AI-SDLC Framework Glossary
AI-SDLC Framework Glossary
Document type: Informative Status: Draft Spec version: v1alpha1
This document defines terms used throughout the AI-SDLC Framework specification. Terms are listed alphabetically. Cross-references to normative sections are provided where applicable.
Terms
A2A (Agent-to-Agent Protocol)
A protocol for inter-agent communication, task delegation, and capability discovery, originally developed by Google and governed under the Linux Foundation's AAIF. AI-SDLC agents publish A2A-compatible Agent Cards for discovery. See agents.md.
Adapter
A standalone module implementing one or more interface contracts for a specific tool. For example, a Linear adapter implements the IssueTracker interface. Adapters are registered via metadata.yaml and discovered from a registry, local directory, or git reference. See adapters.md.
Adapter Binding
A resource of kind AdapterBinding that declares a tool integration as a swappable provider behind a uniform interface contract. See spec.md.
Agent Memory
The five-tier memory model for AI agents: working (ephemeral), short-term (TTL-based), long-term (persistent), shared (cross-agent), and episodic (append-only event history). The storage backend is abstracted by the MemoryStore infrastructure adapter. See agents.md.
Agent Card
An A2A-compatible discovery document published at /.well-known/agent.json describing an agent's name, capabilities, skills, version, and security schemes. See agents.md.
Agent Role
A resource of kind AgentRole that declares an AI agent's identity, capabilities, constraints, and handoff behavior using the Role-Goal-Backstory pattern. See spec.md.
Approval Policy
A stage-level configuration that specifies approval requirements before a Pipeline stage may execute. Approval policies declare whether approval is required, the approval tier, blocking behavior, timeout duration, and the action to take on timeout. See spec.md.
Annotation
A key-value pair in a resource's metadata used for non-identifying information such as build provenance, tooling hints, or operational notes. Annotations are not used for selection or filtering. See spec.md.
Autonomy Level
A numbered tier (0-3) defining the permissions, guardrails, and monitoring intensity for an AI agent. Level 0 (Observer) is read-only; Level 3 (Principal) is autonomous within domain. See autonomy.md.
Autonomy Policy
A resource of kind AutonomyPolicy that declares progressive autonomy levels with quantitative promotion criteria and automatic demotion triggers. See spec.md.
Budget Policy
A Pipeline-level configuration that declares rolling budget constraints (period, amount, alerts) for cost governance. See RFC-0004.
Budget Pressure
A model selection mechanism that automatically routes agents to cheaper models as budget consumption increases. See RFC-0004.
Branching Config
A Pipeline-level configuration that declares branch naming patterns, target branches, and cleanup policy for feature branches created during pipeline execution. See spec.md.
Circuit Breaker
A real-time cost monitoring loop that interrupts agent execution when a cost limit is reached. See RFC-0004.
Complexity Score
A numeric value (1-10) assigned to a task that determines the minimum autonomy level and human involvement required. Used by the routing system to assign tasks to appropriate agents. See autonomy.md.
Condition
A structured status entry in a resource's status.conditions array. Each condition has a type, status (True, False, or Unknown), reason, and timestamps. Conditions represent individual aspects of a resource's observed state. See spec.md.
Credential Policy
A stage-level configuration that specifies JIT (just-in-time) credential scope, time-to-live, and revocation behavior for a Pipeline stage. Credentials are scoped to the minimum permissions needed and automatically revoked on stage completion. See spec.md.
Cost Attribution
The process of tracking costs across dimensions (agent, model, stage, repository, complexity, team) for chargeback and analysis. See RFC-0004.
Cost Policy
An optional Pipeline-level configuration that declares cost boundaries at per-execution, per-stage, and budget levels. See RFC-0004.
Cost Receipt
An extension to provenance metadata that records the total cost breakdown (token cost, cache savings, compute, human review) for an AI-generated artifact. See RFC-0004.
Conformance Level
One of three tiers (Core, Adapter, Full) defining the scope of specification compliance an implementation achieves. See spec.md.
Demotion
Automatic reduction of an agent's autonomy level triggered by a policy violation such as a security incident, excessive rollback rate, or unauthorized access attempt. Demotions include a cooldown period before re-promotion is possible. See autonomy.md.
Failure Policy
A stage-level configuration that defines how a Pipeline handles stage failures. Strategies include abort (stop the pipeline), retry (re-execute up to a limit), pause (suspend for manual intervention), and continue (proceed to the next stage). See spec.md.
EventBus
An infrastructure adapter interface for event publication and subscription. Abstracts the event delivery mechanism (e.g., in-process EventEmitter, NATS, Kafka, cloud pub/sub) behind a topic-based publish/subscribe API. See adapters.md.
