We are quickly entering an era where AI agents are executing B2B payments, moving capital, settling transactions, and managing treasury operations autonomously.
While the use cases are exciting (think treasury rebalancing, liquidity management, and cross-border payment routing), there’s a gap that the industry is just now starting to uncover: these agents need to be able to move value, not just process information.
That’s where AI infrastructure solutions enter the conversation.
What AI agents need that LLMs don’t
Building AI agents for enterprise finance is fundamentally different from building conversational AI. Language models reason and generate text. Financial AI agents need to execute, and execution in finance means touching real assets under real regulatory constraints.
The AI financial infrastructure gap shows up in three primary places.
Transaction authorization and control
AI agents can’t operate with static API keys or credentials that grant blanket access. They need dynamic, policy-governed authorization that adjusts based on transaction type, counterparty, amount, and market conditions. A global PSP’s treasury agent moving $10M between accounts needs different authorization than one executing a $50K supplier payment.
Multi-party orchestration
Global financial workflows involve multiple institutions, payment rails, and counterparties. An AI agent initiating a B2B cross-border payment doesn’t just call a single API. It needs to:
- coordinate liquidity across venues
- route through compliant rails
- settle with counterparties
- reconcile across ledgers
The agent’s intelligence is only as good as the agentic infrastructure it’s built on top of.
Auditability and compliance
AI decision-making needs to be traceable. Not just for basic debugging, but for regulatory compliance. When an AI agent rebalances treasury positions or routes a payment, every decision point needs to be traceable, explainable, and auditable.
The AI infrastructure solutions conversation today focuses on model performance, training costs, and inference speed which all matters for LLM deployments. But for AI agents in finance, the constraint lies in whether your infrastructure can support policy-governed, multi-party, auditable transaction execution at scale.
Why financial services is different
Most discussions about AI infrastructure assume the agent’s job is to generate outputs: text, images, recommendations. In financial services, the output is the transaction. And transactions in finance come with constraints that don’t exist in other domains.
Regulatory requirements
AI agents operating in payments need to comply with AML/KYC, sanctions screening, and travel rule requirements. An agent routing stablecoin payments across borders for a remittance company isn’t just optimizing for speed and cost, but also ensuring every transaction meets regulatory thresholds in multiple jurisdictions. That compliance layer needs to be embedded in the AI-powered financial infrastructure itself, not as a model feature.
Counterparty risk management
AI agents making liquidity decisions or executing trades need real-time access to counterparty data, settlement windows, and credit limits. This is a dynamic state (not a static configuration) that changes based on market conditions, exposure limits, and collateral requirements. The infrastructure layer needs to surface this state to agents in a way that’s consumable and actionable.
Asset custody and security
AI agents in institutional finance don’t just read data, they move assets. That means they need access to custodial infrastructure with cryptographic controls, transaction signing, and security policies. An agent managing a bank’s stablecoin treasury operations needs the ability to initiate transfers while respecting organizational policies around transaction limits, approval workflows, and security thresholds. That requires agentic infrastructure purpose-built for digital asset operations, not generic cloud APIs.
If you’re building AI agents for finance, you’re building intelligent systems that need to operate within highly regulated, multi-party, asset-custodial environments. The infrastructure layer is what makes that possible.
The infrastructure stack for agentic finance
The AI infrastructure solutions required for agentic finance aren’t just a single tool. It’s a composable stack that connects AI decision-making with financial execution capabilities.
- Security outside the agent: Agents operate at a scale and autonomy that humans don’t. Every action they take (be it a transfer, a contract call, or a payment) must be governed. This requires security that lives outside the agent, with defense-in-depth architecture for no single point of private key compromise. Key shares need to be distributed across environments, never exposed during signing, and never accessible to the agent or any single party.
- Policy and governance layer: AI agents need to operate under dynamic authorization rules. A policy engine that defines what agents can do is foundational.
- Transaction execution and wallet infrastructure: In order for AI agents to initiate payments, move assets between accounts, interact with DeFi protocols, or settle with counterparties, you need wallet infrastructure that’s API-first, supports multiple blockchains and asset types, and integrates with both traditional financial rails and digital asset networks.
- Network and liquidity access: AI agents optimizing payment routing or treasury operations need access to liquidity across centralized exchanges, OTC desks, DeFi protocols, and cross-border payment networks. Through a single network connection, agents can route transactions intelligently without building point-to-point integrations with every counterparty.
- Compliance and reporting: Ensuring agent-initiated transactions go through the same compliance workflows as human-initiated ones means integrating with sanctions screening, transaction monitoring, and travel rule compliance systems. These transactions also require reconciliation and reporting for auditability as with any other financial transaction.
Most companies building AI financial infrastructure for finance are starting from the intelligence layer and working backward. The ones succeeding are starting from the transaction execution layer and working forward to build this end-to-end stack.
What we’re building towards with agentic finance
If you’re evaluating AI infrastructure solutions for financial services, the question you should be asking is “can the model operate in production under real financial constraints?” That requires purpose-built agentic infrastructure, not general-purpose cloud APIs.
The next generation of B2B financial services will be built on AI agents. But those agents will only be as capable as the infrastructure they run on. An execution layer that makes agentic finance possible.
Learn more about Fireblocks’ Agentic Digital Asset Infrastructure, and download the Dynamic report on agentic finance and stablecoins.
