Embedded vs. External: Choosing the Right AI Agent Strategy

AI Agents are an exciting new solution to automate work.  The reality is the technology is new, evolving fast with very few standards.  As media companies think about how they’ll deploy Agents, an important consideration is whether to use external agents, embedded agents or a combination.  System of Record (SoR) applications, such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Order Management (OMS) systems, are critical for managing core business data. Integrating AI agents into these systems can enhance functionality, but the approach—embedded or external—has consequential implications on the value. This blog explores the differences between embedded AI agents (integrated directly into the SoR application) and external AI agents (operating as separate systems interfacing with the SoR), along with their pros, cons, and ideal use cases.

Embedded AI Agents

Embedded AI agents are built directly into the SoR application, leveraging its infrastructure, data models, and user interface to provide seamless AI-driven capabilities.

Pros

• Higher User Adoption: Embedded agents have native access to the application’s context, enabling higher utilization, more accurate and relevant responses.  This means the Agent is there for the user in the flow vs requiring the user to remember to initiate the agent from some external interface limiting adoption.
• Seamless Integration: Embedded agents operate within the SoR’s ecosystem, providing a unified user experience without switching applications assisting the user in the actual workflow.
• Data Access: Direct access to the SoR’s database reduces latency and eliminates the need for external data transfers as well as access to data that may not be exposed via APIs.
• Security and Compliance: Data remains within the SoR’s security, simplifying compliance and auditability.
• Lower Overhead: No need for separate infrastructure, in house Agent skillsets, reducing maintenance and operational costs.

Cons

• Limited Flexibility: If the embedded agents are tightly coupled to the SoR’s architecture, making updates or changes may be dependent on the application or agent’s release cycles .  SoR Agent flexibility will be an important factor over time which will need to be mitigated with agent configurability.

External AI Agents

External AI agents are standalone systems that interact with SoR applications through APIs, webhooks, or other interfaces such as MCP, often hosted on separate infrastructure.

Pros

• Flexibility: External agents can leverage a variety of AI models and be updated independently of the SoR’s constraints.  
• Cross-System Integration: External agents can aggregate data from multiple SoRs or external sources, enabling broader insights.
• Customization: Organizations can choose or develop AI agents tailored to specific needs, avoiding reliance on SoR vendors.

Cons

• SoR Change Dependency:  External Agents incur hidden costs as SoR’s update APIs, reliance on MCP capabilities, change workflows and capabilities that result in ongoing maintenance and enhancements putting pressure on internal teams to maintain them.
• AI Model Compatibility - as LLM models upgrade or get swapped, intensive QA is required to ensure expected results requiring strong governance and change control processes.
• Data Security Risks: Transferring data to external systems raises concerns about privacy, security,auditability and compliance.
• Latency: API calls and data transfers can introduce delays, impacting real-time performance.
• User Experience Fragmentation: External agents may require separate interfaces, often chatbot based without proper context, disrupting workflow continuity and adoption.
• Higher Costs: Separate infrastructure and integration efforts can increase operational expenses.

Which Type Is Best Under Certain Conditions?

When to Use Embedded AI Agents

Embedded AI agents are ideal in scenarios where:

1. Unified User Experience Matters: When users need AI capabilities within the same interface (e.g., revising an IO, researching an account in CRM), embedded agents reduce context-switching leading to high user adoption.  Adoption is the key to value realization.
2. Simplicity Is Prioritized: Organizations with limited technical resources may prefer embedded agents to avoid complex integrations.
3. Vendor Innovation - look for SoR vendors who are investing in replacing manual workflow tasks with agentic workflows intuitively in their UI.
4. Performance Stability Is Key: For SoRs with predictable workloads and sufficient resources, embedded agents ensure low latency and reliability.
5. Compliance Is Critical: When adherence to permissions, auditability and security are important, organizations will benefit from embedded agents to keep data secure especially important for public companies.‍

Example: A media company processes IO revisions where it’s important to operate in a secure environment, have full auditability and adherence to company policies like approvals. The agent operates within the OMS, ensuring compliance in a secure environment with workflow and data context.

When to Use External AI Agents

External AI agents are better suited for:

• Complex AI Workloads: Scenarios requiring advanced AI models (e.g., large language models, predictive analytics or even both combined) and large datasets may benefit from dedicated infrastructure.
• Cross-System Insights: When AI needs to integrate data from multiple SoRs or external sources (e.g., combining CRM and social media data for customer insights).
• Rapid Innovation: Organizations needing frequent AI updates or cutting-edge capabilities can leverage external agents to stay agile.
• Custom Solutions: When the SoR’s native AI lacks specific features, external agents offer tailored functionality.

Example: A media company using an external AI agent to analyze data from a CRM, inventory system, ad servers and external market trends to optimize pricing strategies.

Key Considerations

• Data Sensitivity: If data cannot leave the SoR, embedded agents are non-negotiable. For less sensitive data, external agents offer more flexibility.
• Budget and Resources: Embedded agents may reduce infrastructure costs, while external agents require investment in integration, hosting, QA and ongoing maintenance.  SoR vendors that can provide both embedded and external agents may provide the ultimate flexibility and maximum value.
• Scalability Needs: External agents are better for compute-heavy tasks, while embedded agents suit simpler, context-specific functions.

Conclusion

Neither embedded nor external AI agents are universally superior—it depends on the organization’s needs, constraints, and goals. Embedded agents excel in secure, integrated, and simple use cases, while external agents shine in flexible, scalable, and cross-system scenarios. By assessing data sensitivity, integration complexity, and performance requirements, organizations can choose the approach that best aligns with their operational and strategic objectives.

Boostr’s Point of View

At Boostr we believe in both models and are initially deploying embedded agents for all of the reasons outlined.  To maximize value for our clients, we’re decoupling agents from our traditional release cycles, making them configurable and creating bespoke Agents for clients with very specific needs such as cross-platform data.  This way our clients win in all scenarios and get the maximum benefit from AI Agents.

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