AI Agents That Learn on the Fly: Adapting to User Needs in Life Sciences

Author: Denis Avetisyan

New research demonstrates how reinforcement learning can create AI systems that optimize their strategies in real-time based on user feedback, without the need for extensive labeled datasets.

Cumulative rewards increase with the number of queries processed.

This review details a framework leveraging contextual bandits and Thompson Sampling for adaptive agent design in life sciences applications, focusing on reward optimization through user interaction.

While large language models demonstrate promise as agents in complex domains, adapting to nuanced user needs and evolving information remains a key challenge. This is addressed in ‘Optimizing Life Sciences Agents in Real-Time using Reinforcement Learning’, which introduces a novel framework leveraging contextual bandits to enable adaptive decision-making for AI agents in the life sciences. By learning from user feedback-without requiring labeled data-the system optimizes strategy and tool selection, yielding significant improvements in user satisfaction. Could this approach unlock a new era of personalized, continuously improving AI assistance in specialized fields requiring complex reasoning?

Beyond Reaction: The Rise of Proactive Intelligence

Early language models demonstrated remarkable proficiency in generating human-quality text, mastering grammar, style, and even nuanced creative writing. However, these systems largely functioned as sophisticated text predictors, responding to prompts with generated outputs but lacking the capacity for independent action. Unlike agents capable of perceiving environments and enacting changes, traditional models remained confined to the digital realm of text, unable to proactively perform tasks such as scheduling appointments, conducting research, or controlling physical devices. This limitation stemmed from their architecture, designed for pattern recognition and completion rather than goal-oriented behavior and real-world interaction, effectively positioning them as powerful communicators but passive observers rather than active participants.

The emergence of Generative AI Agents signals a fundamental change in artificial intelligence, moving beyond systems that simply react to prompts. These agents are designed not just to generate text, but to autonomously pursue defined objectives. Unlike traditional language models that offer static responses, these agents can dynamically interact with their environment – be it a digital workspace or the real world – by breaking down complex goals into sequential actions. This proactive capability necessitates more than just improved language processing; it demands systems capable of planning, tool utilization, and continuous adaptation, effectively transforming AI from a passive assistant into an active problem-solver. This shift promises to unlock applications where AI can independently manage tasks, optimize processes, and even discover novel solutions without constant human intervention.

Successfully deploying Generative AI Agents demands more than just powerful language models; it requires robust frameworks engineered for intricate environmental interactions. These systems must navigate complex tasks by dynamically selecting and utilizing appropriate tools – whether APIs, databases, or even other AI models – to achieve specified goals. Crucially, these frameworks aren’t static; they must continuously adapt to evolving contexts, learn from past experiences, and refine strategies in real-time. This adaptive capability is achieved through mechanisms like reinforcement learning or iterative refinement, allowing the agent to overcome unforeseen challenges and maintain progress towards its objectives. The development of such frameworks represents a significant leap toward truly autonomous AI, capable of tackling complex, real-world problems with minimal human intervention.

Contextual Adaptation: The Power of Strategic Choice

Contextual Bandit algorithms represent a class of reinforcement learning techniques focused on sequential decision-making where the optimal action is dependent on the current context. Unlike traditional multi-armed bandit problems which assume a fixed probability distribution for each action’s reward, contextual bandits incorporate side information – the ‘context’ – to personalize action selection. This allows the agent to learn a policy mapping contexts to actions, maximizing cumulative reward by exploiting successful actions in similar situations and exploring potentially better alternatives. The robustness of these algorithms stems from their ability to continuously adapt this mapping as new data is observed, enabling effective strategy selection even in dynamic and non-stationary environments. Performance is typically measured by metrics such as cumulative reward, regret (the difference between the reward achieved and the optimal reward), and click-through rate in applications like recommendation systems and online advertising.

The performance of a contextual bandit algorithm is directly tied to its ability to accurately define and interpret the ‘Context Space’. This space represents the complete set of possible situations, or states, the agent may encounter during operation. A well-defined Context Space allows the algorithm to differentiate between situations and select actions optimized for each specific context. Conversely, a poorly defined or incomplete Context Space – one that fails to capture critical differentiating factors – will limit the algorithm’s ability to learn optimal policies and will result in suboptimal action selection. The dimensionality and complexity of the Context Space directly impact the algorithm’s sample complexity; larger and more complex spaces require more data to effectively learn.

