Glossary
61 terms (major concepts & methodologies)
A
METHODOLOGIE
> ACE
ACE — an agent context-engineering method reported to improve agent accuracy by about 10.6% and finance-task accuracy by 8.6% while cutting latency by roughly 87%. It illustrates how structuring the context supplied to an agent, rather than changing the model, can lift measured performance.
METHODOLOGIE
> ADLC
Agentic Development Lifecycle — a proposed software lifecycle designed around the properties and failure modes of AI models rather than human teams. Each phase maps to a defended failure mode or an exploited model property, structured as phases and verification gates. Cited as a convergent framework reached independently by several practitioners.
CONCEPT
> AEO
Answer Engine Optimization — the practice of structuring content so AI answer engines cite it when responding to queries. It adapts search-optimization thinking to a world where users read a generated answer rather than a list of links, prioritizing citable, well-attributed statements over keyword ranking alone.
METHODOLOGIE
> Agentique adaptative
An architectural approach for putting agentic AI into production, organized around four pillars. It addresses how autonomous agents are structured, supervised, and adapted so they keep working reliably under real operating conditions — with edge cases, drift, and load — rather than only in controlled demonstrations.
METHODOLOGIE
> agents parallèles
A working pattern in which many agent instances run at once on the same effort — reported at up to 16 simultaneous agents across roughly 2,000 coding sessions. Running agents in parallel raises throughput and lets independent sub-tasks proceed together, at the cost of coordination and oversight.
CONCEPT
> AGI
Artificial General Intelligence — a system able to match or exceed human capability across most cognitive work rather than one narrow task. Framed by leading AI companies as their stated goal, often defined by benchmarks such as PhD-level research or self-improvement. Whether current approaches can reach it is contested.
METHODOLOGIE
> AI-Assisted Engineering
The methodical integration of AI into a mature software development lifecycle, aimed at code that stays secure, scalable, and maintainable. It positions AI as one disciplined stage within established engineering practice rather than a replacement for review, testing, and design.
CONCEPT
> AI brain fry
Mental fatigue from excessive use or oversight of AI tools beyond one's cognitive capacity. Reported symptoms include a buzzing sensation, mental fog, and slower decision-making. The term names a human cost of continuous agent supervision, distinct from the productivity gains usually emphasized.
CONCEPT
> AI slop
Low-effort AI-generated output produced without real understanding or review — in software, code that compiles and may pass tests yet degrades a codebase's clarity and long-term quality. The term is pejorative, marking the gap between genuine engineering and volume generation, and warns against accepting agent output uncritically.
CONCEPT
> AI4Ops
The application of AI to IT operations, oriented toward autonomous operation of infrastructure and services. It extends the automation of monitoring, incident response, and remediation, so that operational tasks are increasingly handled by agents rather than triggered manually by an on-call engineer.
METHODOLOGIE
> approche spec-driven IA
A spec-driven method for AI-assisted development structured in stages: onboarding, atomic planning, iterative development, and capitalization. Work is anchored on an explicit specification the agent follows, so intent is fixed up front and progress accumulates into reusable knowledge rather than one-off output.
METHODOLOGIE
> augmented coding
AI-assisted coding that keeps quality, testing, and coverage as first-order priorities. It contrasts with looser styles that accept generated output uncritically: the developer stays responsible for correctness, using the agent to move faster without ever lowering the bar on verification and review.
CONCEPT
> Augmented Craftsman
A developer augmented by AI who nonetheless stays in the code — reviewing, shaping, and owning the result rather than delegating it wholesale. The term marks a stance that keeps human craft and judgment central even as agents handle more of the mechanical work.
B
METHODOLOGIE
> BMAD
BMAD (Breakthrough Method for Agile AI-Driven Development) — an agile methodology for AI-assisted software work, described through the metaphor of an urban plan for agentic AI. It structures how agents and humans collaborate across a project so that autonomous work stays coordinated and directed rather than ad hoc.
