In financial services, the rapid rise of agentic AI is a wake-up call for every company to seize the unprecedented opportunities it presents: to analyze live data at sub-millisecond speeds, to process billions of dollars in transactions, protect customers from fraud, ensure regulatory compliance, personalize advice for millions of clients, and slash loan processing times from weeks to hours. And the opportunities go on!
Himanshu Gupta, Senior Architect, Capital Markets, Solace, identifies five key business use cases where properly orchestrated AI agents are delivering impact and ROI for frontier financial services firms. But exploiting this potential requires more than just deploying intelligent AI models, he explains the infrastructure behind how they do it.
In financial organizations, recent years have seen unprecedented trading volumes and volatility. Milliseconds dictate millions in value. Reactive AI chatbots are now not enough, but it’s where autonomous agentic AI agents capable of perceiving, deciding, and acting in real time, excel. They can interpret complex objectives, break them down into sub-tasks, and execute multi-step workflows.
But for this coordination to work, agents must share a continuously updated view of what’s happening across the financial enterprise. When one agent updates a position, all related agents need that context immediately. In the financial world, market conditions continually shift, so that means every agent working on affected instruments needs the update not in a few minutes but instantly. When a compliance rule changes, every agent making decisions based on that rule must adapt in real-time.
The high-five agentic AI use cases transforming capital markets
By adopting this governed, real-time foundation, financial institutions are already unlocking high-value use cases that slash operational costs and drive revenue:
Intelligent portfolio management & trading: Orchestrated AI agents are revolutionizing portfolio management by rapidly processing millions of trading signals across global markets to optimize execution and manage risk. By coordinating specialized agents for market analysis, risk assessment, and regulatory compliance, institutions like JPMorgan Chase have significantly improved trade execution speed and reduced transaction costs. To function effectively, these networks require high-performance infrastructure capable of routing millions of messages with sub-millisecond latency to maintain synchronized, real-time context.
Real-time fraud detection & prevention: Financial institutions are deploying orchestrated AI agents to analyze billions of daily transactions, overcoming the high false-positive rates of traditional rule-based fraud detection systems. Specialized agents instantly coordinate insights on geographic inconsistencies, spending patterns, and merchant risk profiles to accurately distinguish genuine threats from normal consumer behavior. HSBC partnered with Google to co-develop Dynamic Risk Assessment, an AI system that enabled them to identify 2-4 times more financial crimes with significantly improved accuracy. The system also reduced false positives by 60%. An orchestration platform that can handle billions of transactions while maintaining the real-time coordination will separate effective fraud detection from systems that are either too permissive or too restrictive.
Automated regulatory compliance & reporting: AI systems are automating the staggering complexity of global regulatory compliance by dynamically tracking rule changes, gathering internal data, and formatting reports across various jurisdictions. Instead of relying on error-prone manual processes, compliance teams use networks of specialized agents to ensure accurate, timely submissions while shifting human focus to strategic risk management. Orchestrating these dynamic workflows requires intelligent data routing and guaranteed delivery to seamlessly adapt to evolving regulations and maintain perfect synchronization across different filing schedules.
Personalized customer experience & financial advisory: Orchestrated AI agents are democratizing financial advisory services by scaling highly personalized wealth management to everyday consumers, not just high-net-worth individuals. By coordinating insights from profile analysis, goal planning, and risk assessment agents, platforms can dynamically adjust investment recommendations the moment a client’s life circumstances or market conditions change. Delivering this tailored experience at scale demands secure data sharing across business units and the ability to compose dynamic agent workflows while strictly maintaining privacy and regulatory boundaries.
Intelligent loan processing & credit assessment: Agentic AI is transforming the notoriously slow and manual loan approval experience by intelligently coordinating data verification across multiple internal systems and external agencies. Specialized agents handle everything from extracting application information to verifying income and assessing credit risk, drastically reducing processing times and operational costs while improving approval rates. To support these diverse lending scenarios seamlessly, financial institutions require dynamic orchestration infrastructure that routes data efficiently while maintaining perfect, compliant audit trails for every automated decision.
