Agentic AI

Trajectory Analysis and Failure Mode Taxonomy

Systematic trajectory analysis and failure mode classification for agentic AI , identifying where autonomous agents break down and building taxonomies for safer systems.

Agentic AI systems fail in structured, classifiable ways. An agent that misplans at step three will fail differently from one that executes correctly but pursues the wrong sub-goal, or one that succeeds locally while violating a global constraint. Appen's trajectory analysis service systematically identifies, classifies, and documents these failure modes at the level of granularity that guides the next data collection and fine-tuning cycle.

What Appen Delivers

Step-Level Trajectory Review

Expert review of complete agent action sequences, with annotation of each step as correct, suboptimal, incorrect, or unnecessary. Step-level annotation identifies precisely where in a trajectory an agent's reasoning or execution breaks down, providing targeted signal for fine-tuning rather than aggregate failure counts.

Failure Mode Classification

Systematic taxonomy of agent failure types across categories including planning failures, tool misuse, context loss, instruction misinterpretation, and safety violations. A structured failure taxonomy transforms individual trajectory errors into an actionable dataset for targeted model improvement.

Counterfactual Correction

For trajectories where an agent fails, human experts provide the correct continuation from the point of failure, creating counterfactual trajectory pairs that can be used directly for supervised fine-tuning of the failing behaviour.

Failure Rate Analysis by Domain and Task Type

Aggregated failure mode statistics across task categories, difficulty levels, and domain types, identifying where your agent is systematically underperforming and what data interventions are most likely to improve performance.

Closing the Agentic Improvement Loop

Full RL environment design and golden trajectory creation define what agents should do. Trajectory analysis documents what they actually do and why the gap exists. Together, these three capabilities form the data loop that drives iterative agentic AI improvement from initial training through to deployment reliability.