METHODOLOGIE
> Boucle de codage agentique
The short, minutes-scale loop in which a coding agent works against a product spec and evaluation set: it acts, its output is checked, and it iterates. Treating this loop — rather than a single prompt — as the unit of work is central to reliable agent-driven development.
C
CONCEPT
> commerce agentique
Commerce conducted by AI agents acting on behalf of consumers — searching, comparing, and completing purchases with limited human involvement. Described as an emerging and accelerating category, it shifts the buyer from a person browsing to an agent transacting, reshaping how products are discovered, priced, and sold online.
CONCEPT
> Compaction
The automatic summarization of a conversation that replaces accumulated history with a condensed version, freeing context space while preserving intent. It is a common countermeasure to context rot, letting long agent sessions continue without the earlier turns crowding out what still matters.
METHODOLOGIE
> Compound Engineering
An engineering practice in which each unit of work also improves the system that produces future work: lessons, tests, prompts, and tooling are captured as durable artifacts so quality and speed compound over time instead of resetting with each task. Discussed mainly in the context of AI-assisted software development.
METHODOLOGIE
> Compounding Knowledge Lifecycle
A cycle of capture, storage, retrieval, and refresh that makes organizational knowledge composable over time. Each pass adds durable, reusable artifacts so that later work builds on earlier work instead of restarting, echoing the compounding logic applied to teams and engineering.
CONCEPT
> compounding teams
Teams that no longer write code directly but build recursive frameworks around models — tooling, prompts, and processes that make each future task cheaper and better. The label captures a shift in where engineering effort goes: into the system that produces work, not the work itself.
METHODOLOGIE
> context engineering
The discipline of deliberately assembling, structuring, and pruning the information given to a language model — instructions, retrieved documents, code, and history — so the limited context window carries exactly what a task needs. Treated as a first-class engineering concern, distinct from the wording of a single prompt.
CONCEPT
> Context Flywheel
The compounding effect by which iteratively curated context improves each successive agent task: better context yields better output, which in turn enriches the context for the next task. It frames context as an asset that accrues value rather than input assembled fresh each time.
CONCEPT
> Context Rot
The gradual degradation of a model's output quality as its context window fills with accumulated, partly irrelevant history: earlier instructions get diluted, contradictions creep in, and attention spreads thin. Motivates practices such as compaction, summarization, and starting fresh sessions to keep context dense and relevant.
METHODOLOGIE
> cycle SFEIR à 11 phases
An AI-driven software development lifecycle organized in eleven phases, numbered 0 to 10, with three human gates and two capitalization points. It formalizes where humans intervene in an otherwise agent-run process, and where knowledge is captured for reuse across future projects.
D
CONCEPT
> dette technique
The accumulated cost of expedient code and design choices that must later be reworked — interest paid in slower change and more defects. In AI-assisted development it recurs as a specific risk: agents can generate large volumes of code that passes tests yet erodes long-term quality when generation outpaces review.
METHODOLOGIE
> DICE
DICE (Domain-Integrated Context Engineering) — an extension of context engineering that folds an explicit domain model into how inputs and outputs are structured for a language model. By encoding domain rules into the context, it aims to make agent behavior more predictable on specialized tasks.
F
CONCEPT
> Floating platform
A platform-evolution strategy: as the underlying platform absorbs a capability, the now-redundant custom pieces are discarded and the build point is raised to the next layer of value. It keeps a product riding above commoditized foundations instead of maintaining what the platform already provides.
METHODOLOGIE
> framework 6 étapes
A six-step framework for building AI-assisted solutions: define, design a standard operating procedure, build an MVP, connect, test, and deploy. It gives a repeatable path from intent to a running system, keeping design and verification explicit at each step rather than improvised.
G
METHODOLOGIE
> GDPval
A benchmark measuring AI models on expert-level professional tasks across fields such as finance, law, retail, and software, graded blind by specialists with years of experience. Reported scores reach 40-49% of expert level but require extensive human framing, so the metric itself is seen as under-specified.
