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Core Capability

Multi-Agent Architecture

Specialized AI agents for planning, coding, reviewing, fixing, investigating, and documenting

What Is Multi-Agent Architecture?

Traditional coding assistants use a single AI model to handle all tasks. SiftCoder's multi-agent architecture is different: we use six specialized AI agents, each with specific expertise and capabilities.

Just as a software team has specialists for different roles (frontend, backend, QA, documentation), SiftCoder has specialized agents that collaborate to produce higher quality results than any single agent could achieve alone.

Specialized Expertise

Each agent excels at its specific task, producing better results than a generalist

Automatic Collaboration

Agents work together seamlessly, handing off tasks and building on each other's work

Quality Assurance

Multiple review points ensure code quality before completion

Continuous Improvement

Agents learn from codebase patterns and improve over time

Meet the Six Agents

Each agent has specific capabilities and works with others to deliver complete solutions

Planner Agent

Analyzes requirements and creates implementation plans

Capabilities

  • •Analyzes specifications and requirements
  • •Breaks down features into implementable tasks
  • •Identifies dependencies and potential risks
  • •Creates detailed step-by-step plans
  • •Optimizes for codebase patterns and conventions

Workflow

Spec Analysis → Feature Breakdown → Task Planning → Risk Assessment

Coder Agent

Implements code following detected patterns

Capabilities

  • •Writes clean, maintainable code
  • •Follows project conventions exactly
  • •Implements tests for all functionality
  • •Uses appropriate design patterns
  • •Generates type-safe code

Workflow

Task Understanding → Pattern Detection → Code Generation → Test Creation → Quality Check

QA Reviewer Agent

Validates implementations against requirements

Capabilities

  • •Validates acceptance criteria
  • •Runs comprehensive test suites
  • •Identifies code quality issues
  • •Checks for edge cases
  • •Ensures documentation completeness

Workflow

Acceptance Validation → Test Execution → Issue Detection → Quality Assessment → Report Generation

QA Fixer Agent

Fixes identified issues automatically

Capabilities

  • •Fixes bugs and errors
  • •Addresses quality issues
  • •Re-runs tests to validate fixes
  • •Ensures no regressions
  • •Maintains code quality standards

Workflow

Issue Analysis → Solution Planning → Fix Implementation → Re-testing → Validation

Investigator Agent

Safely explores codebases to understand behavior

Capabilities

  • •Read-only codebase exploration
  • •Understands code behavior
  • •Investigates bugs and issues
  • •Analyzes dependencies
  • •Provides detailed diagnostics

Workflow

Issue Definition → Safe Exploration → Code Analysis → Root Cause Identification → Detailed Report

Documenter Agent

Generates comprehensive documentation

Capabilities

  • •Generates API documentation
  • •Creates user guides
  • •Writes code comments
  • •Produces architecture diagrams
  • •Maintains documentation currency

Workflow

Code Analysis → Content Extraction → Documentation Generation → Review → Publication

How Agents Collaborate

1

Specification Input

User provides a specification, issue, or feature request

Agent: None

The process begins with human input defining what needs to be done

2

Planning Phase

Planner Agent analyzes requirements and creates detailed plan

Agent: Planner Agent

Breaks down the work into implementable tasks with clear acceptance criteria

3

Implementation Phase

Coder Agent implements the planned tasks

Agent: Coder Agent

Writes code following detected patterns, includes tests and documentation

4

Review Phase

QA Reviewer Agent validates the implementation

Agent: QA Reviewer Agent

Runs tests, checks quality, validates acceptance criteria

5

Fix Phase (if needed)

QA Fixer Agent addresses any issues found

Agent: QA Fixer Agent

Fixes bugs, quality issues, and re-runs tests to validate

6

Completion

Feature is complete and ready for use

Agent: All Agents

High-quality, tested, documented code ready for production

Real-World Example: Building a User Authentication Feature

Planner Agent

⏱️ 5 minutes
Feature plan with 5 tasks: 1. Design user database schema 2. Create authentication API endpoints 3. Build login/register UI components 4. Implement session management 5. Add password reset functionality

Coder Agent

⏱️ 15 minutes
Complete implementation: - User model with TypeScript interfaces - REST API endpoints (/auth/login, /auth/register) - React components (LoginForm, RegisterForm) - JWT token validation middleware - Unit tests for all components - Integration tests for API

QA Reviewer Agent

⏱️ 3 minutes
Validation results: ✓ All acceptance criteria met ✓ Tests passing (47/47) ✓ Code quality: A ✓ Security: OWASP compliant ⚠ Missing: Rate limiting on login endpoint

QA Fixer Agent

⏱️ 5 minutes
Issue resolved: + Added rate limiting middleware + Re-ran tests: 47/47 passing + Verified no regressions + Final validation: ✓ Complete

Total Time: 28 minutes

Traditional approach would take 2-3 days. Multi-agent architecture completes it in under 30 minutes with higher quality and comprehensive testing.

Key Benefits

Faster Development

10x faster than manual coding with parallel agent execution

  • Tasks done in parallel
  • No context switching
  • Continuous workflow

Higher Quality

Multiple review points catch issues early

  • Automated testing
  • Code reviews
  • Quality gates

Consistency

Agents follow detected patterns exactly

  • Code conventions
  • Design patterns
  • Style guide compliance

Scalability

Handle multiple features simultaneously

  • Parallel execution
  • Resource optimization
  • Load balancing

Experience Multi-Agent Architecture

See how six specialized AI agents can transform your development workflow

Try It Now