Prem Munday

Agentic commerce, where artificial intelligence (AI) systems act on behalf of users to find products, negotiate purchases, and execute payments, is developing rapidly. This creates shared responsibility: developers must build legally sound systems, while regulators and infrastructure operators must consider how existing frameworks apply and where new approaches may be needed. The Bank of England operates, oversees and is co-ordinating the design of payment systems as part of its statutory responsibilities. Emerging agent‑based payments can have implications for how the private sector safely innovates and how regulators and payment infrastructure providers adapt. This post explores how agentic commerce could reshape future payment design.

How might agentic commerce be used in practice?

It is important to note from the outset that agentic AI always requires human deployment, and that deployers retain legal responsibility for an agent’s actions; responsibility does not sit within a ‘black box’.

Visa provides one example from industry for how agentic commerce could be used, setting out a four‑step adoption process:

• Recommending products: using Large Language Models (LLMs) to recommend better products. An agent could compare products and recommend the most suitable option.
• Initiating payments on your behalf: agents can make payments with user verification, such as one-off bill payments.
• Transacting on your behalf: agents execute payments according to predefined rules, such as renewing a service when usage hits a threshold.
• Orchestrating payments: an agent owns the whole lifecycle of payments and communicates with other agents to orchestrate complex payment flows.

This last point leads to a potential scenario where agentic payments become like ‘locals paying at bazaars’, with agents forming informal relationship-based agreements with other agents. This highlights a future state where agents might adapt the behaviours we see in payments, with potential downstream impacts:

• Payments move from being human-initiated to agent-initiated.
• There is an increase in speed and volume of transactions as agents may transact, negotiate, return and refund payments at speeds faster than humans.
• There are decreased transaction sizes, as agents may transact in small values to complete complex, orchestrated workflows.
• We need new authentication to resolve how humans and their agents interact, moving from Know-Your-Customer (KYC) to Know-Your-Agent (KYA) for payments, as highlighted by Dave Birch.

While some automated activity exists today in areas such as algorithmic trading, consumer and retail payments introduce distinct requirements around authentication, liability and consumer protection.

So how do payments and agents interact, and what are the responses to this?

A previous post examined how existing financial infrastructure can govern agents. I am developing this by highlighting how the infrastructure for managing agents can impact how payment systems are built.

Today’s agent payments landscape is fragmented, with multiple identity frameworks, payment protocols and communication layers that are not interoperable across providers. For example, some agent identity standards are only supported by specific card schemes, while agent payments protocols and how they integrate with checkouts vary across stablecoin and card‑based rails. Addressing this fragmentation is a shared task: the private sector needs to build and adopt interoperable standards; with public sector participants having a role in setting clear expectations and, where appropriate, common requirements.

We are already seeing new private sector solutions to solve the issues around fragmentation, standards and interoperability with different payments methods. These solutions tend to cover four aspects: how agents communicate with each other, how they pay, how they assure identity and how they settle payments.

• How agents communicate: New shared standards are emerging that allow AI agents to exchange information and instructions with each other. Examples include the Model Context Protocol (MCP) and Agent2Agent (A2A) frameworks. Think of these as like a common language that different agents can use regardless of who built them.
• How agents pay: New protocols are being developed to define how agents interact with online checkouts and payments processes. Examples include the Agentic Commerce Protocol (ACP), Universal Commerce Protocol (UCP), and Agentic Payments Protocol (AP2). These are the equivalent to giving agents a standard way to navigate the payments processes, like authentication of your card, that humans currently do manually.
• How agents prove their identity: For payment systems to trust an agent, they need a reliable way to verify who or what is acting. Card schemes are developing their own solutions (such as Visa Intelligent Commerce and Mastercard’s Agent Pay). Some solutions have also been developed by users for specific blockchains, such as ERC-8004 for Ethereum. The challenge is that these approaches are not consistent with each other.
• Which payment rails agents use: A payment rail is the underlying infrastructure that moves and settles money from one party to another, such as card networks, Faster Payments or blockchain-based systems. Agents will need to connect to these rails to complete transactions. Both established card providers and newer blockchain-based options (like the X402 protocol) are developing ways to accommodate agent-initiated payments.

These innovations highlight a future route to solving issues with how agents and payments infrastructure work together; but there still may be issues that arise that require further integration with payments infrastructure, new standards or reimaging payments infrastructure we have.

So, what are the design challenges for building payment rails that work with agentic payments? Some of these fall on private sector designers; others raise questions for regulators and infrastructure operators.