METHODOLOGIE
> git worktrees
A Git feature that checks out several branches into separate working directories from one repository, used to isolate parallel tasks on the same codebase. It lets multiple agents or experiments run side by side without their changes colliding, then merge back independently.
CONCEPT
> Goût développeur
A developer's judgment about what a good solution looks like before writing it, together with the discipline to pursue that standard. In AI-assisted work it gains weight: when generation is cheap, knowing which output is worth keeping becomes the scarce, human contribution.
METHODOLOGIE
> grill-with-docs
An engineering skill for upstream design, DDD-flavored, that works by a sequential interview guided by four principles — interview, precision of language, evidence, and iteration. It draws out requirements and constraints before code is written, so design decisions rest on stated evidence rather than assumption.
H
CONCEPT
> Harness
The scaffolding layer around a language model that turns it into a working agent — tools, prompts, memory, execution environment, and safety checks. A common formulation holds that the model is a small part of an agent's practical capability and the harness the larger part, making it a distinct engineering object.
METHODOLOGIE
> Harness engineering
The building and tuning of the scaffolding around a language model that turns it into a working agent: tools, file access, execution environment, feedback loops, and safety checks. The harness — not the raw model — determines much of an agent's practical capability, making it a distinct engineering activity.
I
K
L
CONCEPT
> loi de Goodhart
Goodhart's law — when a measure becomes a target, it ceases to be a good measure, because optimizing the metric distorts the behavior it was meant to track. It is a caution for AI development: benchmarks and productivity metrics can be gamed once teams steer directly at them.
METHODOLOGIE
> Loop Engineering
The design and tuning of the iterative cycle an autonomous coding agent runs — plan, act, observe, correct — including how results are fed back, when the loop terminates, and how errors are recovered. The loop, rather than any single prompt, is treated as the core unit to engineer.
O
METHODOLOGIE
> Orchestration d'agents
The practice of directing multiple AI agents through a task: chaining them, tracking their state, and recovering when one fails. It requires knowing agents' failure modes to compose them reliably. Practitioners note the open-source layer for registry, lifecycle, permissions, and skills is still largely missing.
METHODOLOGIE
> orchestration multi-agents
The coordination of several specialized agents working toward one goal — routing tasks between them, managing shared state, recovering from errors, and running independent work in parallel. Orchestration gains specialization and throughput beyond a single agent, at the cost of added coordination logic. It recurs in complex agent-native workflows.
CONCEPT
> Outcome-based pricing
A pricing model in which software is billed for the work or results it delivers rather than for seats or licenses. Discussed as a likely shift for AI products, where autonomous agents perform tasks that were previously labor: revenue moves from fixed per-user fees toward the economics of operations and outcomes.
P
CONCEPT
> Paradoxe de Jevons
An economic observation from William Stanley Jevons (1865): greater efficiency in using a resource can raise total consumption rather than lower it, because falling cost expands demand. Applied to AI-assisted development, it suggests that making code far cheaper to produce may increase — not reduce — the total volume written and maintained.
CONCEPT
> personal software
A single-purpose application created by and for one person, fitted to their precise need rather than a general market. Cheap AI generation makes such throwaway, tailored software newly practical, shifting some building from shared products toward disposable tools an individual makes for themselves.
METHODOLOGIE
> Phase Build
A two-hour block of autonomous construction in which a candidate builds with AI tools and frameworks of their choice. Used in assessment, it observes how a developer directs agents under time pressure rather than testing recall, foregrounding judgment and workflow over syntax.
METHODOLOGIE
> pipeline de vérification adversariale multi-agents
A verification pattern combining a generator agent, independent reviewers, an automated check (tests or formal methods), and consensus by vote. By pitting agents against one another before accepting a result, it aims to catch errors that a single agent would confidently pass.
