Agentic workflows promise to turn time-consuming operational tasks into a stream of nearly real-time moments. To take one example, much of the cost of reconciling mismatches (transactions, prices, valuations) comes from navigation, filtering, and assembling evidence. Professionals know what to do. When the right data shows up quickly in front of them, they can handle the complex aspects of each task.

But designing a single agent for investment operations, let alone a system of interconnected agents, depends on getting three pillars right: effective controls, accuracy and predictability, and a user experience (UX) that eliminates the need for separate training.

These pillars are essential for agentic AI to deliver on its promise in investment management. Investment managers and the firms that support them should only embrace agentic AI when they have designed and built for control.

Note on terminology:

An agent completes one or a small number of discrete tasks.
An agentic workflow orchestrates the activities of agents into a larger result, with or without further user input.
Agentic AI refers to the overall category of technology.

Controls: making execution safe

Controls prevent agents from completing tasks or seeing data that they shouldn’t. Without them, agents would have unfettered access to your data, your client data, and to actions with millions or billions of dollars in consequences.

Building for control means weaving six main principles into each design pattern.

Build in a sandbox

Agentic workflows belong inside an enterprise sandbox that is designed to meet customer data security expectations. In that sandbox, customer data remains separate from training, and users retain control over how the system operates on live data.

Keep agents specialized

That foundation makes orchestration practical: Instead of expecting one agent to complete an end-to-end process, you coordinate specialist agents across the workflow, each with a specific task.

Emphasize structured responses

The conversational approach to generative AI is an inherent part of its success. Yet chat-based interactions are ill-suited to questions about investment operations data. Without structured control, chat tends to tell a story and keep the conversation going, rather than retrieving the specific data a workflow needs. With structured control and an exact response format, teams can build agents that return an exact response from specific fields in a predictable format.

Create “single tenant” agents

Agents need to be single-tenant, one client at a time. That design avoids cross-client bleed when a user has access to multiple clients. It prevents a scenario where an agent response or action pulls live data from one client while answering a request about another.

Audit everything

In high-stakes workflows, each step matters. Full auditability of what the agent does, step by step, including the decisions it makes and the tools it uses, makes the workflow reviewable and accountable, rather than relying on “because the model said so.” Human accountability is non-negotiable.

Create and enforce guardrails

When each agent has a specific job, that job should stay bounded even when a user tries to push it elsewhere. This layer validates the user’s input and the agent’s response, then matches them to that objective. When there is a mismatch, the system redirects the user toward what the agent can safely do, keeping the agent aligned with its stated purpose.