Agentic AI
Training the doing layer of AI—autonomous agents that execute tasks in digital and physical environments. We provide the demonstration data, execution logs, and expert verification that define capable, reliable agents.
Data Capabilities
Six purpose-built services for teams building agents that must execute, not just respond.
Agentic Task & Verifier Design
End-to-end task specification, environment scaffolding, and binary or rubric-based verifiers for agentic AI workflows that require automated reward signals. Appen designs verifiable task environments where agent success can be measured objectively and consistently at scale.
Trajectory Analysis & Failure Mode Taxonomy
Systematic review of agent action sequences to identify where and why agents fail, misplan, or produce unsafe outputs. Appen's trajectory analysis service builds the failure taxonomy that guides the next data collection and fine-tuning cycle.
Golden Trajectory Creation
Expert-demonstrated step-by-step task completions across coding, web navigation, tool use, and multi-step reasoning. Golden trajectories are the imitation learning signal that teaches agents to act before reinforcement learning begins.
Full RL Environment Design
Complete reinforcement learning environment design, including task definition, reward function specification, and sandbox scaffolding for RLVR and RLHF-based agentic training. Appen builds environments where verifiable rewards are achievable and measurable.
Enterprise RAG Evaluation
Human evaluation of retrieval-augmented generation pipelines across precision, recall, citation accuracy, and hallucination rate. Appen's RAG evaluation service closes the gap between leaderboard performance and enterprise AI production reliability.
SWE-Driven Deep Evaluation Workflows
Software engineer-led evaluation of agentic code generation, debugging, refactoring, and tool-use sequences. Designed for teams where agent outputs will be reviewed or executed by technical users who can identify subtle logical and functional failures.
Insights & Resources
Expert thinking on agentic ai from Appen’s data scientists and AI researchers.
Agentic AI vs Generative AI: What’s the Real Difference?
A practical breakdown of what distinguishes agents from generators—and why the data requirements are fundamentally different.
Appen Launches Next-Generation Annotation Platform with Enhanced LLM Fine-Tuning
New platform capabilities designed specifically for agentic AI evaluation, trajectory annotation, and RL environment management.
How ReflexAI Empowers Veterans with AI Mental Health Support
Developing conversational AI trained on expert-annotated dialogue for sensitive, high-stakes mental health applications.
Ready to build with confidence?
Talk to our team about agentic AI data—from golden trajectories to full RL environment design.