Distinguishing AI helpers from AI agents in clinical trial operations
Artificial intelligence is increasingly embedded in everyday digital tools. Many professionals already interact with AI-powered helper modules integrated in familiar applications. These tools assist with tasks such as online searches or responding to user questions and are typically powered by large language models (LLMs). LLMs are trained on large collections of text and are designed to interpret questions and generate human-like responses.
While useful, these tools operate within a limited interaction model: a human asks a question, the system provides an answer, and any resulting action must still be carried out by a person. Understanding this limitation is essential when considering more advanced applications of AI.
From AI Helpers to AI Agents
AI agents build on the same underlying language model technology but extend it significantly. Rather than responding only to individual prompts, AI agents are software systems designed to pursue defined goals and complete tasks.
In addition to generating responses, AI agents can observe their environment, retain memory, plan sequences of actions, and execute tasks with pre-defined boundaries. They may access external data sources, extract and organise information, and coordinate multiple steps to achieve an objective. In this sense, AI agents connect reasoning capabilities with operational execution.
When designed and governed appropriately, AI agents can complement human expertise and support more efficient and consistent workflows. Their use, however, must remain aligned with clearly defined processes, roles, and accountability frameworks.
Core Characteristics and Components of AI Agents
AI agents resemble human operators in several functional ways, although their capabilities vary widely depending on design and intended use. More advanced agents may combine several of the following characteristics:
- Reasoning, using available information and logic to draw conclusions
- Observation, gathering relevant information from their environment
- Planning, identifying and sequencing steps required to achieve a goal
- Action, performing tasks based on decisions or predefined rules
- Collaboration, interacting with humans or other AI agents
- Adaptation, improving performance over time based on feedback
To operate effectively, AI Agents rely on four foundational components.
At their core is a large language model (LLM), where interpretation and reasoning take place. Because LLMs are trained primarily on publicly available information, they must be supplemented with access to study-specific and operational clinical data to perform meaningful work in clinical trial operations.
AI agents also require memory to maintain context and learn from experience. Memory may be short-term, long-term, episodic, or shared across agents. This allows the agent to recall past interactions, maintain continuity, and adapt its behaviour over time.
To act beyond generating text, an AI agent requires access to tools. These may include software interfaces, data repositories, or other systems that enable information retrieval, data manipulation, or controlled actions.
Finally, each AI agent operates within a defined role or “persona.” This determines how it behaves, what decisions it is authorised to make, and how it interacts with users. The persona may evolve with experience but remains bounded by its assigned role.
Types of AI Agents and Selection Considerations
AI agents can be classified by their level of autonomy and complexity. Selecting the appropriate type is critical and should be guided by the nature of the task and the associated risk.
- Simple reflex agents respond to predefined conditions using fixed rules, without memory or foresight
- Model-based reflex agents maintain an internal representation of the environment, allowing them to account for changes over time
- Goal-based agents evaluate possible actions based on their ability to achieve a defined objective
- Utility-based agents select actions that maximise overall benefit across multiple outcomes
- Learning agents adapt their behaviour over time based on feedback and experience
In most cases, applying the simplest agent capable of performing the task is preferable. This approach reduces cost, complexity, and operational risk. More advanced configurations can be introduced progressively as experience and confidence grow.
Conclusion: A Process-First Approach
AI agents represent an evolution beyond AI helper tools by linking reasoning capabilities to structured action. Their potential value lies not in autonomy alone, but in their ability to operate within clearly defined processes and roles.
Experience across industries1 shows that successful AI initiatives depend less on technical sophistication and more on alignment with well-understood workflows and responsibilities Without clear workflows, roles, and objectives, even advanced AI systems fail to deliver value.
1MIT “The GenAI Divide state of AI in business 2025"
What Comes Next
With a clear definition of AI agents in place, the next step is to examine the environment in which they might operate. The following article in this series explores the clinical endpoint adjudication process, describing the roles involved and establishing a foundation for evaluating where AI support may realistically add value.
