Agentic AI training data
Agentic AI training data teaches models to plan, act, and use tools.
Agentic AI services
Agents act, plan, and fail in ways that need specialist human judgment to correct. For 30 years Appen has built the trajectories, failure annotations, and RL environments that help research and engineering teams ship agents that hold up in production, backed by software engineers, OSWE-certified security experts, and practitioners from the enterprise functions agents actually run.
Golden trajectory creation
Expert-authored optimal execution paths: the ideal sequence of actions, tool calls, and decisions that defines a correct agent run.
Trajectory analysis and failure mode taxonomy
Structured classification of where and why agents break down, so teams can target fixes.
RL environment design
Complete reinforcement learning setups covering tools, context, tasks, and verifiers across coding, DevOps, ITSM, HR, sales, and finance.
Agentic task and verifier design
Golden-answer construction and rubric-based scoring that measures whether agents are correct, complete, and well-reasoned.
SWE-driven deep evaluation
Long-form, no-LLM-assisted reviews led by software engineers for the failures only expert human judgment catches.
Enterprise RAG evaluation
End-to-end measurement of how accurately agents retrieve, synthesize, and apply private enterprise knowledge at production scale.
Specialist talent across coding, security, and enterprise functions
Generic annotators fall short as enterprise agentic deployment accelerates. Appen's vetted specialists give research teams domain-authentic data, annotation, and evaluation, deployed quickly.
Coding and security
Software engineers handle long-form coding trajectory review, bug localization, and failure-mode annotation. These are the cases where LLM-as-a-judge underperforms. OSWE-certified offensive security specialists identify vulnerabilities in model-generated code and build labeled data to remove them.
- Long-form coding trajectory review and scoring
- Failure-mode annotation across plan, tool use, and output
- OWASP-aligned secure vs. insecure RLHF pairs
- Red-team prompts targeting injection and privilege escalation
Enterprise verticals: accounting, HR, marketing, IT/DevOps
Practitioners with genuine enterprise backgrounds generate authentic task demonstrations, evaluate outputs against professional standards, and build RL environments that reflect real workflow complexity across ERP, ITSM, HRIS, and CRM.
Ready-to-use datasets
Licensed off-the-shelf data available now or coming soon — accelerate development without starting from scratch.
Chinese instruction set sentence corpus
Large-scale instruction-tuning corpus covering multi-turn dialogue, reasoning, roleplay, and long-horizon tasks for training and evaluating agentic systems.
Chinese command and control prompt response corpus
Command-and-control prompt and response pairs for device and service execution, supporting evaluation of intent recognition and reliable tool invocation.
English (United States) device commands
Spoken device command audio enabling voice-driven agents to translate user intent into concrete actions.
Resources
How AI research organizations use Appen for agentic data.
Agentic Coding Trajectories
Annotating complete coding sessions to train agents that plan, debug, and iterate.
RL Environments
Designing custom reinforcement learning environments with realistic task structures and constraints.
ReflexAI veteran mental health support
Conversational AI trained on expert-annotated dialogue for high-stakes applications.
Rubric-based retrieval evaluation
Scoring how accurately agents retrieve and apply enterprise knowledge, across 790,000+ evaluations.
Insights and resources
Agentic AI vs generative AI: what's the real difference?
Why the data requirements differ fundamentally.
When an agent passes the test but misses the point
Why reward hacking makes auditable trajectories essential.
RLVR: building reliable, auditable AI systems
How verifiable rewards differ from RLHF, and where each fits.
Old is new again: how rubrics and fine-tuning work together
How rubric-based scoring turns human judgment into a measurable signal.
Frequently asked questions
What approach does Appen take to building agentic AI training data?
How is agentic AI data different from generative AI data?
How do we build golden trajectories?
How do you evaluate where an agent fails?
Can Appen build a custom RL environment for our domain?
Build agents that hold up in production
From golden trajectories to full RL environment design, our specialists support your research and engineering teams end to end.