The potential use-cases and private sector innovations bring to the fore a few questions I grapple with when thinking about designing future payments infrastructure. These are:

• How to ensure consistent identity and authentication across human and agent actors.
• Whether payment systems should support higher‑frequency, lower‑value transactions.
• How deterministic requirements in payment law can be upheld when interacting with non‑deterministic AI systems.
• How regulation can encourage interoperability and enable integration between competing standards.

On the first issue, one question when integrating agent identity into payments is what role there should be for a central entity to mandate agent identity, like the conduct principles around how the private sector is required to implement KYC.

On the second issue, agents might need faster, lower value and high-volume payments. We need to consider if the existing rails can support these transactions or if new ones are needed. This also raises a larger infrastructure point: do payment rails need to be designed and built from the start to incorporate the ways mentioned above on how agents communicate, pay, and prove identity, as opposed to these being bolted on afterwards?

Thirdly, payment systems are designed to be deterministic: given the same input, they produce the same result. This predictability underpins reconciliation, fraud controls and legal certainty. Agentic systems rely on probabilistic AI outputs. An agent may phrase requests differently, pursue alternative paths to achieve a goal, or retry transactions in unexpected ways. This mismatch creates risks. Agents could generate excessive payment requests, submit non‑standard data, or trigger unintended transactions. Payment rails therefore need guardrails, clear policies and the ability to detect erroneous agent‑initiated activity. Designers of agentic payment systems – public and private – will need to manage this variability while preserving the predictability required for settlement. Regulators may also need to consider appropriate safeguards and standards.

Finally, agents will require a universal way to interact with online checkouts and allow interoperability. Today, each checkout journey varies by merchant, payment service and rail. For agents to participate meaningfully in commerce, we will need a layer of abstraction that allows them to complete checkout flows regardless of whether the underlying rail is Visa, Mastercard, Faster Payments, or emerging options like stablecoins. This means designing rails that provide interoperability with agent identity, payments protocols and communications standards so payment systems can interact with the many private sector frameworks that might get adopted. This also raises the question of to what extent a central authority should be the standard setter for agentic payments and commerce, to better enable this innovation.

These design choices have direct implications for how policymakers and payment system designers approach future infrastructure. While acknowledging payments sits in the context of a broader ecosystem where agents would not just interact with payments through the underlying rails but also via intermediaries (eg wallets, checkouts etc), there is an understanding that different payment technologies have different strengths in an agentic context. Blockchain-based forms of money, including stablecoins and tokenised deposits, can support programable, rule-based payments and small transaction values and flexible automated workflows. Existing card-based rails benefit from broad acceptance and established consumer protections. It is important that payment system builders, be it in the public or private sector, choose technologies and design them in ways that meet appropriate safety and resilience standards.

The broader challenge, for the public and private sector, is to determine how existing payments infrastructure can be adapted for agentic use, and where genuinely new approaches may be needed. In some cases, existing infrastructure may be sufficient; in others, new technologies such as blockchains could enable step changes in how agents, payments and commerce interact. Acknowledging these decisions helps us understand how to develop payment systems that remain trusted and fit for purpose in an economy where agentic payments may grow.

Prem Munday works in the Bank’s Distributed Ledger Technology Lab.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Nades Raviraj and Danny Walker

Big and uncertain shocks have pushed UK inflation above the 2% inflation target over the past few years. How did financial markets view the Monetary Policy Committee’s (MPC’s) monetary policy during this unprecedented period? We show that markets have come to perceive the MPC’s policy stance as increasingly dependent on data releases. In particular, the responsiveness of UK market rates in tight windows around data releases rose significantly from 2022 to 2025. Zooming out to longer time windows in between MPC meetings, the change in services inflation explained a historically large share of the overall change in market rates over the same period.

UK interest rates have recently had to respond to big shocks.

The MPC has had to respond to very large shocks in recent years. It routinely considers a wide range of inputs – including data, analysis, forecasts and scenarios – when deciding its strategy and policy stance at each meeting. In its communications it has often pointed to specific data sources (for example, in the August 2023 minutes). Financial markets are important because they play a key role in transmitting monetary policy to the economy. So which inputs do financial markets appear to think the MPC cares about most? And how data dependent do they think policy has been in practice?

Interest rates have become more responsive to UK inflation data releases.

To analyse the data dependence of UK monetary policy as perceived by markets, we use high-frequency data to look at how short-term market interest rates respond in tight windows around UK inflation data releases, over the period 2012 to early 2026. The size of this response signals how strongly markets expect the MPC to react to domestic inflation news (refer to Healy and Jia (2024) and Mangiante et al (2025) for similar methods).

