:06
in action where we are showing how a
- 14:08
secure resource can be accessed. Now in
- 14:10
this case uh the AI agent is essentially
- 14:14
making a you know discovering that there
- 14:15
is a secure source the agent core
- 14:18
payments is kicking in and then it's
- 14:20
completing the transaction by utilizing
- 14:23
the wallet that is already integrated
- 14:25
and uh the transaction completes. Now uh
- 14:29
this is on the buyer side. Now let's
- 14:32
look at the seller side to understand
- 14:33
what's happening. Right? So uh again
- 14:35
there is a lot of bot activity that's
- 14:37
happening. We have released uh under uh
- 14:40
the uh AWS web application firewall a
- 14:42
feature where we have bot detection in
- 14:44
place. Today we detect over 650
- 14:46
different types of bots. Think of bots
- 14:48
like perplexity bot, GPD bot, cloud bot,
- 14:51
you know, again Google bots, right? So
- 14:53
there are so many bots that are out
- 14:54
there. So we're able to detect also
- 14:57
understand the intent of these bots. So
- 14:58
why are these bots accessing the
- 15:00
content? Are they accessing the content
- 15:02
to train their models? are they doing it
- 15:04
because they have to respond back to an
- 15:06
intent where they're uh responding for a
- 15:08
rag search. So we're able to identify
- 15:10
the intent. We're also able to verify
- 15:12
the bots and identify them by a
- 15:14
signature. So we are able to say hey
- 15:17
this is a verified bot. So maybe you
- 15:19
have uh built a relation with one of
- 15:21
these organizations and these
- 15:24
verification will allow you to have a
- 15:26
different pricing for the organizations
- 15:28
that are already verified. So we'll look
- 15:30
at that in a second. So there's also
- 15:31
real-time traffic analysis that allows
- 15:34
uh more uh to be customized. And I'm
- 15:36
also happy to share with you today that
- 15:38
we announced VAF AI traffic
- 15:41
monetization. This is a service that
- 15:42
allows you to monetize uh based on the
- 15:45
content that uh you know based on how
- 15:47
you can measure, verify and monetize
- 15:49
based on the AI traffic that is hitting
- 15:51
your endpoints. Now if you might be
- 15:53
familiar with CloudFront which is our
- 15:55
content distribution network you can add
- 15:57
a web application firewall at that point
- 15:59
and essentially you can moni start
- 16:01
monetizing uh right away and based on a
- 16:04
few clicks uh you can do that again
- 16:06
using infrastructure as code as well.
- 16:07
Now I also spoke about a gateway service
- 16:10
that allows you to expose your AI
- 16:12
endpoints uh that are internal use and
- 16:15
mcpify them. So the same web application
- 16:17
firewalls can be used there. So your
- 16:19
internal APIs can be MCPI and then you
- 16:21
can start monetizing as well. So what
- 16:23
happens during monetization? The AI
- 16:25
agent AI bot essentially requests for
- 16:28
some content. The bot context uh
- 16:31
understands what kinds of bots is
- 16:33
detecting it. It's able to detect the
- 16:34
bot. It's able to categorize and
- 16:37
understand the intent of the bot as we
- 16:39
discussed earlier and verify uh and
- 16:42
check what kind of bot is available. So
- 16:43
then we are able to monetize uh using
- 16:46
the X42 and the publishers get paid as
- 16:49
well. So important to note is that again
- 16:51
there is no SDK change no changes at the
- 16:53
origin origin. Publishers keep 100% of
- 16:56
the revenue as well and uh again there
- 16:59
is no transaction fees or subscription
- 17:00
fees. So this supports X42 and we are
- 17:04
adding support for more uh protocols as
- 17:06
well. U a few dimensions on how you can
- 17:09
start monetizing. Think uh you have
- 17:11
separate paths. So a slash uh uh blog in
- 17:15
this case can be charging for a
- 17:16
different rate than a slash research or
- 17:19
maybe an API endpoint itself and uh you
- 17:22
you know the identity of these bots.
- 17:24
Again, if you make uh some kind of a
- 17:26
relationship with the uh bots uh
- 17:29
companies, organizations, maybe you make
- 17:30
a relation with Anthropic, then you can
- 17:33
essentially have a different pricing for
- 17:34
those bots versus different uh
- 17:36
unverified bots, right? So, uh think of
- 17:39
that option. And then you can also set
- 17:40
different pricing for intent as well. If
- 17:42
somebody's coming here, if a bot is
- 17:45
accessing the content for uh again
- 17:47
training, you can charge a different
- 17:48
rate than what it's doing for a search
- 17:50
as well. So again, these are different
- 17:52
VAF rules. they can be uh in a
- 17:54
combination of end or or then you can
- 17:57
access that. So this is how the
- 17:58
reimagine flow would look like uh where
- 18:01
you're allowing the AI agents or you
- 18:04
know essentially verified bots and
- 18:06
unverified bots to have different
- 18:07
pricing and humans to have different
- 18:09
pricing. Some cases you want to have
- 18:11
humans to access the content freely.
- 18:12
Some cases again the humans could be
- 18:14
charged where the bots could be charged
- 18:16
differently as well. Right? So this is
- 18:18
how uh you know you can reimagine the
- 18:20
price. So again there is some uh
- 18:21
dashboards that show the revenue numbers
- 18:24
and how uh you know you're uh able to
- 18:27
aggregate by different uh bots and
- 18:30
figure out what kind of revenue model
- 18:31
you want to operate and it also shows
- 18:33
what is the path uh that's being
- 18:35
accessed by these bots right so uh again
- 18:38
what is currently uh everyone using
- 18:40
agent e-commerce for they're using agent
- 18:42
commerce to uh run um again LLM
- 18:45
inference getting uh compute web
- 18:48
scraping uh they're uh creating research
- 18:50
search agents to be able to uh you know
- 18:53
serve the requests and uh agent to agent
- 18:55
as well. We see MCPs also being
- 18:57
monetized now. Uh again this is the last
- 19:00
12 months of uh traffic uh from um again
- 19:04
what we are seeing on uh Coinbase
- 19:06
agentic market u where you're seeing
- 19:08
that a $50 million volume transaction
- 19:10
happened over 170 million transactions.
- 19:13
The average settlement time is 200
- 19:15
milliseconds on base uh with about a
- 19:19
tenth of a cent as cost per transaction.
- 19:22
So I spoke to you about agent uh agent
- 19:25
core payments which is uh one of the you
- 19:28
know parts of the bigger ecosystem agent
- 19:30
uh bed agent core uh where you can
- 19:33
essentially bring your own model. You
- 19:34
can bring your own framework and uh you
- 19:37
can start building AI agents. You can
- 19:40
add context by adding memory. You can uh
- 19:43
bring again your own managed knowledge
- 19:45
bases. You can add web search
- 19:46
capabilities to the agents. you can
- 19:48
mcpify your internal APIs and then you
- 19:51
can uh have many more features like
- 19:53
being able to run evaluation on how your
- 19:55
agents are performing. So again you can
- 19:58
uh use runtime which is a bedrock uh
- 20:01
agent core runtime where you can bring
- 20:03
your own agentic uh uh application and
- 20:06
serve uh at scale and every request will
- 20:10
have uh its own isolated uh micro
- 20:12
virtual machine that is running to serve
- 20:14
the requests. So that's it from my side
- 20:17
here today. Uh thank you and uh hope you
- 20:20
have a nice day.
- 20:22
[applause]
- 20:38
>> [music]