Challenge
Deploying agents that can act autonomously in production.
To unlock tangible business value, organizations must look beyond copilots and AI assistants, creating agents that can act—capably wielding tools and interfacing fluidly with company systems.
This level of autonomy requires trust that agents will adhere to company policies and behavior requirements, especially in customer-facing tasks.
Prompt engineering alone is too fragile to provide the dependability and control needed to promote AI agents to production.
Solution
Unlock reasoning agents tuned to your enterprise with RL.
Reinforcement learning enables organizations to encode desired behaviors and requirements into the model itself, while also unlocking enterprise-specific reasoning capabilities.
Reasoning enables agents to reflect on intent, plan tool use, and execute complex actions. Enterprises can critique agents' CoT to improve autonomy.
Reinforcement learning allows companies to tune personalized reasoning models that are able to interact autonomously with their business systems.
A leading North American EdTech company launched an Al tutor to elevate student learning outcomes in a safe, efficient, and scalable way.
They wanted their Al tutor to tailor its approach to the unique needs and learning preferences of the individual student, incorporating in-house research on educational strategies.
The EdTech built a proof-of-concept using proprietary models; unfortunately, customizing tutor behavior to the necessary degree was impossible with prompt engineering alone.
Instead, they used Adaptive Engine to reinforcement fine-tune a 24B model for the required behaviors, using a combination of AI judges and self-play to generate synthetic training data.
The small model outperformed all frontier models and specialty products, including GPT-4o, Gemini Coach, and Khanmigo, on helpfulness, conversation quality, and educational strategy.
LOW-LIFTThe model was tuned using mostly synthetic data; a sample of just 50 annotated teacher feedbacks were used to align model adherence to preferred educational strategies.
EFFICIENTUsing proprietary models for this agentic worklow created unacceptable latency and cost. With a 24B parameter model, the EdTech was able to cut costs significantly and improve latency.
Continuously improvingOnce deployed to production, agents continue to learn from production feedback; so, the Al tutor will continuously improve learning outcomes based on student feedback.
Generate synthetic data seamlessly
With a small amount of seed data, users can generate quality training data using self-play.
Tune with reinforcement learning
Outperform proprietary models with minimal data annotation using RLAIF or RLEF.
Evaluate with customized AI judges
Understand model performance on the metrics that matter most with customizable AI judges.