We find that the estimated responsiveness of market rates to inflation data has risen significantly in the UK since 2022. The estimated responsiveness to inflation data has risen by more than for GDP, PMI or labour market data. A 10 basis point surprise in UK inflation data releases was associated with a 3 basis point change on impact in three-year swap rates on average over the three years to 2025, compared to close to no change in the three years to 2022 (Chart 1). This points to greater importance of inflation data in market participants’ perceptions of the MPC’s reaction function, consistent with the idea that policy itself has become more data dependent.

Chart 1: Market rates have become more responsive to data surprises in narrow windows around UK inflation data releases since 2022

Note: Chart shows coefficient estimates from a set of rolling three-year regressions of the high-frequency change in three-year swap rates in 10-minute windows surrounding UK data releases on the total surprise in the data release.

We test whether market perceptions of higher data dependence also show up over longer time windows between consecutive MPC meetings.

We next assess whether perceptions of heightened market rate responsiveness to inflation data only affect market rates the day the data is released or whether those perceptions appear to be reflected over longer time periods. This is important because short-term changes in market rates that then dissipate quickly have smaller impacts on the economy than changes in rates that persist over time.

Specifically, we widen the window to measure the change in market interest rates from market close on the day of each MPC meeting to the next. This is a simple proxy for markets’ perception of the change in policy stance between MPC meetings. We then analyse how this change in market rates correlates with the macroeconomic data that is released between meetings (a simpler version of Orphanides (2001)). We compare how well different data – such as inflation (both outturns and short‑term forecasts), wage growth, employment and PMIs – explain movements in market rates.

This exercise has limitations. We add controls for other changes in macroeconomic data but these controls will not capture all possible drivers of market rates in these time windows. The results should therefore be interpreted as suggestive rather than definitive.

Markets have come to view services inflation data as a key determinant of the policy stance.

We find that services inflation – specifically the change in the annual service inflation rate between MPC meetings – was the most important variable in explaining UK market rates from the end of 2021 to 2025. A 10 basis point change in annual services inflation between MPC meetings was associated with around a 6 basis point change in the three-year swap rate over the three years to late 2025 (Chart 2). The change in services inflation explained 35% of the variation in market rates over that period. This represents a clear break from the past: before the recent cycle, the link between services inflation and market rates was much weaker. Over the three years to 2019 for example, the change in services inflation explained roughly 5% of the variation in market rates – which was a period when services inflation was much lower than post 2022.

Chart 2: Market rates have been strongly correlated with the change in UK services inflation between MPC meetings since 2022

Note: Chart shows coefficient estimates from a set of rolling three-year regressions of three-year swap rate on changes in services inflation using data summarised in windows between MPC meetings. Shaded area is 95% confidence interval.

This finding is robust to the inclusion of a set of other macroeconomic variables as controls, such as headline inflation, the unemployment rate and PMIs. Including the unemployment rate alongside services inflation improves the fit but only slightly. Even including the US one-year swap rate in the same regression – which on its own explains around 60% of the variation in UK rates, given the well-documented close relationship between the two – only slightly reduces the importance of UK services inflation in explaining UK rates.

Our findings might point to a broader international trend towards stronger data responsiveness of market rates. Our analysis suggests that a similar, albeit less strong, relationship also held in the US until recently.

It isn’t obvious that monetary policy should be equally data dependent at all times.

At first glance, it might seem obvious that monetary policy should always be data dependent, and that that financial markets should incorporate this throughout. But that isn’t necessarily the case. Our estimates suggest market perceptions of the MPC’s data dependence were much lower before 2022. And this has some theoretical underpinning. Market responses may reflect that, if a monetary policy maker broadly understands the shocks hitting the economy, and monetary policy can only affect the economy with some lag, the policymaker should respond to the future economic outlook rather than data releases, which are backward-looking. Of course, data releases will often contain some signal about the outlook, but the strength of that signal will vary over time. At a time when big and uncertain shocks are hitting and certainty about the outlook is low, it may make sense to place more weight directly on the data (Bailey (2025) and Haberis et al (2025)).

What happens next?

Markets have perceived the MPC as being very data dependent over recent years. This represents a big change compared to the period before the Covid pandemic. This raises an important question: what will markets look at to determine the UK policy stance going forward? Placing greater weight on real‑time data may be a sensible response to the heightened uncertainty of recent years. That uncertainty does not appear to be going away any time soon, so perhaps the perceived data dependence of monetary policy is here to stay.

Nades Raviraj and Danny Walker work in the Bank’s Monetary and Financial Conditions Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.