Beyond simple autocomplete:
an AI agent that does real software engineering work
It understands developer intent and works across the entire lifecycle—from planning and coding to debugging, refactoring, and documentation.
Every AI coding step, from generation through testing and validation, is logged and auditable.
Repetitive work drains engineering capacity
Repetitive coding, testing, and maintenance fragment developer focus and consume time meant for core work.
Understanding legacy code takes too long
À mesure que les projets prennent de l’ampleur, le coût de la découverte et de la compréhension du code ainsi que de l’intégration augmente. Les nouveaux membres de l’équipe mettent trop de temps à apporter des contributions significatives.
You cannot explain what the AI did
Conventional AI coding tools show only the result. They do not reveal why the code was created or trace how the work was performed, making them a poor fit for enterprise audit and security policies.
Natural-language code generation
Describe the code you need in plain language. Coding Agent understands the project context and generates code that follows the existing style. You can set the scope anywhere from a single function to an entire module.
Project-wide code analysis and discovery
It indexes the full codebase and maps its structure. Even across hundreds of thousands of legacy lines, it quickly identifies dependencies, call flows, and key modules to shorten onboarding.
Modifications et refactorisation du code
Il va au-delà des simples modifications pour améliorer la qualité, les performances et la lisibilité du code. Les changements sont présentés clairement, et le code concerné est examiné en parallèle.
Test generation and execution
After writing code, it generates and runs tests immediately. For failures, it analyzes the cause and iterates on a fix, automating the full path to better coverage.
Analyse des erreurs et mesures correctives
It analyzes error messages and stack traces to identify root causes, then recommends structural improvements that prevent recurrence—not just a one-off patch.
Git workflow support
Il crée des branches, rédige des messages de commit et prépare des descriptions de demandes de tirage, en s’intégrant naturellement sans modifier votre flux de travail Git existant.
Inventaire consolidé et validation de l’état des paiements dans ApproveAsync(). The flow also emits an admin notification event after approval.
[BUG] order.Payment est nul pour une commande, mais le code y accède directement order.Payment.Status. A pending or manually approved order could therefore cause the approval API to fail with a NullReferenceException.
return order.Payment != null
&& order.Payment.Status == PaymentStatus.Paid
&& order.StockReserved;As Review AI noted, some orders have no payment information. Add a null check and include a manual-approval test case in OrderApprovalServiceTests.
Révision des demandes d’extraction par l’IA
The moment a developer opens a pull request, Review AI analyzes the changes. It flags risky code in the actual project files and leaves actionable suggestions between the existing code review and senior review.
Standardized review criteria
Apply consistent review standards across the organization instead of relying on individual reviewers. Pull requests are automatically checked against internal conventions, architecture principles, and security policies.
Fewer review bottlenecks
AI handles repetitive mechanical checks first, freeing developers to focus on design decisions and critical logic. Senior review workload falls while delivery speeds up.
Assign coding work in one natural-language sentence
Give an implementation, refactoring, or bug-fix request in natural language. Coding Agent understands the existing code context and handles the work from planning through validation—not merely completion.
It analyzes and understands the entire project structure
It analyzes the full internal codebase and works from that context, accelerating legacy-code discovery, onboarding, and dependency analysis.
It automates testing from authoring through execution
After a code change, it automatically generates tests, runs them, and analyzes errors, reducing testing time while supporting consistent quality.
It works inside your real development workflow
Les opérations Git, la gestion des branches et la rédaction des messages de validation s’intègrent naturellement à l’environnement de développement existant, sans nouveau flux de travail à apprendre.
Coding Agent logs the entire work process: who issued each instruction, how the AI reasoned, and how the output was produced.
Full-process logging
Every step—from the natural-language instruction to code changes and test execution—is timestamped and available for later review.
Trace who issued each instruction
Administrators can review AI usage, instructions, and generated code by team member, with role-based access controls that enforce security policy.
Audit the evidence behind AI decisions
Evidence explaining why the AI produced the code and which context informed its decision is stored for code review and post-event audit.
Autocomplete tools and agentic AI are fundamentally different. Coding Agent is not a tool that recommends code; it is an agent that performs software engineering work.
| Comparaison | GitHub Copilot / Cursor | General AI coding tool | Coding Agent |
|---|---|---|---|
| How work is performed | Code suggestions · Autocomplete | Génération d’un seul fichier | Planning, execution, and validation |
| Project-context awareness | Limited (current file only) | Limited | Full-codebase analysis |
| Automated testing and validation | Not supported | Not supported | Generation, execution, and error correction |
| Audit logs | None | None | Complete work history |
| Suivi des éléments probants des décisions de l’IA | None | None | Auditable with decision evidence |
| On-premises deployment | Unavailable (cloud only) | Limited | Prise en charge complète sur site |
| Long-running work | One-off response | One-off response | Built on an agentic architecture |
| Permission and access management | Individual only | Limited | Gestion par équipe et par rôle |
The era of code-suggestion tools is giving way
Les premiers outils de codage par IA se concentraient sur la saisie semi-automatique. Le marché évolue rapidement vers une IA agentique qui comprend les projets dans leur ensemble et prend en charge le travail de la planification aux tests et à la validation.
For enterprise adoption, security matters as much as performance
Les institutions financières, les organismes du secteur public et les grandes entreprises sont de plus en plus préoccupés par l’IA SaaS externe. Les environnements de développement sur site, les journaux d’audit et la gestion des accès deviennent des exigences déterminantes.
Coding Agent performs the full process, from planning through coding, testing, validation, and error correction.
The key differences are its ability to understand the whole project, carry out long-running work, and record every step in an auditable log.
Administrators can review and export the complete AI usage history for each team member.
Le code et les données restent dans votre environnement, avec les mêmes fonctionnalités disponibles sur un réseau interne.
It can be deployed in financial institutions, public agencies, and enterprises with strict security policies.
It fits into Git-based workflows without requiring changes to the existing development process.
Our technical team assists with environment setup during implementation.
Language coverage continues to expand. Contact us if your organization requires a particular language.
AI usage scope and audit access can be separated for developers, senior engineers, team leads, administrators, and other roles.
Team-level usage-history reports are also available.
Comparez la productivité avant et après l’adoption pour mesurer le retour sur investissement.