Agentic Finance: Everything You Need to Know

The financial industry has long used algorithmic solutions to assess credit risk, detect fraud, and support investment portfolio management. In recent years, however, rapid advances in neural network-based solutions have gradually expanded the role of artificial intelligence (AI) across financial activities, from analysis and recommendations to autonomous actions within digital systems.

The development of AI agents and their gradual adoption across financial activities are shaping a new field known as agentic finance. CP Media takes a closer look at what agentic finance is, the context in which it’s emerging, and how the field is developing.

What Is Agentic Finance?

A key feature of agentic finance is the combination of reasoning and execution. For example, when asked to allocate available funds, a financial AI agent could potentially assess the relevant inputs, evaluate them against predefined criteria, develop possible courses of action, select appropriate financial instruments, execute a transaction, and verify the outcome.

Financial AI agents are expected to:

• Focus on achieving a specific goal
• Be capable of planning multi-step processes
• Have access to external data and financial services
• Be able to take actions independently within predefined limits

The degree of autonomy can range from preparing a transaction for human approval to executing it independently within established limits.

How Agentic Finance Differs From Traditional Automation

Financial operations have been automated for decades, so not every action performed by software without human involvement qualifies as agentic finance. Traditional automation relies on rigid conditions and predefined rules. For example:

• Automatic payments debit a predetermined amount on a specific date
• A trading algorithm automatically buys or sells an asset when it reaches a specified price
• A robo-advisor can allocate a portfolio according to a fixed risk model

Agentic systems offer much greater flexibility. The user defines the desired outcome, while the AI agent determines how to achieve it based on the context. If conditions change, the AI model can revise its plan, choose a different tool, or request additional information. Google experts note that traditional systems primarily respond to requests or analyze data, while agentic AI is designed to make decisions and take actions autonomously.

AI assistants occupy an intermediate position because they can analyze information and make recommendations, but the final action always remains with the user. Agentic finance begins when a system is given a degree of autonomy to select and execute financial actions in pursuit of a defined goal.

Agentic Finance as Part of the Agentic Economy

Because the terminology in this field is still evolving, it’s important to distinguish between these concepts. The agentic economy is a broader concept, with agentic finance forming one part of it. Agentic finance represents its financial layer and encompasses payments, asset and liquidity management, as well as interactions with banks, payment systems, capital markets, and DeFi services.

Against this backdrop, Google introduced the Agent Payments Protocol (AP2) for agent-initiated payments, Visa began developing its own solutions for agentic financial transactions, and Circle introduced infrastructure for building AI agents. These are only a small fraction of the initiatives underway in this field. Such efforts clearly show that major market participants are beginning to build a financial ecosystem for AI agents and preparing for the emergence of a new category of transaction initiators.

What Financial Agents Can Do in Practice

Potential agentic finance use cases span several segments:

• Payments: selecting a payment method, preparing or initiating a transaction, staying within budget, and monitoring execution
• Commerce and purchasing: searching for offers, comparing terms, and moving from selection to payment
• Asset management: analyzing markets, adjusting portfolios, and executing trades when authorized
• Treasury operations: forecasting cash flows, managing liquidity, and prioritizing payments
• Banking and personal finance: managing budgets, comparing financial products, and handling routine transactions
• Decentralized finance: interacting with programmable protocols for trading, lending, and liquidity provision

Some of these use cases are already being tested experimentally. In 2025, the Bank for International Settlements (BIS) tested the use of a generative AI agent for intraday liquidity management. In simulated conditions, the agentic system independently maintained reserves, prioritized payments, and adapted to uncertainty. Based on the results, the researchers specifically emphasized the need for safeguards and human oversight.

How Agentic Finance Works

At a high level, an agentic financial transaction involves the following key steps:

1. Goal setting. The user or organization defines the desired outcome and constraints.
2. Planning and decision-making. The agent analyzes data and determines the actions required.
3. Authorization. The system verifies the agent’s permissions and whether the transaction is permitted.
4. Execution of the financial action. The agent initiates a payment, trade, or other transaction.
5. Settlement. The transaction is processed through the appropriate financial infrastructure.
6. Outcome verification and feedback. The agent evaluates the outcome and adjusts subsequent actions if necessary.

This closed-loop process allows the agent to account for the consequences of its own actions and continue operating until it achieves the goal or reaches a point where human intervention is required. Specific mechanisms for identity verification, account access, and settlement form a separate infrastructure layer of agentic finance.

Opportunities and Limitations of Agentic Finance

The economic impact of adopting agentic finance stems from shortening the path between decision and action. An agent can operate around the clock, process large volumes of data, and tailor decisions to a user’s goals. For financial institutions, this could mean lower operating costs and greater personalization.

Consumer interest is already evident, although a lack of trust remains one of the barriers to broader adoption. In an FCA study published in the summer of 2026, 20% of surveyed U.K. consumers, or around 11 million adults, said they would be highly likely to use AI agents capable of acting autonomously within predefined goals.

At the same time, respondents expressed concerns about control over such systems because the cost of errors in financial matters can be particularly high. An incorrect response from a model can be corrected, while an erroneous trade, transfer, or investment decision can result in a direct financial loss. The development of agentic finance therefore depends on model reliability, security, clearly defined permissions, fraud prevention, and a clear allocation of responsibility.

Additional risks arise at the systemic level. The Financial Stability Board (FSB) identifies reliance on third-party providers, increased correlation in market participants’ behavior, cyber threats, and risks stemming from errors and unpredictable behavior by AI models themselves. As autonomy increases, similarly configured agents may respond to the same signal in similar ways, while a failure in a widely used technology component could affect many organizations at once.

Where Agentic Finance Is Headed

Agentic finance is still taking shape. According to data from a joint study by the Bank of England and the U.K. Financial Conduct Authority (FCA), 75% of surveyed financial firms were already using AI tools in 2024, but only 2% of reported use cases were fully autonomous.

Meanwhile, according to a Cambridge Centre for Alternative Finance report published in April 2026, 52% of traditional financial institutions are already testing or deploying AI agents. Of those, 29% are at the pilot stage, while another 23% are at more advanced stages of deployment. FinTech companies are moving faster, with the corresponding figures at 57% and 45%.

The most likely trajectory is a gradual expansion of delegated authority. Systems first analyze and advise, then prepare transactions, gain the ability to execute them with user approval, and ultimately act independently within predefined limits. The FCA considers this same gradual model, from an assistive tool to autonomous agents operating within agreed boundaries, in the study cited above.

The main shift in agentic finance involves a change in how people interact with the financial system. Instead of completing a sequence of steps themselves, users will increasingly be able to define a goal and set conditions, leaving execution to a software agent. The scale of this transition will depend on whether the industry can clearly define the boundaries of agent authority, establish reliable authorization mechanisms, and determine responsibility for outcomes.