Glossary
Agentic Workflows and Harnesses
Section titled “Agentic Workflows and Harnesses”Multi-step model workflows, harness responsibilities, state, memory and reusable workflow patterns.
AI Engineering
Section titled “AI Engineering”Designing, building, evaluating, deploying and improving software systems in which AI models perform part of the system work.
AI Suitability
Section titled “AI Suitability”Assessing whether AI belongs in the task and whether errors are detectable, tolerable and recoverable.
Architecture and Interfaces
Section titled “Architecture and Interfaces”Boundaries, contracts, integration and structural decisions that make AI systems changeable.
Code Quality and Version Control
Section titled “Code Quality and Version Control”Maintainability, readable diffs and reversible change in human and agent-written code.
Coding Assistants Versus Coding Agents
Section titled “Coding Assistants Versus Coding Agents”The difference between local code help and goal-directed multi-step agent work.
Configuration, Permissions and Sandboxing
Section titled “Configuration, Permissions and Sandboxing”Model configuration, MCP integrations, execution boundaries, approvals and secrets.
Context Engineering
Section titled “Context Engineering”Designing the complete information environment available to a model at a particular step.
Context, Instructions and Planning
Section titled “Context, Instructions and Planning”Managing finite context, persistent project guidance, skills and adaptive plans.
Coordination Risks and Concurrency Safety
Section titled “Coordination Risks and Concurrency Safety”Cost, context, communication, conflict and stopping-condition risks in multi-agent work.
Data Modelling and Data Engineering
Section titled “Data Modelling and Data Engineering”Representing entities, relations, events and lifecycle so AI has meaningful data to work with.
Deep Learning
Section titled “Deep Learning”Machine learning using multilayer neural networks, underpinning modern vision, speech, multimodal systems and LLMs.
Delegation, Handoffs and Orchestration
Section titled “Delegation, Handoffs and Orchestration”How orchestrators assign, combine and account for work across agents.
Deterministic-Probabilistic Boundaries
Section titled “Deterministic-Probabilistic Boundaries”Deciding what belongs in rules or conventional code and what can responsibly use a model.
DevOps, Deployment and Observability
Section titled “DevOps, Deployment and Observability”Build, release and operations practices for observable and recoverable AI software.
Embeddings, Vector Search, Graphs and Semantic Layers
Section titled “Embeddings, Vector Search, Graphs and Semantic Layers”Retrieval building blocks and their trade-offs for semantic, structured and relationship-heavy information.
Evals and Error Analysis
Section titled “Evals and Error Analysis”Structured tests and qualitative inspection for improving probabilistic AI-system behaviour.
Evaluating Coding Agents
Section titled “Evaluating Coding Agents”Evidence dimensions for judging agent output, process quality, safety and robustness.
Evidence-based completion
Section titled “Evidence-based completion”Declaring success based on concrete evidence such as passing checks, inspected diffs or rendered behaviour.
Foundation Models and LLMs
Section titled “Foundation Models and LLMs”Foundation models, LLMs, multimodal models and model selection as production engineering choices.
Governance and Human Decision Boundaries
Section titled “Governance and Human Decision Boundaries”Where humans retain authority over risk, irreversible action and release decisions.
Grounding and RAG
Section titled “Grounding and RAG”Connecting model output to trusted, task-relevant sources or system state.
Human Oversight and Guardrails
Section titled “Human Oversight and Guardrails”Controls that constrain AI inputs, outputs or actions and keep human authority meaningful.
Machine Learning
Section titled “Machine Learning”Methods that learn patterns from data instead of expressing every rule explicitly.
Model, Harness, Tools and Workspace
Section titled “Model, Harness, Tools and Workspace”The practical components that determine what a coding agent can do.
Multi-Agent Patterns
Section titled “Multi-Agent Patterns”Fan-out/fan-in, pipelines, reviewer patterns and debate patterns.
Multiple Agents and Subagents
Section titled “Multiple Agents and Subagents”When separate agents help and when a single agent is the better engineering choice.
Outcomes and Learning Loops
Section titled “Outcomes and Learning Loops”Outcome metrics, guardrail metrics, build-measure-learn loops and decision records.
Principles for Effective Use
Section titled “Principles for Effective Use”Operational principles for getting useful, reviewable and reversible work from coding agents.
Problem Framing and Stakeholder Analysis
Section titled “Problem Framing and Stakeholder Analysis”Defining the situation, affected people, desired change and competing interests before selecting an AI solution.
Product Hypotheses and Prototypes
Section titled “Product Hypotheses and Prototypes”Testing falsifiable product hypotheses with prototypes and vertical slices.
Production Operations, Observability, Cost and Latency
Section titled “Production Operations, Observability, Cost and Latency”Running AI systems under real deployment, tracing, reliability, cost and performance constraints.
Prompt Engineering
Section titled “Prompt Engineering”Designing instructions and examples that elicit useful model behaviour.
Requirements and Design
Section titled “Requirements and Design”Requirements engineering, computational thinking and design for AI-supported systems.
Risk and Responsible AI
Section titled “Risk and Responsible AI”Assessing hazards and designing accountable AI systems with meaningful human oversight.
Security, Privacy and Accessibility
Section titled “Security, Privacy and Accessibility”Security, privacy and inclusive design constraints for AI-supported applications.
Specifications, Quality Attributes and Acceptance Criteria
Section titled “Specifications, Quality Attributes and Acceptance Criteria”Turning desired behaviour, constraints and quality into verifiable conditions.
Task Briefs and Examples
Section titled “Task Briefs and Examples”A reusable task brief structure for bounded, verifiable agent work.
Technical Debt, Documentation and Teamwork
Section titled “Technical Debt, Documentation and Teamwork”Managing future cost, decision records, collaboration and review in fast-moving AI projects.
Testing and Testability
Section titled “Testing and Testability”Traditional tests for deterministic behaviour and evals for probabilistic behaviour.
The Agent Loop
Section titled “The Agent Loop”Observe, reason, act, inspect, update and stop or continue.
The Ten-Step Working Method
Section titled “The Ten-Step Working Method”A readable process for directing coding agents from task framing through shipping and observation.
Tool Calling and MCP
Section titled “Tool Calling and MCP”Allowing models to request controlled operations through validated tools and standard interfaces.
User and Workflow Research
Section titled “User and Workflow Research”Understanding tasks, handoffs, exceptions and user progress before changing a workflow.
Value, Feasibility, Viability and Responsibility
Section titled “Value, Feasibility, Viability and Responsibility”Four tests for deciding whether a shaped AI intervention deserves further investment.