Context Feature Extraction involves transforming raw query data into a set of numerical features suitable for input into a contextual bandit algorithm. This process typically includes techniques such as tokenization, stemming, and the creation of term frequency-inverse document frequency (TF-IDF) vectors, or the utilization of pre-trained word embeddings like Word2Vec or GloVe. The quality of these extracted features directly impacts the bandit’s ability to generalize and select optimal actions; poorly chosen or irrelevant features can lead to inaccurate context representation and suboptimal performance. Feature engineering may also involve incorporating user-specific data, temporal information, or other relevant signals to enrich the contextual representation and improve the bandit’s learning process.

Balancing Discovery and Commitment: Thompson Sampling in Practice

Thompson Sampling is a Bayesian approach to the exploration-exploitation dilemma inherent in contextual bandit problems. Unlike methods like $\epsilon$-greedy which rely on fixed probabilities or heuristics, Thompson Sampling maintains a probability distribution over the expected reward of each action. At each time step, the algorithm samples a reward estimate from this distribution for each available action, and selects the action with the highest sampled value. This probabilistic selection inherently balances exploration – by occasionally sampling high values from actions with uncertain rewards – and exploitation – by favoring actions with consistently high estimated rewards. The Bayesian framework allows the algorithm to update its beliefs about action quality based on observed rewards, refining the probability distributions and improving decision-making over time.

Thompson Sampling utilizes Beta-Bernoulli conjugate priors to represent uncertainty about the expected reward of each action. Specifically, for each action, a Beta distribution, parameterized by $α$ and $β$, is maintained; this distribution represents the prior belief about the action’s success rate. When an action is selected, a sample is drawn from this Beta distribution, and if the action results in a reward (success), the corresponding $α$ parameter is incremented; otherwise, the $β$ parameter is incremented. This Bayesian updating process ensures that the algorithm maintains a probabilistic belief about each action’s quality, allowing it to balance exploration – selecting actions with high uncertainty – and exploitation – selecting actions with high estimated reward – based on the sampled probabilities.

The Thompson Sampling agent utilizes a reward function to quantify the effectiveness of each action, receiving feedback after each query to update its internal model. This feedback is crucial; the agent doesn’t simply record successes and failures, but rather maintains a probability distribution over the expected reward for each action. With each interaction, this distribution is updated using Bayesian inference. Empirically, the system typically requires between 20 and 30 queries to exhibit discernible patterns in strategy optimization, indicating a convergence towards actions with higher estimated rewards and a reduction in the sampling of suboptimal choices. This relatively rapid learning rate is a key benefit of the algorithm.

Real-World Impact: Augmenting Intelligence in Life Sciences

The developed adaptive framework holds considerable promise for transforming life sciences through the deployment of intelligent AI agents. These agents can move beyond simple data retrieval to actively assist in multifaceted challenges such as accelerating drug discovery timelines and enhancing the accuracy of clinical decision support systems. By dynamically adjusting strategies based on user interactions and evolving data landscapes, the framework enables AI to tackle the inherent complexity of biological systems. This capability allows for personalized approaches to both research and patient care, potentially identifying novel therapeutic targets or tailoring treatment plans with unprecedented precision. Ultimately, the framework envisions a future where AI serves as a collaborative partner, augmenting the expertise of scientists and clinicians to drive innovation and improve health outcomes.

A core strength of this adaptive framework lies in its ability to quantitatively assess agent performance through the tracking of cumulative reward. This metric doesn’t simply indicate success or failure, but provides a nuanced understanding of how effectively the agent is navigating complex tasks. Demonstrated in life science applications, the framework consistently outperformed random approaches, achieving a notable 15-30% improvement in user satisfaction. This enhancement stems from the system’s capacity to dynamically select optimal strategies for addressing user queries, a process fueled by the continuous monitoring and refinement enabled by cumulative reward tracking. Consequently, the system isn’t merely executing pre-programmed responses; it’s actively learning and improving its approach based on measurable outcomes, leading to a more effective and user-centric experience.

