Artificial intelligence is moving beyond experimentation and into daily operations, especially in regulated industries like wealth and asset management. As firms look to automate complex processes without compromising compliance or control, a practical question emerges: how to build AI agents that are reliable, transparent, and aligned with regulatory standards?

In the financial services industry, AI agents are not generic chatbots. They are purpose-built digital workers designed to execute specific tasks such as monitoring portfolios, triggering compliance checks, managing entities, or supporting client communication. This article explains how AI agents can be built, created, used, and governed in a professional context, drawing on the approach used by Performativ and its AI AgentKit framework.

The Role of AI Agents in Modern Financial Platforms

Before exploring implementation, it’s important to clarify what AI agents actually do in a regulated setting. AI agents operate within defined boundaries, executing tasks based on rules, permissions, and real-time data rather than free-form decision-making.

In wealth and asset management, AI agents are commonly used to:

• Monitor portfolios and flag deviations or risks
• Automate operational Entities
• Support compliance and audit processes
• Assist advisors with insights and reporting
• Reduce repetitive manual tasks

Unlike standalone AI tools, agents embedded within a platform operate directly on trusted data sources and follow governance rules set by the organization.

How to Create AI Agents for Professional Use

Understanding how to create AI agents starts with defining purpose before technology. In regulated industries, successful agents are task-specific, auditable, and predictable.

Step 1: Define the Use Case Clearly

The most effective AI agents solve narrow, well-defined problems. Examples include:

• Monitoring portfolio allocation thresholds
• Detecting compliance triggers
• Preparing draft reports or summaries
• Tracking operational tasks or alerts

Clear scope reduces risk and ensures the agent operates within acceptable boundaries.

Step 2: Connect Agents to Trusted Data

AI agents must work with accurate, consolidated data. In Performativ’s ecosystem, agents operate on unified portfolio, transaction, and compliance data rather than external or unverified sources.

This ensures:

• Consistent outputs
• Reduced data discrepancies
• Full traceability of actions

Step 3: Apply Governance and Permissions

Professional-grade AI agents require strict governance. This includes:

• Role-based access controls
• Defined permissions for actions and data visibility
• Clear logging of agent activity

Performativ’s AgentKit framework is designed specifically to allow firms to design, govern, and deploy AI agents while maintaining compliance.

How to Use AI Agents Safely and Effectively

Knowing how to use AI agents is just as important as building them. In the financial services industry, agents should augment professionals, not replace accountability.

Human-in-the-Loop Design

AI agents work best when humans retain oversight. Common models include:

• Agents that generate recommendations for approval
• Alerts that require human confirmation
• Automated tasks with audit trails

This approach ensures regulatory confidence while still delivering efficiency gains.

Embedded Within Existing Entities

AI agents should integrate into existing systems rather than operate as isolated tools. When embedded in a wealth management platform, agents can:

• Trigger actions based on real-time events
• Communicate with advisors through dashboards or notifications
• Operate consistently across portfolios and clients

This reduces friction and increases adoption.

How to Make AI Agents That Scale Across Teams

To understand how to make AI agents scalable, it’s essential to focus on architecture and extensibility rather than one-off automation.

Modular Agent Design

Scalable agents are built as modular components that can be:

• Reused across teams
• Configured for different Entities
• Updated without disrupting operations

Performativ’s AgentKit Builder supports this modular approach, allowing firms to deploy multiple agents across departments while maintaining centralized governance.

Platform-Level Integration

AI agents scale more effectively when they are part of a broader platform that already handles:

• Portfolio management
• Compliance
• Reporting
• Client communication

This avoids duplicated logic and ensures agents operate on consistent data.

AI Agents in Wealth and Asset Management

AI agents are particularly powerful in environments where complexity and regulation intersect. In wealth and asset management, agents support professionals by reducing manual effort while improving accuracy.

Portfolio Monitoring and Alerts

Agents can continuously monitor portfolios for:

• Allocation drift
• Liquidity risks
• Performance anomalies

Rather than reacting manually, teams receive proactive notifications supported by data.

Compliance and Audit Support

AI agents help:

• Track regulatory requirements
• Log actions and decisions
• Maintain immutable audit trails

This is especially valuable under frameworks such as MiFID II, ESG reporting, and DORA.

Operational Efficiency

Agents automate repetitive Entities such as:

• Task tracking
• Data validation
• Report preparation

This frees professionals to focus on client strategy and decision-making.

Governance, Security, and Compliance by Design

One of the biggest misconceptions about AI agents is that they reduce control. In reality, when built correctly, they increase transparency.

Professional AI agents should include:

• Full activity logs
• Clear decision logic
• Controlled deployment environments

Performativ’s approach emphasizes compliant AI agents are governed, auditable, and embedded within secure infrastructure rather than operating independently.

The Performativ Approach to AI Agents

Performativ’s AI Agents and AgentKit Builder are designed specifically for regulated financial environments. Rather than offering generic AI tools, Performativ enables firms to:

• Design custom AI agents aligned with internal Entities
• Govern agent behavior through permissions and controls
• Deploy agents within the same platform used for portfolio management, reporting, and compliance

This ensures that AI adoption enhances existing operations rather than creating new silos or risks.

More details about Performativ’s AI agent framework can be explored here:
https://www.performativ.com/ai-agents

Common Mistakes to Avoid When Building AI Agents

When exploring how to build AI agents, organizations often encounter similar pitfalls:

• Starting with overly broad use cases
• Allowing agents to operate without clear governance
• Treating AI as a replacement rather than a support tool
• Deploying agents outside core systems

Avoiding these mistakes helps ensure long-term success and regulatory confidence.

Future Outlook: AI Agents as Standard Infrastructure

AI agents are rapidly becoming part of standard infrastructure rather than experimental add-ons. As platforms mature, agents will increasingly:

• Support real-time decision-making
• Enhance regulatory resilience
• Improve scalability without increasing headcount

For firms that build AI agents responsibly today, the long-term benefits extend far beyond automation.

Final Thoughts

Understanding how to build AI agents effectively requires more than technical knowledge. It requires a platform-first mindset focused on governance, integration, and trust.

In regulated industries like wealth and asset management, AI agents succeed when they are embedded within secure platforms, operate on unified data, and support, not replace human expertise. Performativ’s approach demonstrates how AI agents can be built responsibly, delivering efficiency and insight while maintaining the highest standards of compliance and control.

As AI continues to evolve, firms that invest in structured, governed agent frameworks will be best positioned to scale with confidence.

‍