In financial organisations, recent years have seen unprecedented trading volumes and volatility. Milliseconds dictate millions in value. Reactive AI chatbots are no longer enough, but this is where autonomous agentic AI agents capable of perceiving, deciding, and acting in real time can play a role. 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 every agent working on affected instruments needs the update instantly, not a few minutes later. When a compliance rule changes, every agent making decisions based on that rule must adapt in real time.
Five agentic AI use cases in capital markets
By adopting a governed, real-time foundation, financial institutions are applying agentic AI to several use cases:
- Intelligent portfolio management and trading: Orchestrated AI agents can process millions of trading signals across global markets to optimise execution and manage risk. By coordinating specialised agents for market analysis, risk assessment, and regulatory compliance, financial institutions can improve trade execution speed while reducing transaction costs. To function effectively, these networks require infrastructure capable of routing millions of messages with sub-millisecond latency to maintain synchronised, real-time context.
- Real-time fraud detection and prevention: Financial institutions are deploying orchestrated AI agents to analyse billions of daily transactions, addressing the high false-positive rates of traditional rule-based fraud detection systems. Specialised agents coordinate insights on geographic inconsistencies, spending patterns, and merchant risk profiles to distinguish genuine threats from normal consumer behaviour. Platforms handling billions of transactions require real-time coordination to support effective fraud detection.
- Automated regulatory compliance and reporting: AI systems are helping automate the complexity of global regulatory compliance by tracking rule changes, gathering internal data, and formatting reports across multiple jurisdictions. Instead of relying on manual processes, compliance teams can use specialised agents to support timely submissions while allowing staff to focus on strategic risk management. These workflows require reliable data routing and delivery to adapt to changing regulations and maintain synchronisation across filing schedules.
- Personalised customer experience and financial advisory: Orchestrated AI agents can extend personalised financial advisory services to a broader customer base. By coordinating insights from profile analysis, goal planning, and risk assessment agents, platforms can adjust investment recommendations as a client’s circumstances or market conditions change. Delivering this at scale requires secure data sharing across business units while maintaining privacy and regulatory requirements.
- Intelligent loan processing and credit assessment: Agentic AI can automate loan approval processes by coordinating data verification across internal systems and external agencies. Specialised agents can extract application information, verify income, and assess credit risk, reducing processing times while supporting consistent decision-making. These scenarios require orchestration that routes data efficiently while maintaining compliant audit trails for automated decisions.
AI adoption in capital markets
Thomas Shuster, Research Director, Worldwide Capital Markets, Wealth, and Digital Assets at IDC Financial Insights, has argued 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 continued: “Early generative AI tools improved drafting, summarisation, 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 foundation for agentic AI
Institutions that have successfully adopted AI have not only developed more sophisticated algorithms but also established the infrastructure and practices needed to orchestrate agents at scale.
This begins with six key considerations:
- Adopt real-time data streaming: Capital markets run on live data. Real-time autonomous agents require continuously updated data rather than batch-processed information. A continuous stream of market data, reference data, and trade events allows AI systems to respond as events occur.
- Master multi-agent orchestration: Complex financial workflows often require multiple specialised agents working together. Systems should enable agents to discover one another, coordinate actions, and share context dynamically.
- Embrace open standards: The AI landscape continues to evolve rapidly. Building on open standards such as A2A, MCP, and API-friendly protocols can reduce vendor lock-in, allowing organisations to replace applications or cloud-native AI services without redesigning underlying systems.
- Ensure governance: AI deployment in financial services requires oversight. Organisations should establish policies covering AI decision-making, data usage, and risk management, supported by audit trails, behavioural controls, and frameworks for testing orchestrated agent systems.
- Invest in expertise: Organisations need teams that understand both AI capabilities and the requirements of financial services. IT teams should be able to design orchestration patterns, monitor agent performance, and adapt systems as business needs evolve.
- Build pilots that demonstrate value: Begin with use cases that require agent coordination and have measurable business outcomes. These can help validate an orchestration approach before broader deployment.
Orchestrating AI agents
As capital markets firms increase investment in AI agents, many are focusing on the agents themselves without giving equal attention to orchestration and the underlying connectivity. AI agents require real-time context and coordination to operate effectively across enterprise workflows.
The bridge from agentic AI experimentation to production is effective multi-agent orchestration, which coordinates communication, context sharing, and governance across multiple AI agents.
AI agent orchestration platforms build on event-driven architectures by combining real-time data distribution with networks of autonomous AI agents. While an event mesh routes data dynamically across the enterprise, AI agent orchestration enables agents to reason over, coordinate around, and act on that information.
The next phase of agentic AI in financial services
The financial services industry is expanding the use of AI agents across trading, fraud detection, compliance, customer advisory, and lending.
To support these use cases at scale, institutions need effective orchestration so AI agents can share context, coordinate actions, and respond to changing conditions in real time. This can help organisations improve customer experiences, strengthen risk management, and support more consistent operational outcomes.
