Use case
Agentic reasoning & trajectories
Build the next generation of AI agents and solve the training bottleneck with scalable, human-based trajectory training and evaluation
Why Labelbox for agentic reasoning
Generate high-quality data
Empower human experts to easily refine existing trajectories or create new, ideal examples, ensuring the best possible training data for your models.
Scale agent development
Use the purpose-built Agent Trajectory Editor to efficiently manage the data lifecycle for agentic systems, and scale up human evaluations with Alignerr.
Accelerate development
Streamline the creation, annotation, and analysis of agent trajectories, significantly reducing the time from initial concept to deployment.
Custom evaluation workflows
Use customizable, fine-grained tools to pinpoint exactly where agents are succeeding and failing, leading to more effective training and optimization.
The importance of agentic reasoning to AI’s future
AI agents are transforming technology by performing complex tasks autonomously. Agent trajectory training, analyzing the sequence of reasoning, actions, and observations, is crucial to developing reliable and capable agents. Human evaluations and advanced training data are essential to moving AI towards proactive, goal-oriented systems that mirror human problem solving.
The hurdles in evaluating and training agentic systems
Evaluating and training AI agents is challenging. Trajectory data is complex, requiring specialized tools for capture and annotation. Traditional methods struggle, and identifying subtle errors within reasoning, tool usage, or observations demands significant domain expertise. Without the right tools or human expertise, AI labs face a major obstacle to building high-performing agent systems.
Accelerate agentic AI development with Labelbox
Labelbox's innovative Agent Trajectory Editor simplifies agent training and evaluation. Our platform enables effortless capture, editing, and annotation of complex agent trajectories. Customizable classifications and an intuitive interface allow precise feedback, streamlining development, and accelerating optimization, from initial creation to production monitoring.
Discover and recruit the world's most qualified AI trainers
Customer spotlight
A leading AI lab aimed to improve its large language model (LLM) for K-12 STEM education by identifying its weaknesses. Labelbox's Labeling Services, in collaboration with the Alignerr network, assembled a team of STEM experts with advanced degrees in fields like chemistry, biology, and engineering. These experts created multimodal prompts (text and image) and accurate answers to assess the model. Their work helped pinpoint the LLM’s limitations, enabling the lab to target areas for improvement.
Learn moreCritical tasks needed to enhance agentic reasoning & trajectories
Analyze source quality
Assess if the agent used reliable and appropriate sources for information retrieval.
Detect biases & fairness
Identify any biases or unfair representations present in the agent's trajectory or final output.
Evaluate optimal tool use
Determine if the agent selected the most effective tools and used them correctly to achieve its goals.
Review reasoning logic
Evaluate the soundness and efficiency of the agent's planning and reasoning steps.
Enhance output formatting
Ensure the agent's output conforms to desired style, structure, and branding guidelines.
Validate full task completion
Evaluate the final task completion status to ensure the agent fulfilled the original goal.