METHODOLOGIE
> Plan mode
An agent operating mode that separates planning from execution: the agent first proposes a step-by-step plan for a human to review and approve, and only then carries it out. It reduces wasted or unsafe actions on complex tasks by front-loading intent and human oversight before any change is made.
METHODOLOGIE
> procédure infographique
A working procedure built around infographic-quality visual presentation, cited with reference to Steve Jobs's obsession with perfection. It treats the clarity and finish of a visual artifact as part of the method itself, not decoration added after the substance is settled.
METHODOLOGIE
> Programme de tutorat IA
A structured AI-mentoring program running six weeks, with twelve ninety-minute sessions held twice a week. It formalizes how practitioners are trained to work with AI tools over time, treating adoption as a taught skill rather than something picked up incidentally.
METHODOLOGIE
> PROJ-AI
PROJ-AI — a lightweight methodological layer that makes collective projects transmissible through a repository, an agent, and a shared doctrine. It turns projects into reusable artifacts, so that method and context carry over between teams instead of being rebuilt each time.
S
CONCEPT
> SDLC
The Software Development Life Cycle — the sequence of phases through which software is defined, built, verified, deployed, and maintained. Traditionally designed around human teams and largely invariant, it is the reference frame against which AI-era changes are measured as agents compress, reorder, or automate individual phases.
CONCEPT
> SecNumCloud
A high-level French security qualification for cloud services, defining the requirements a provider must meet to host sensitive workloads. It functions as a trust and sovereignty benchmark, shaping which platforms are eligible for regulated, public-sector, or otherwise security-critical use in France.
CONCEPT
> skills
A harness primitive that packages reusable agent capability as persistent, shareable files — commonly Markdown (SKILL.md) — loaded on demand. Skills implement progressive disclosure: instructions and tools enter the context only when needed, keeping it dense and guarding against context rot. They make agent behavior testable and portable.
METHODOLOGIE
> Software Factory
Non-interactive development driven by specifications and scenarios, without human intervention in the loop — reported at a scale of about $1,000 in tokens per human engineer per day. It frames software production as an automated pipeline where humans set specs and agents execute them.
CONCEPT
> subagents
Specialized secondary agents that a primary agent spawns to handle a bounded sub-task — searching, reviewing, or transforming — each with its own context and tools. Delegating to subagents keeps the parent agent's context focused and lets independent pieces of work run in parallel.
T
METHODOLOGIE
> Tension Map
A mapping of a market's contradictions and pressure points — rather than its market shares — used to reveal opportunity spaces. By locating where forces pull against each other, it surfaces openings that a share-based view of competition would miss.
CONCEPT
> token
The base unit of generative AI processing and cost — a short chunk of text, image, or audio a model reads or produces, roughly a syllable of text. Pricing, context limits, and spend are counted in tokens, which makes it an emerging economic unit: as its cost falls, what is worth generating changes.
U
V
METHODOLOGIE
> vibe coding
A programming style, named by Andrej Karpathy, in which a developer drives an AI agent mostly through natural-language intent and accepts its output without closely reading every line, iterating by feel rather than by manual editing. Effective for prototypes; risky for production code that requires careful review.
METHODOLOGIE
> Vibe Reviewing
Code review assisted by AI agents but validated by rigorous human sign-off. It parallels vibe-coding on the review side: agents surface issues and assessments at speed, while a person keeps final responsibility for what is accepted into the codebase and what is sent back.
W
METHODOLOGIE
> Wardley Mapping
A visual strategy technique that maps a value chain against the evolution of its components, from novel to commoditized. It helps teams see where to build, buy, or outsource, and is applied to reason about positioning in fast-moving AI tooling markets.
METHODOLOGIE
> workflow IA Wardley
A workflow that automates the production of Wardley maps with AI assistance, turning strategic mapping from a manual exercise into a repeatable, tool-supported step. It lowers the effort of keeping a value-chain map current as market conditions and component maturity change over time.