The AI Advantage for capital markets Frontier Firms
In a recent guest post for Microsoft, Thomas Shuster, Research Director, Worldwide Capital Markets, Wealth, and Digital Assets, IDC Financial Insights highlighted that some “frontier firms” are leading the way in agentic AI adoption in capital markets, stating: “It is less about being first to experiment with new tools and more about translating AI investment into measurable, repeatable operating gains.”
He continues: “Early generative AI tools improved drafting, summarization, and search. These capabilities were helpful but not transformative or differentiated. The step change occurs when firms shift from task acceleration to workflow redesign, deploying AI agents to execute multistep processes across systems under bounded human oversight.”
Building the architectural foundation for AI success in capital markets
Institutions that have successfully leveraged AI have not only developed more sophisticated algorithms but also established the necessary infrastructure and adopted practices that facilitate the orchestration of agents at a large scale.
This begins by laying a robust foundation built on six key pillars:
Adopt real-time data streaming: Capital markets run on live data. You cannot fuel real-time autonomous agents with stale, batch-processed data. Establishing a continuous, real-time, contextual stream of data ensures that market data, reference data, and trade events flow continuously to the AI systems the moment they occur.
Master multi-agent orchestration: Complex financial workflows are fragile. Even a single task can require multiple specialized agents working together. Systems need to be designed where agents can discover each other, coordinate their actions, and share context dynamically.
Embrace open standards: The AI landscape is evolving at a breakneck pace. Building on open standards such as A2A or MCP and API-friendly protocols prevents vendor lock-in, allowing firms to swap out applications or cloud-native AI services as better technology emerges without having to re-architect their systems.
Ensure governance: End-to-end autonomy in finance requires strict oversight. Establish clear policies for AI decision-making, data usage, and risk management before deployment. This includes audit trails showing how agents coordinated to make decisions, controls for managing agent behavior, and frameworks for testing orchestrated agent system.
Invest in experts: Success requires teams that understand both the capabilities and limitations of AI along with the requirements of the financial services space. IT teams need to be able to design orchestration patterns, monitor agent performance, and adapt systems as business needs evolve.
Build pilots that prove orchestration value: Begin with use cases that demonstrate clear ROI and require agent coordination, and use them to validate your orchestration approach, then scale using infrastructure that can support enterprise-wide deployment as your agent network grows.
The tech it takes to succeed: enter the Agent Mesh
As capital markets firms rush to leverage AI agents, many are investing aggressively in the agents without respecting how much orchestration and underlying connectivity matters. You can build the smartest AI agents in the world, but without real-time context and orchestration, they’re just isolated islands of intelligence!
The bridge from agentic AI experimentation to full-scale production is the Agent Mesh. An Agent Mesh makes it easy to build, deploy, and orchestrate AI agents by handling all of the complex communication, orchestration, and governance.
An Agent Mesh platform builds upon the proven scalability of an Event Mesh by combining real-time, event-driven data distribution with a network of autonomous AI agents. While an Event Mesh routes data dynamically across the enterprise, the Agent Mesh introduces intelligent agents that can autonomously reason about, and act on, this live information flow.
The agentic AI era of financial services is here
The financial services industry has crossed a threshold, but institutions must orchestrate AI agents across trading, fraud detection, compliance, customer advisory, and lending to fully open the Agentic AI door.
These institutions are delivering better experiences, managing risk more effectively, and driving innovation that competitors can’t match.
Himanshu Gupta is Senior Architect, Capital Markets at Solace
As one of Solace’s solutions architects, Himanshu Gupta is an expert in many areas of event-driven architecture, and specializes in the design of systems that capture, store, and analyze market data in the capital markets and financial services sectors. This expertise and specialization are based on years of experience working at both buy- and sell-side firms as a tick data developer, where he worked with popular time series databases kdb+ and OneTick to store and analyze real-time and historical financial market data across asset classes.
In addition to writing blog posts for Solace, Himanshu publishes two blogs of his own: enlist[q] focused on time series data analysis, and abitdeployed.com, which is about general technology and latest trends. He has also published code samples at GitHub and kdb+ tutorials on YouTube!
Himanshu holds a Bachelor of Science degree in electrical engineering from City College at City University of New York. When he’s not designing real-time market data systems, he enjoys watching movies, writing, investing, and tinkering with the latest technologies.
TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.
Why ASEAN’s most dangerous work should no longer be done by humans