Reference
AI Agent Glossary
Key terms in agent evaluation, expert data, and AI reliability.
Agent Reliability
The ability of an AI agent to consistently complete multi-step tasks in production environments without planning failures, tool-use errors, or ambiguity collapse. Measured across full task chains, not isolated capabilities.
Related productAuto Research
The discipline of building AI agents that participate in the loops driving scientific and engineering progress — hypothesis generation, data selection, training pipeline design, experimentation, and iteration. Auto Research benchmarks (such as AutoLab) deliver tasks as sandboxed Docker environments with fixed pipelines, fixed seeds, hardware budgets, a graded metric, a baseline (e.g. random selection), and a reference solution. Agents are scored on whether their submitted code actually moves the metric, not whether it compiles.
Related productData Card
An auditable document accompanying a dataset that describes its composition, collection methodology, annotator qualifications, quality metrics, and intended use. Data cards enable transparency and reproducibility in AI training.
Data Provenance
The documented history of a data point: who created it, their qualifications, when it was created, and what verification steps it passed. Provenance enables quality auditing and regulatory compliance for AI training data.
Related productEvaluation Framework
A structured methodology for assessing AI agent performance that includes task design, metrics selection, failure taxonomy, and regression testing. Goes beyond simple accuracy scoring to diagnose root causes of failures.
Expert Data
AI training data created and verified by domain specialists (PhDs, senior engineers, licensed practitioners) with reasoning provenance. Distinguished from crowd-sourced data by domain authority, reasoning traces, and multi-stage verification.
Related productFailure Mode
A specific, repeatable pattern of agent error — such as 'loses state after tool call errors' or 'applies wrong formula in multi-step derivations.' Failure modes are classified by root cause and ranked by frequency × severity.
Failure Taxonomy
A structured classification system for AI agent errors. Common categories include planning errors, tool-use errors, knowledge gaps, and reasoning errors. Taxonomies transform vague quality complaints into specific, actionable engineering tasks.
Related productHuman-in-the-Loop
A system design pattern where human experts review, correct, or validate AI outputs at defined checkpoints. In expert data production, humans provide ground truth labels and reasoning provenance that automated systems cannot.
Multi-Stage Verification
A data quality process where annotations are independently reviewed by multiple experts, with disagreements resolved through structured arbitration. Catches errors that single-pass review misses.
Regression Testing
The practice of re-running evaluation cases from previously fixed failure modes after model updates to ensure old failures don't reappear. Essential for maintaining reliability as agents evolve.
Reinforcement Learning Environment
A simulated or structured setting where AI agents learn through trial, error, and reward signals. Effective RL environments model real-world task complexity including multi-step planning, tool interaction, and partial observability.
Severity Ranking
Prioritization of failure modes by the product of frequency and impact. A rare catastrophic failure may outrank a common minor one. Severity ranking guides resource allocation for agent improvement.
Synthetic Data
Training data generated by AI models rather than humans. Scales cheaply but inherits and amplifies the generating model's biases and errors. Best used for augmentation alongside expert-verified ground truth.
Tool-Use Agent
An AI agent that interacts with external tools (APIs, databases, code executors) to complete tasks. Tool-use agents face unique failure modes including incorrect API calls, misinterpreted results, and cascading tool errors.
Trace Analysis
The process of examining an AI agent's complete execution log — every planning decision, tool call, intermediate result, and recovery attempt — to identify where and why failures occur. Foundational to agent failure diagnosis.
Related productVerified Task Environment
A reproducible, sandboxed environment for evaluating Auto Research agents. Includes a Dockerfile, fixed datasets, a fixed training and evaluation pipeline, hardware and time budgets, a graded metric, and a baseline score. Modeled on AutoLab's task layout (instruction.md, task.toml, environment/, solution/, tests/).
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