Agentic AI

Agentic Task and Verifier Design

Expert-designed task specifications and verifier rubrics for agentic AI , enabling reliable multi-step task execution and outcome verification at scale.

Reinforcement learning requires rewards. For agentic AI systems, those rewards must be reliable, consistent, and grounded in task-specific correctness criteria that a verifier can evaluate without human review at every step. Appen's agentic task design service builds the task environments, instruction specifications, and binary or rubric-based verifiers that make scalable agentic RL training possible.

What Appen Delivers

Task Environment Specification

End-to-end definition of agentic tasks including goal specification, available tools, environment state, success criteria, and failure conditions. Task specifications are designed to be unambiguous for both agents attempting the task and verifiers assessing completion, eliminating the ambiguity that undermines reward signal quality.

Verifier Design and Implementation

Binary and rubric-based verifiers for coding tasks, web navigation, information retrieval, multi-step reasoning, and tool use. Well-designed verifiers enable RLVR training by providing the reliable ground-truth signal that makes reinforcement learning scalable beyond the limits of per-step human evaluation.

Adversarial Task Probing

Systematic testing of task environments to identify failure modes including reward hacking opportunities, ambiguous success criteria, and environment exploits that would allow an agent to achieve high reward without actually completing the intended task.

Human Baseline Performance Data

Expert-human performance recordings on designed tasks, providing the ceiling performance benchmark against which agent trajectories can be compared and the imitation learning seed data that accelerates early agent training.

Verifiers as the Infrastructure of Agentic Training

The quality of an agentic training pipeline is bounded by the quality of its verifiers. Ambiguous verifiers produce reward hacking. Incomplete verifiers produce agents that achieve the measured objective while failing the intended one. Appen's verifier design methodology is built around the specific failure modes that undermine agentic RL at scale.

Combined with golden trajectory creation and trajectory analysis, task and verifier design completes the agentic data pipeline from task definition through to failure mode correction.