Enforcement Level
The strictness of a quality gate: advisory (warning only), soft-mandatory (must pass unless overridden), or hard-mandatory (must pass, no override). See policy.md.
Handoff Contract
A versioned JSON Schema defining the required data structure for inter-agent transitions. Every agent transition produces a typed, validated, auditable artifact conforming to its handoff contract. See agents.md.
Infrastructure Adapter
An adapter that abstracts a runtime infrastructure concern (audit storage, sandboxing, secret management, memory persistence, event delivery) rather than an external SDLC tool. Infrastructure adapters use the same AdapterBinding resource model as SDLC adapters. The five infrastructure interfaces are: AuditSink, Sandbox, SecretStore, MemoryStore, EventBus. See adapters.md.
Interface Contract
A typed API definition for an integration category. SDLC interfaces: IssueTracker, SourceControl, CIPipeline, CodeAnalysis, Messenger, DeploymentTarget. Infrastructure interfaces: AuditSink, Sandbox, SecretStore, MemoryStore, EventBus. Each adapter implements one or more interface contracts. See adapters.md.
Label
A key-value pair in a resource's metadata used for identification, selection, and filtering. Labels enable resource queries and policy targeting. See spec.md.
Model Selection
An AgentRole-level configuration that routes tasks to different models based on complexity score and budget pressure. See RFC-0004.
MCP (Model Context Protocol)
A protocol for connecting AI agents to external tools and data sources, originally developed by Anthropic and governed under the Linux Foundation's AAIF. AI-SDLC adapters can wrap MCP servers. See adapters.md.
MemoryStore
An infrastructure adapter interface providing a key-value persistence backend for the five-tier agent memory model. Abstracts the storage mechanism (e.g., JSON files, Redis, DynamoDB) behind a simple read/write/delete/list API. See adapters.md.
Metadata
The metadata object present on every resource, containing name, namespace, labels, and annotations. See spec.md.
Notification Template
A templated message for Pipeline events such as gate failures, agent errors, or PR creation. Templates specify a target (issue, pr, or both), a title, and an optional body with placeholder variables. See spec.md.
Namespace
A scoping unit within the metadata of a resource, typically corresponding to a team or project. Resource names must be unique within a namespace.
Pipeline
A resource of kind Pipeline that defines a complete SDLC workflow from trigger through delivery, including stages, agent assignments, quality gates, and routing rules. See spec.md.
Promotion
Advancement of an agent's autonomy level after meeting quantitative criteria (minimum tasks, metric thresholds) and receiving explicit approval from designated roles. See autonomy.md.
Pull Request Config
A Pipeline-level configuration that declares conventions for pull request creation, including title templates, description sections, provenance inclusion, and issue-closing keywords. See spec.md.
Provenance
The recorded origin of an AI-generated artifact, including model identifier, tool, prompt hash, timestamp, human reviewer identity, and review decision. See metrics.md.
Quality Gate
A resource of kind QualityGate that defines a policy rule evaluated against development activity with a defined enforcement level. See spec.md.
Reconciliation Loop
The continuous process of observing current state, diffing against desired state, and acting to close the gap. The reconciliation loop is the runtime heart of the AI-SDLC Framework, following the Kubernetes controller pattern. See spec.md.
Resource
A declarative object with five top-level fields: apiVersion, kind, metadata, spec, and status. All AI-SDLC configuration is expressed as resources. See spec.md.
Role-Goal-Backstory
The pattern used by AgentRole resources to define an agent's identity. role is the agent's title, goal is what it aims to achieve, and backstory provides context for the agent's persona. Derived from the CrewAI framework. See agents.md.
Routing Strategy
The method by which tasks are assigned to agents based on complexity score. Four strategies are defined: fully-autonomous, ai-with-review, ai-assisted, and human-led. See autonomy.md.
SecretStore
An infrastructure adapter interface for secret resolution and management. Abstracts the secret storage mechanism (e.g., environment variables, HashiCorp Vault, AWS Secrets Manager) behind a get/set API. See adapters.md.
Secret Reference
A secretRef object used in resource specs to reference sensitive values (API keys, tokens) without embedding them directly. The referenced secret is resolved at runtime by the implementation. See spec.md.
Skill
A declared capability of an Agent Role with an ID, description, tags, and examples. Skills enable Agent Card discovery and task routing. See agents.md.
Spec/Status Split
The separation of user intent (spec) from system-observed reality (status) in every resource. spec represents desired state; status represents what the system observes. Controllers continuously reconcile the gap. See spec.md.
Transaction Limit
A guardrail field in AutonomyPolicy that specifies maximum cost per time period. See autonomy-policy.schema.json.