One of our recently published articles about enterprise technology systems explained the benefits of using a unified architecture that connects applications, data, and AI agents through an orchestration layer (see “Designing Intelligent Systems – A Unified Blueprint for Applications, Data, AI Agents, and Orchestration”). That blueprint established the foundation for building intelligent systems capable of seamless integration and adaptive workflows.

This follow‑up article focuses on two critical elements within that orchestration layer: observability and the information domain.

• Observability ensures transparency and system health by monitoring interactions across applications, data, and AI agents.
• The information domain provides the contextual backbone – business processes, roles, applications, and infrastructure – that AI agents rely on to plan and execute effectively.

Together, these components enable scalable automation, human-in-the-loop decision-making, and organizational transformation. Following consideration of these two critical components, this article presents a practical example in the (re)insurance domain to illustrate how observability and structured information domains unlock the full potential of multi-agent orchestration.

High-Level Interaction Flow

Observability

Every interaction passing through the orchestration layer – whether involving applications, data, or AI agents – is monitored and traced.

Observability data includes:

• Logs – Records of discrete events such as errors and warnings
• Metrics – Numerical measurements of system performance and health over time (e.g., CPU usage)
• Traces – Records that follow the path of a single request or transaction
• User Feedback – Feedback and support tickets

This comprehensive view of system behavior enables:

• Support teams to diagnose challenges and improve performance
• AI agents to optimize planning and execution paths based on the system’s current state

Information Domain

Observability helps AI agents determine how to optimize execution. But what exactly is being optimized? That’s where the information domain comes in.

The information domain is a repository of:

• Business-critical information
• Supporting processes
• IT systems and infrastructure that enable information flow

In short, the information domain contains details about business processes, roles, applications, integrations, and underlying infrastructure.¹ Its quality directly impacts the effectiveness of AI agents referencing it.

Hypothetical Example – Direct (Re)Insurance Quote-to-Bind Process and Roles

Traditional Workflow:

1. Cedent submits underwriting information via email.
2. Underwriter assistant parses and stores submission in workflow and documentation tools.
3. Underwriter generates a quote using policy administration and rating systems.
4. Cedent accepts or rejects the quote.
5. If accepted, underwriter assistant binds the submission.

Traditional Workflow – Direct (Re)insurance Quote-to-Bind Process and Roles

This process – and its supporting infrastructure – resides in the information domain. When designing multi-agent orchestrations, this domain provides the foundational context for:

• Plan generation – Action sequences based on reasoning, orchestration, and information retrieval
• Action execution – Interactions with external systems via functions

AI Agent Workflow Redesign

1. Orchestration Agent acts as the central coordinator, managing the overall workflow and ensuring seamless interaction between the specialized agents. This agent leverages observability data and the information domain to optimize execution paths and maintain system health.
2. Submission Agent receives the submission, identifies lines of business, and routes to Parsing Agent(s).
3. Parsing Agent extracts underwriting details for assistants, underwriters, and downstream systems.
   • Underwriter assistant reviews and provides feedback via an application.
4. Quote Agent sends data to policy administration, rating, and document generation systems, then prepares a quote with insights (e.g., quote-to-bind ratio).
   • Underwriter reviews and submits quote to cedent.
5. Cedent reviews and accepts.
6. Binding Agent finalizes binding, with underwriter assistant sign‑off.

AI Agent Workflow Redesign – Direct (Re)insurance Quote-to-Bind Process and Roles

As Frank Schmid, Gen Re Chief Technology Officer, noted in his article “The Potential of AI for Process Innovation”: “As the technology matures, adoption will shift from enhancing existing tasks to enabling new ones within redesigned workflows – some of which will be coordinated and executed by AI agents in novel organizational structures.” In other words, workflow redesign delivers higher economic benefit over task-level improvements followed by organizational redesign.

This workflow redesign example illustrates a redesigned intake process:

• Automated submission ingestion replaces manual intake.
• AI agents orchestrate workflows with human-in-the-loop for critical decisions.

At the highest level, organizational redesign amplifies economic benefit. As (re)insurance operations evolve, the role of underwriters and assistants are being redesigned to shift from manual task execution to higher-value responsibilities. Specifically, these roles will evolve to focus on overseeing AI agents and improving business processes – enhancing data, applications, and orchestration.

The Strategic Imperative – Observability and Information Domains

The orchestration layer, powered by observability and anchored by the information domain, forms the backbone of intelligent systems. Observability ensures transparency and adaptability by providing real-time insights into system performance and human interactions. The information domain, in turn, supplies the contextual foundation that enables AI agents to plan, reason, and execute effectively.

Together, these components unlock the full potential of AI‑driven orchestration:

• Enhanced system reliability through continuous monitoring and feedback
• Optimized workflows via informed decision-making
• Scalable system design that integrates human oversight with AI efficiency

Organizations that invest in robust observability and well-structured information domains position themselves to achieve not just incremental improvements, but also transformative gains in operational efficiency and business value.