AI-DLC Glossary
Quick reference for all AI-DLC terminology and concepts.
A
- Adoption
- The process of reverse-engineering an existing feature into AI-DLC intent artifacts, enabling operational management and structured iteration without requiring the feature to have been built through AI-DLC's construction phase
- AHOTLSee in paper
- Autonomous Human-on-the-Loop: human defines criteria and reviews output; AI operates autonomously within boundaries; used for well-defined, programmatically verifiable work
B
- BackpressureSee in paper
- Quality gates that automatically reject work not meeting criteria, providing feedback for iteration
- BoltSee in paper
- Smallest iteration unit in AI-DLC 2026; operates in supervised (HITL), observed (OHOTL), or autonomous (AHOTL) mode
C
- Completion CriteriaSee in paper
- Programmatically verifiable conditions that define when work is successfully done
- Completion PromiseSee in paper
- Signal (e.g., COMPLETE, BLOCKED) that autonomous execution has finished
- Context BudgetSee in paper
- Available attention capacity in AI context window; quality degrades when overloaded
D
- Design Blueprint
- A structured artifact produced by the Design Direction phase that defines a project's visual language — archetype, parameters, component patterns — and seeds the design knowledge artifact
- Design Direction
- Elaboration phase (2.75) where greenfield or early-stage projects select a visual archetype and tune design parameters, producing a design blueprint that guides wireframe generation and builder context
H
- HITLSee in paper
- Human-in-the-Loop: human validates each significant step before AI proceeds; used for novel, high-risk, or foundational work
I
- IntegratorSee in paper
- Final validation hat that runs conditionally based on VCS strategy; validates auto-merged state (trunk) or creates single PR (intent); skipped for unit/bolt strategies
- IntentSee in paper
- High-level statement of purpose with completion criteria that serves as starting point for decomposition
K
- Knowledge Artifact
- A structured file in `.ai-dlc/knowledge/` capturing project intelligence in one of five types: design, architecture, product, conventions, domain; persists across intents
- Knowledge Layer
- The collection of knowledge artifacts that accumulate project intelligence over time, serving as persistent cross-intent context for elaboration and execution
M
- Memory ProviderSee in paper
- Source of persistent context (files, git, tickets, ADRs, runbooks) accessible to AI agents
- Mob ElaborationSee in paper
- Collaborative ritual where humans and AI decompose Intent into Units with Completion Criteria
- Mob Execution
- Collaborative ritual where multiple teams build Units in parallel with AI assistance
O
- OHOTLSee in paper
- Observed Human-on-the-Loop: human watches AI work in real-time with ability to intervene; synchronous awareness with asynchronous control; used for creative, subjective, or training scenarios
- Operation
- A file-based operational task spec (`.md` with YAML frontmatter) defining scheduled, reactive, or process-type work with agent or human ownership; stored in `.ai-dlc/{intent}/operations/`
P
- Pass
- A typed iteration through the standard AI-DLC loop (elaborate, units, execute, review) that refines an Intent through a specific disciplinary lens; defined as frontmatter-enabled markdown files with instructions and workflow constraints; three built-in passes (design, product, dev) can be augmented or extended by project-level definitions; passes are optional (zero overhead when unused) and configurable via `default_passes` in settings; output of one pass becomes input to the next
Q
- Quality GateSee in paper
- Automated check (tests, types, lint, security) that provides pass/fail feedback
R
- Ralph Wiggum PatternSee in paper
- Autonomous loop methodology: try, fail, learn, iterate until success criteria met
S
- Stack Config
- Infrastructure stack configuration in `.ai-dlc/settings.yml` describing deployment, compute, monitoring, alerting, and operations layers
U
- UnitSee in paper
- Cohesive, independently deployable work element derived from an Intent; named with numerical prefix + slug (e.g., `unit-01-setup-auth`); can declare dependencies via `depends_on` forming a DAG
- Unit DAGSee in paper
- Directed Acyclic Graph of unit dependencies enabling parallel execution (fan-out) and convergence (fan-in)