Software factory

Agent development lifecycle

A repeatable operating cycle for building, testing, deploying, monitoring, improving, and governing an agent system.

Agentic coding

A software-development method in which a coding agent plans and executes multi-step work while a human directs, reviews, or governs the outcome.

Agentic software engineering

The discipline of designing software work, environments, and controls so AI agents can perform substantial engineering without displacing human product judgment and accountability.

Clanker

A derogatory slang term for a robot, AI system, or automated technology, used jokingly or hostilely to express disdain for machines or their perceived replacement of human work.

Coding agent

An AI agent that can inspect a software project, change it through development tools, evaluate the result, and iterate toward a software outcome.

Coding assistant

An AI system that helps a human understand or change software while the human remains the primary driver of the workflow.

Cognitive debt

The accumulated loss of shared human understanding, reasoning continuity, or recovery competence caused by repeatedly delegating cognition without rebuilding comprehension.

Context economy

The way software and repository structure changes the amount, quality, and retrieval cost of context an agent needs for a correct change.

Controlled self-improvement

A bounded process for improving an agent's prompts, skills, memory, workflows, routing, or harness under independent evaluation and reversible rollout.

Cost per accepted durable outcome

The full cost of producing, validating, correcting, and operating work divided by outcomes that pass acceptance and remain useful for a defined period.

Digital twin

A living digital representation of a specific system or environment, connected closely enough to test or reason about its real behavior.

Human attention budget

The finite supply of skilled human judgment available to specify, review, govern, and recover automated work.

Improvement graph

A proposed control map showing how optimizers, evaluators, counter-metrics, auditors, and promotion gates govern changes to an AI system.

Large language model operations (LLMOps)

The operational discipline for evaluating, deploying, observing, governing, and maintaining applications built around large language models.

Machine learning operations (MLOps)

The engineering and operational practices used to build, deploy, observe, govern, and maintain machine-learning systems throughout their lifecycle.

Production truth

Evidence from sustained real operation used to judge whether a system keeps producing acceptable outcomes after launch.

Promotion

The governed decision to move an artifact or change into a more trusted lifecycle state after required evidence and policy checks.

Run contract

A machine-readable, human-auditable agreement defining one agent run's objective, authority, evidence, limits, and recovery path.

Semantic failure

A failure that looks mechanically successful while producing the wrong meaning, intent, binding, or real-world consequence.

Single-writer control

A control pattern in which all changes to a sensitive persistent state pass through one authorized writer.

Software factory

A repeatable production system that turns software demand into accepted, operated software through reusable processes, tooling, controls, and feedback.

Spec-driven development

A development approach in which an explicit, versioned specification materially guides implementation and verification.

Token budget

An explicit allocation or ceiling for model-token consumption across a named scope, with rules for warning, stopping, and exceptions.

Token burn

The amount or rate of model-token consumption across a defined unit of work and time window.

Token efficiency

The relationship between quality-constrained outcomes and the model tokens consumed to produce them.

AI slop

A contested label for low-quality, low-effort, often high-volume content produced or amplified with generative AI.

Dark software factory

A domain-bounded software production system in which humans specify intent, risk, and policy while a model-harness-environment system plans, builds, verifies, ships, observes, and repairs software with little routine human intervention.

Outcome maxing

An emerging label for optimizing an AI workflow around accepted, durable results instead of easy-to-count activity.

Token maxing

Deliberately or incentive-drivenly maximizing the tokens consumed by AI work, often by expanding context, reasoning, turns, agents, or tasks, while treating greater usage as a route to capability or a proxy for productivity.

Token minning

An emerging counterterm for systematically reducing AI token consumption while preserving an explicit threshold for useful outcome quality.

Token spin

Token-consuming AI activity that produces insufficient learning, accepted work, or maintained value for its total cost.

Useful intelligence per dollar

Sarah Friar's proposed AI value scorecard connecting useful work, successful-task cost, dependability, and value at scale.

Vibe coding

A loose AI-assisted programming style that accepts generated behavior with little inspection or understanding of the underlying code.