Today, AI agents are transforming how we program, and it's not about giving them a single "superprompt", but rather a structured workflow with specialized agents for each task. Modern platforms define specific subagents — each with its own context and permissions — that handle certain types of activity. This way, we can assign one agent only backend tasks, another frontend, or another testing, reducing complexity by quickly identifying where an error occurs. This modular approach ensures each agent fulfills a specific role without interfering with others.
Back-end Development
- Business logic and APIs: A specialized agent can handle writing or reviewing server code — for example, designing REST API routes, managing databases, or implementing complex business logic.
- Service orchestration (BFF): In microservices architectures, it's common to use a backend-for-frontend (BFF) pattern where a central agent coordinates multiple services. This BFF agent can manage the flow between front-end and different microservices, acting as an intelligent integration point.
- AI components in infrastructure: A backend agent's stack includes a language model (the central "brain"), tool integrations (e.g., frameworks, external APIs), short and long-term memory, and configurable roles. Thanks to this, agents operate independently and can collaborate with each other, adapting to changes in real time.
- Resilience and security: Robustness patterns are incorporated into the flow: automatic retries, error monitoring, and control logic. Inputs/outputs must also be validated and sanitized at each stage to prevent attacks (prompt injection) and verify outputs against strict criteria.
Advantages: By delegating specific tasks, we increase speed and precision: a backend agent can specialize in optimizing server code and scalability. Also, by keeping responsibilities isolated, debugging is facilitated. Disadvantages: It requires coordinating multiple agents, adding initial complexity. It also increases architectural overhead and requires investing in monitoring and governance to maintain coherence.
Front-end Development
- Interface and user experience: Agents can analyze UI code (HTML/CSS/JS) and ensure visual consistency. For example, they can scan the entire interface repository to detect discordant buttons or styles and fix them automatically. They also help ensure accessibility by auditing WCAG compliance and suggesting ARIA labels or alt texts where missing.
- Performance optimization: They can review client assets (images, CSS, scripts) and propose improvements (like compression, lazy loading, or modern formats) to speed up loading. Some agents even test A/B variants to measure real impact on users.
- UI code generation and refactoring: Given prototypes or wireframes, an agent could generate basic interface components. Or identify obsolete code: for example, detecting a monolithic React component and refactoring it into reusable pieces.
- Visual collaboration and documentation: They track UI changes, detect conflicts in merge requests, and can even generate component documentation. They function as a dedicated assistant maintaining coherence between designers and programmers.
Advantages: They accelerate repetitive tasks and ensure homogeneity: developers spend less time on style adjustments or accessibility testing, focusing on innovation. Disadvantages: AI can still fail to understand subjective aesthetic aspects or complex user flows, so its output requires human supervision. It also requires clearly defining scope.
End-to-End Testing and Integration
- Automated testing: Agents can be assigned to generate and execute test cases. For example, an agent could read new code and automatically create unit or integration tests according to project standards. The use of dedicated QA agents is even proposed to validate final software results.
- E2E testing: Specialized agents can simulate complete user flows (login, transactions, etc.), connecting frontend and backend. This way they detect integration failures before deployment.
- Early error detection: By continuously running these generated tests, agents identify bugs and generate detailed reports (logs, reproduction steps). This reinforces code quality and speeds up feedback for developers.
Advantages: They expand QA coverage, find errors that might be overlooked manually, and support continuous integration without additional effort. Disadvantages: They can generate redundant tests or false positives if validation criteria aren't well adjusted. It's also necessary to manually review critical findings and update tests as requirements change.
Additional Roles and Agile Methodologies
Beyond coding, AI agents can assume product management and coordination roles. In certain multi-agent frameworks (like MetaGPT), it's proposed that one agent acts as Product Manager, defining requirements and priorities, while other agents architect, develop, and validate. This follows the agile spirit: a digital "Product Owner" could organize the backlog based on user needs. Other agents can function as Scrum Master, tracking sprints, generating meeting minutes, or measuring team metrics.
For example, Atlassian highlights that specialized agents with access to real project context are the most effective. The case of Rovo is cited, an assistant that reads team documentation (Confluence, Jira, goals) and answers questions aligned to organizational context.
Advantages: AI helps maintain focus on business objectives, automating administrative tasks (planning, data analysis) and offering quick insights. This can double productivity: Atlassian reports that strategic teams collaborating with AI achieve up to 2x return on investment and save about 105 minutes of work daily. Disadvantages: However, AI doesn't replace human judgment: final review, user empathy, and creativity remain the responsibility of leaders and developers.
Best Practices and Considerations
To take advantage of this task division, it's key to clearly define each agent's role with precise instructions. Each agent should receive a system prompt and its own constraints, avoiding overlaps (for example, a UI agent shouldn't modify the database). It's recommended to feed agents with sufficient context: technical specifications, code standards, and reference examples.
Additionally, agent performance must be monitored. Logging their actions helps debug issues (traceability) and iterate on prompts. One must also be aware of computational costs: using appropriate models and optimizing prompts reduces token usage and speeds up execution. Finally, even though agents operate autonomously, a human intervention mechanism should always be planned for critical stages.
"The separation of responsibilities between AI agents allows leveraging their specific strengths at each development stage, maintaining agile cycles and high quality."
As these practices mature, technical teams will be able to increasingly focus on the global vision, delegating operational details of each domain to code agents.