Glossary
Short definitions of the terms that come up in AI-native software delivery. Every entry links to the guide or article that covers it in full.
AI agent
Software that can take a sequence of actions toward a goal with limited step-by-step human direction, as opposed to a single AI call that answers one prompt and stops. Moving an agent from a working demo to production usually means adding guardrails the demo never needed.
AI-native deliveryAI-native delivery
A way of building software where AI writes the code by default and a spec plus an eval suite, not a person, verify that every change does what it was supposed to do. It differs from AI-assisted development, where a human still writes most of the code with AI as a helper.
AI-native deliveryEval-driven development
A practice where an automated eval suite, written from the specification before the change ships, is the thing that proves AI-generated code works. It plays the role a senior reviewer used to play, but it checks the same rules every time instead of remembering them selectively.
Cost and scopeFixed price vs time and materials
The two common ways to pay for software work. Fixed price sets one number for a defined scope and tends to punish change. Time and materials pays for the hours worked and adapts more easily, but depends on trusting the team doing the work.
AI products and agentsHallucination
A confident-sounding answer from an AI model that is factually wrong or fabricated. Reducing it in a product usually means constraining what the model is allowed to answer from, not just prompting it to be more careful.
ComplianceHIPAA
The US federal law governing how healthcare software must protect patient health information, covering who can access it, how it is transmitted, and what happens when a breach occurs. It shapes the technical design of the software, not just a company's paperwork.
AI products and agentsHuman in the loop
A design decision to have a person review or approve an AI system's output before it takes effect, usually reserved for the actions where a wrong AI decision would be expensive or hard to reverse.
Cost and scopeMVP (minimum viable product)
The smallest version of a product that can prove the core idea is worth more investment. It is not a stripped-down version of the final product; it is a different, smaller thing built to answer one question as cheaply as possible.
ComplianceNAIC Insurance Data Security Model Law
A model law, adopted in some form by more than 25 US states, that requires insurers to run a written information security program, an annual risk assessment, and a breach notification process. It is the main compliance backbone insurance software has to be built against.
Team and engagementOnshore, nearshore, offshore
Three ways to locate a development team relative to your own time zone and geography: in your own country (onshore), a nearby time zone (nearshore), or a distant one (offshore). Each trades off cost, overlap hours, and communication friction differently.
AI products and agentsRAG (retrieval-augmented generation)
A technique where a system looks up relevant information from a document store before asking a model to answer, so the answer can be grounded in real, current data instead of only what the model learned during training.
Cost and scopeScope creep
The slow, mostly invisible growth of a project's requirements after the budget and timeline were set, usually arriving as many small additions rather than one big decision. It is one of the most common reasons a fixed budget stops matching the actual build.
ComplianceSOC 2
An audit framework that verifies a company's controls over security, availability, and confidentiality of customer data. Financial and healthcare software buyers frequently require a vendor to hold or be working toward a SOC 2 report before they will sign a contract.
Team and engagementSoftware development partner
A team that helps shape the plan for a product and stays accountable for whether it still works after launch, as opposed to a vendor that builds a fixed spec and leaves once it ships.
Team and engagementStaff augmentation
Adding engineers to your existing team and process, under your management, rather than handing a whole project to an outside team. It fills a capacity gap; it does not replace the judgment of a partner who helps shape what gets built.
Cost and scopeTechnical debt
Shortcuts taken to ship faster now that cost more to fix later, the same way a financial loan costs more the longer it goes unpaid. Not every shortcut is a mistake; the question is whether the team can see the debt and choose when to pay it down.
Team and engagementTechnical due diligence
An independent review of a company's codebase, engineering practices, and technical risk, usually done before an investment or acquisition. It answers whether the software can support the plan the business is being valued on.
AI-native deliveryThe AI last mile
The gap between an AI-built demo that works once in front of an audience and a product that holds up under real users, real data, and edge cases nobody scripted. Closing it is usually most of the actual engineering effort in an AI product.
AI-native deliveryThe verification gap
The distance between an AI model producing code quickly and a team being able to prove that code is correct. Generation got fast; verification did not, so the gap is where most of the real engineering work now sits.
ComplianceUnauthorized practice of law (UPL)
A legal restriction on giving specific legal advice without a law license, which shapes what a piece of legal software can and cannot say to a user. States differ on where the line sits, and some now explicitly exclude clearly-labeled software tools from the definition.