Matching agent capabilities to endpoint adjudication tasks
Before examining specific applications of AI agents in clinical endpoint adjudication, it is important to revisit the different types of AI agents and their defining characteristics. Understanding these distinctions helps determine which agents can realistically support adjudication tasks and which introduce unnecessary complexity or risk.
This article builds on the definitions introduced earlier in this series and focuses on aligning agent capabilities with adjudication roles and responsibilities, rather than on technical considerations alone.
Understanding Agent Capabilities in Practical Terms
As described in the first article of this series, AI agents can be classified by increasing levels of complexity and autonomy. The main categories include simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. These are typically single-agent systems, in which one agent performs a task independently.
At one end of the spectrum, reflex agents respond to predefined signals or conditions. At the other end, learning agents operate in dynamic environments, adapting their behaviour based on experience rather than fixed rules.
Multiple agents of different types can also be combined into multi-agent systems, in which agents interact, coordinate, or divide responsibilities. Such systems may be composed of agents with similar roles or with clearly differentiated functions.
Another useful distinction relates to how agents operate:
- Reactive agents respond to immediate stimuli
- Proactive agents anticipate future states and plan actions
- Rational agents select actions to maximise expected outcomes based on available information
These characteristics are not mutually exclusive and can be combined depending on the design and intended use of the agent.
For endpoint adjudication, these distinctions matter because they directly affect predictability, oversight and suitability for regulated workflows.
Automation, AI Agents, and Incremental Progress
Simple reactive and model-based agents closely resemble automation and semi-automation features already present in many clinical systems. Electronic data capture systems, for example, routinely perform real-time data checks, trigger queries for unusual values, and open or close forms based on entered data. These functions have significantly reduced data discrepancies by preventing errors at the point of entry.
Such examples illustrate an important principle: meaningful progress often comes from incremental enhancements that support existing processes, rather than from introducing highly autonomous systems prematurely. This principle is particularly relevant in the regulated clinical environment.
More advanced agents – such as goal-based, utility-based, and learning agents –introduce higher levels of autonomy. They can pursue defined objectives, evaluate alternative courses of action, and, in some cases, improve their performance over time. While these capabilities may be attractive, their use requires careful consideration of scope, governance, and accountability.
Matching Agent Types to Endpoint Adjudication Roles
With these principles in mind, it becomes possible to assess how different types of AI agents might support specific roles within the endpoint adjudication process.
A model-based agent could assist investigational site personnel when an event requiring adjudication occurs. If connected to appropriate local data sources, such an agent could retrieve relevant documents, verify completeness, apply predefined redaction rules, and transmit the resulting package to the adjudication system. This approach could reduce manual workload and ensure consistent application of rules. However, because such an agent would need to operate within the site’s systems, it would fall outside the sponsor’s direct control, which introduces practical and governance considerations.
Support for adjudicators could be provided by an agent integrated into the adjudication platform and provided by the sponsor. Such an agent could perform preliminary package reviews, identify missing information, and suggest follow-up requests. While it might also propose a medical assessment, any use of algorithm-generated opinions would require validated methodologies and extensive qualification, placing this capability beyond the scope of routine operational support.
A learning agent could assist medical reviewers by identifying potential adjudication cases that may not have been flagged by investigational sites. To function effectively, such an agent would require access to condition-specific medical knowledge, information about the investigational product, and the full clinical dataset.
The coordinator role, by contrast, is largely administrative and process-driven. Many coordinator activities involve structured decision rules, task sequencing, and workload management and status tracking. For these activities, a goal-based agent represents a particularly suitable form of AI support. This role offers a controlled environment in which AI agents can demonstrate value while remaining within clearly defined boundaries.
Conclusion: Simplicity and Fit, not Ambition
Selecting the right type of AI agent for endpoint adjudication depends less on technical ambition than on a clear understanding of the task at hand. Applying overly complex agents where simpler approaches suffice increases risk without delivering proportional benefit.
Across endpoint adjudication, the most promising applications of AI agents are those that support defined roles, operate within established processes, and remain subject to appropriate human oversight. Matching agent capabilities to task requirements ensures that AI contributes to efficiency and consistency while preserving accountability.
What Comes Next
In the next article of this series, the coordinator role will be used as a practical example to examine how an AI-supported adjudication process compares with a fully human-based approach, including considerations related to data confidentiality and real-world implementation constraints.