The long-term efficacy of this adaptive framework hinges on its ability to navigate non-stationarity-the inevitable shifts in optimal strategies over time. Initial success in responding to life science queries doesn’t guarantee sustained performance, as user needs, available data, and even the underlying scientific landscape are constantly evolving. Consequently, the system isn’t designed as a static solution, but rather as a continually learning entity. Ongoing adaptation is achieved through continuous monitoring of performance metrics and iterative refinement of its decision-making processes, ensuring it remains responsive to changing conditions and maintains a high level of accuracy and relevance. Addressing non-stationarity is therefore not merely an improvement, but a fundamental requirement for the framework’s lasting utility and real-world impact.

Productionizing Intelligence: The Role of Strands Agents

AWS Strands Agents offers a complete solution for translating agentic AI concepts into functional, real-world applications. This framework isn’t simply a collection of tools, but rather an integrated system designed to manage the entire lifecycle of an agent – from initial design and development to rigorous testing, secure deployment, and continuous monitoring. It addresses key challenges in productionizing AI, such as managing complex agent interactions, ensuring reliable performance at scale, and maintaining the necessary observability for debugging and improvement. By providing pre-built components and a structured approach, Strands significantly lowers the barrier to entry for organizations seeking to leverage the power of autonomous agents, allowing them to focus on building innovative solutions rather than wrestling with underlying infrastructure complexities.

AWS Strands Agents fundamentally alters the lifecycle of agentic AI, offering a cohesive platform designed to accelerate innovation and deployment. Traditionally, assembling the necessary components – from large language models to memory systems and tool integrations – proved a complex and time-consuming undertaking for organizations. Strands circumvents these hurdles by providing pre-built, modular components and a standardized development environment. This streamlined approach allows teams to rapidly prototype agent behaviors, test various configurations, and iterate on designs with unprecedented speed. Beyond prototyping, the platform’s scalable infrastructure automatically handles the resource allocation and management needed to deploy agents into production environments, ensuring reliable performance even under significant user load. The result is a significant reduction in time-to-market for AI-powered applications, empowering organizations to quickly realize the benefits of intelligent automation.

The advent of technologies like AWS Strands Agents signifies a leap toward truly proactive artificial intelligence, moving beyond reactive systems to applications capable of anticipating user needs and autonomously executing complex processes. This isn’t simply about automating existing tasks; it’s about enabling agents to learn, adapt, and independently manage workflows, from scheduling and resource allocation to personalized recommendations and intricate data analysis. The potential extends across numerous sectors, promising streamlined operations, enhanced user experiences, and the ability to address challenges previously requiring significant human intervention. By lowering the barrier to entry for building and deploying these intelligent agents, this technology paves the way for a future where AI seamlessly integrates into daily life, augmenting human capabilities and driving unprecedented levels of efficiency.

The pursuit of adaptive strategies, as detailed in the presented framework, echoes a sentiment of elegant reduction. The paper’s emphasis on learning from user feedback without reliance on labeled data exemplifies a system striving for inherent understanding, rather than imposed instruction. This resonates with Turing’s observation: “Intelligence is the ability to learn and apply knowledge and skills.” The work effectively demonstrates that a truly intelligent system-an adaptive agent in life sciences-minimizes the need for explicit direction, maximizing its performance through observation and iterative refinement. It is not the complexity of the algorithm, but its capacity to distill meaningful insight from minimal input, that defines its success.

Where to Now?

The presented work addresses a surface. Adaptive agents, guided by reward, are not novel. The utility of contextual bandits, similarly, lacks surprise. The current value resides in the specific application – life sciences – and the implicit acknowledgement of a persistent difficulty: eliciting meaningful signal without exhaustive annotation. Future iterations must confront the fragility of this signal. User feedback, while potent, is subject to noise, bias, and the inherent inconsistencies of human judgement.

A deeper examination of the reward function itself is required. Current formulations prioritize immediate satisfaction. Longer-term consequences, crucial in many life science applications, remain largely unaddressed. Furthermore, the framework’s scalability to genuinely complex decision spaces – those involving substantial state dimensionality and delayed rewards – demands rigorous testing. Simplification, not expansion, is the key.

Ultimately, the enduring question is not whether agents can adapt, but whether adaptation, in and of itself, yields genuine progress. The pursuit of optimization, divorced from a clear understanding of underlying mechanisms, risks merely rearranging the symptoms. True advancement lies not in building more elaborate systems, but in refining the questions they attempt to answer.

Original article: https://arxiv.org/pdf/2512.03065.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/