Managing critical capital markets infrastructure means navigating a relentless stream of change from trading venues, data vendors, and platform providers. It is a process that cannot be ignored: missing even a single update risks operational disruption or regulatory exposure.
Firms have long relied on manual triage: key resource sifting through PDFs, emails, and portals to work out what’s changed and who needs to act. It’s slow, inconsistent, and increasingly unsustainable as venue complexity increases, trading hours expand, and regulatory expectations rise.
It's no surprise many firms have considered whether artificial intelligence could help. The idea of using AI to process notices, identify changes, and free up human capacity is appealing. In fact, most capital markets firms already have multiple generative and agentic AI initiatives underway, focused on transforming client engagement, product development, or competitive positioning. Yet when it comes to trading and market data operations, and particularly the management of mandatory change, these initiatives rarely get the attention needed to move from idea to implementation. The result is that while AI remains a promising concept for this domain, it is often seen as a hobby project or a proof-of-concept, rather than a production-ready solution.
The experiments that have been attempted in this space have produced mixed results. The problem is not that the technology lacks capability. Rather, it is that generic, horizontal AI platforms, no matter how powerful, are poorly suited to such specialised, domain-specific challenges. Parsing and interpreting notices from exchanges and vendors is not the same as summarising a news article or drafting an email. The knowledge required is niche, the workflows are complex, and the impact of mistakes are significant.
The projects that succeed take a very different approach. Instead of aiming for broad autonomy, they apply agentic AI within structured, specific workflows built for scale. In this model, agents are not designed to replace human operators with a single general model. They are designed as specialised digital colleagues, each with a narrow remit, working within a clear process framework where every decision is observable and traceable. AI in this context is not used for free-form improvisation. It is used for judgement inside well-defined boundaries. Crucially, these agents are not asked to invent knowledge of the domain. They are given the right context, in the form of schemas, ontologies, subscriptions, and policies, so they have a foundation for reasoning that is both verifiable and controlled.
This approach is particularly powerful in the world of mandatory market change. Hundreds of notices arrive daily, in multiple unstructured formats. Deciding whether any given change is material requires not just subject matter expertise but also an understanding of the firm’s specific business context, such as what protocols it uses, which data sets it subscribes to, how its systems are configured, and what rules apply. This is precisely the type of work where agentic AI excels. Agents can consume large volumes of unstructured input, apply firm-specific rules, and orchestrate structured responses. Human experts are freed from repetitive tasks and can instead focus on the complex edge cases that benefit greatly from their oversight and experience.
At the heart of a successful solution is an agentic framework designed for accountability. The work is broken into a set of agents, each responsible for a specific part of the process. One agent might convert unstructured emails into structured notices. Another might validate which notices are relevant. A third might determine the impact and trigger downstream updates. By limiting the scope of each agent, the workflow remains auditable and manageable. Every action can be traced, and the overall process feels less like a black box and more like an assembly line - transparent, consistent, and governed.
Agents, however, are only as effective as the context they are given. Asking an agent to infer the details of a market data feed change on its own is no more effective than hiring a new staff member and expecting them to learn everything without guidance. Just like human colleagues, agents need structured onboarding. They must be given access to the knowledge that matters, the definitions, priorities, subscriptions, and constraints that shape decision-making. By grounding agents with this context, the risk of hallucination is dramatically reduced and agents become reliable and safe at scale. Agents reason over known inputs, rather than inferred knowledge, and decisions are anchored to verifiable data and schemas.
Personalisation builds on this foundation. Every firm has its own definition of what counts as a material change. A notice that is irrelevant to one organisation may be critical to another. By capturing these rules within the workflow, agentic AI can filter out the noise and flag only those changes that matter. With memory and feedback, the system adapts over time, learning the firm’s profile and further refining its ability to distinguish between material and non-material updates.
Integration is the final piece of the puzzle. For agentic AI to succeed, it must enhance the workflows that already exist rather than attempt to replace them with generic natural language prompts. That means agents that can query data subscriptions, user entitlements, or application inventories, and update inventories or raise tickets in platforms like JIRA or ServiceNow to trigger downstream tasks in the same way a human would. The fast-evolving Model Context Protocol (MCP) make this far easier by allowing agents to connect to internal and external ticketing, permissioning, and configuration systems through a common interface. Instead of building bespoke integrations for each system, MCP lets agents call multiple sources consistently, stitching together a holistic picture of context and impact.
This interplay between agents, context, and integrations changes the role of automation in trading and market data operations. Models are no longer treated as oracles, generating answers in a vacuum. Instead, agents operate as structured participants in the workflow: parsing unstructured inputs, checking state against internal systems, planning and executing next steps, and escalating to humans when ambiguity arises. It is a division of labour that aligns naturally with the way resilient operations are designed - repeatable work handled by machines, exceptional cases handled by people.
By combining agentic AI with robust context engineering and interoperability, firms can address the long-standing problem of mandatory market change. Instead of expending valuable expert hours on repetitive manual work, resources can be redirected to higher-value activities. Instead of treating every notice as a fire drill, change can be managed as a continuous, structured process. Instead of experimenting with generic AI that lacks domain specificity, firms can deploy workflows designed for the real needs of capital markets infrastructure.
As the AI hype cycle settles, the winners will be those who deploy practical, auditable solutions that deliver measurable outcomes. At AiMi, our platform is built specifically for trading and market data operations. It transforms managing mandatory change from a manual, resource-intensive overhead into a scalable and controlled workflow. By combining domain-educated agents with enterprise-grade controls, AiMi transforms market change management from a reactive, manual process into an intelligent, continuously governed workflow. Our platform is already helping firms address these challenges, adapting to each organisation’s unique requirements while delivering immediate value. For teams still manually reviewing notices and tracking changes, learn more about how we can help.