Unlocking agentic AI at scale in capital markets operations

AI tools are now widely used across capital markets operations. LLMs are embedded in trading and compliance workflows, and hyperscalers are offering tailored AI infrastructure to major financial institutions.

Across the industry, the conversation is moving from AI experimentation to practical, day‑to‑day use. Many firms are discovering that the challenge isn’t access to models, but putting the right foundations, controls, and accountability around them.

In capital markets operations, even small decisions can impact settlement, reporting, and disclosures. That means how AI is built and deployed matters just as much as the model itself. Firms scaling automation successfully are clear about where agentic AI belongs in the trade lifecycle, and how to deploy it responsibly.

A strong governance model, combined with deep industry expertise, is what separates a smart demo from trusted, production-grade automation.

**Where agentic AI doesn’t fit **

One of the most important decisions in capital markets automation is one that rarely makes the headlines: distinguishing between work that is deterministic, and work that requires genuine interpretation.

Much of the trade lifecycle is rules-based. In reconciliations, for example, comparing positions across custodians, applying tolerance bands, flagging breaks, and routing exceptions all follow fixed logic. So do portions of tax calculations, settlement lifecycle management, and regulatory reporting under frameworks like EMIR and SFTR.

For these tasks, the same inputs should always produce the same outputs. That consistency makes them well-suited to automation based on predefined rules or logic, in line with what regulators and clients expect. Introducing probabilistic AI into these steps can add unnecessary variation and weaken transparency. Where certainty is required, probability is not an upgrade.

**Agentic AI becomes powerful when predictability breaks down **

Standard Settlement Instructions (SSIs), for example, often arrive in different formats, such as free-text emails, PDFs, or embedded in confirmation documents, and often lack key details, such as which asset class they apply to or whether it’s a temporary exception. The same is true for trade confirmations, which rarely follow a single standard. Key terms may appear differently across counterparties, asset classes, or document formats. One confirmation might include fees and rates in free-form text, and another in tables.

This requires contextual understanding, not fixed rules, which is where agentic AI shines. With the ability to ingest and interpret vast volumes of unstructured data, it can reduce settlement risk and manual work in ways that rules-based automation cannot.

More broadly, when used within tightly defined guardrails, agentic AI is highly effective at interpreting unstructured inputs, assessing its own confidence, and then determining next steps. In low‑risk scenarios, such as where an exception is caused by a known formatting or data variation, an agent can validate the data, update downstream systems, and close an exception end‑to‑end. Where confidence is lower or risk higher, the same agent can provide a recommendation, explain the reasoning behind it, and pass it to a human for review, with a full audit trail.

**Governance built for AI  **

Regulatory expectations around oversight, transparency, and explainability are becoming clearer. Firms are being asked not just whether they use AI, but how decisions are monitored, controlled, and validated.

As agentic AI moves from pilot to production, the orchestration layer becomes essential. This connects AI models with rules engines, governance checks, human-in-the-loop processes, and data foundations built for capital markets operations.

Frontier AI vendors are building increasingly sophisticated general-purpose models. But their value in real operations environments depends on what surrounds them: the rules engines that validate AI outputs against standing instructions and internal policies; the confidence thresholds that decide if a task is processed automatically or escalates to a human; and the audit trails that record every decision, AI or human, and provide complete visibility of how AI models and tools behave. Firms also need clear risk‑management processes that span both product and business functions, and data‑protection safeguards that are built into every deployment from day one.

KPMG’s research points to 2026 as the year of governed, monitored, and integrated AI agents operating at scale. The firms best positioned for this shift are implementing a robust orchestration and governance framework that meets the demands of the industry they operate in. This layer must be designed in from the start to provide the level of traceability expected in regulated, client-facing environments.

This is what differentiates AI that a regulator, client, or internal audit function can trust, and AI that is limited to low-stakes tasks.

**The operating model that scales **

Industry leaders are moving towards a flexible operating model where rules-based automation handles the predictable, high-volume parts of the trade lifecycle, and agentic AI handles the more ambiguous, interpretive tasks.

Importantly, clean and validated data must underpin both, with human oversight applied where confidence or risk demands it. Neither rules engines nor AI models can perform reliably on incomplete or poor-quality data. In capital markets, there is little margin for error – mistakes that flow downstream can carry very real reputational, financial, and regulatory consequences.

The technology to create this architecture exists today. The differentiator is no longer access to capable AI models. What sets firms apart is the discipline, domain expertise, and infrastructure required to deploy them responsibly, at scale, and in a way that fits real-world scenarios in capital markets operations.