Agent Workspace

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# Git workflow configuration
branching:
  main:
    protection:
      - required_reviews: 2
      - status_checks: [build, test, security-scan]
      - dismiss_stale_reviews: true
    
  development:
    auto_merge:
      - feature branches with passing tests
      - dependency updates from Dependabot
    
  feature:
    naming: "feature/{agent-name}/{feature-description}"
    lifecycle:
      - create_from: development
      - merge_to: development
      - auto_delete: true
    
  hotfix:
    naming: "hotfix/{issue-number}/{description}"
    lifecycle:
      - create_from: main
      - merge_to: [main, development]

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# Intelligent commit message generation
class SmartCommit:
    def generate_message(self, changes):
        analysis = self.analyze_changes(changes)
        
        if analysis.type == "agent_creation":
            return f"feat(agents): add {analysis.agent_name} with {analysis.capabilities}"
        elif analysis.type == "tool_update":
            return f"fix(tools): improve {analysis.tool_name} error handling"
        elif analysis.type == "workflow_modification":
            return f"refactor(workflows): optimize {analysis.workflow_name} performance"
        
        # Fallback to AI-generated message
        return self.llm.generate_commit_message(changes)

# Automated merge conflict resolution
class MergeAssistant:
    def resolve_conflicts(self, conflicts):
        for conflict in conflicts:
            if self.is_agent_config(conflict.file):
                resolution = self.merge_agent_configs(
                    conflict.ours, 
                    conflict.theirs
                )
            elif self.is_workflow(conflict.file):
                resolution = self.merge_workflows(
                    conflict.ours,
                    conflict.theirs
                )
            else:
                resolution = self.ai_resolve(conflict)
            
            yield resolution

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# Agent definition with embedded documentation
agent:
  name: "sales-assistant"
  version: "2.1.0"
  description: |
    Intelligent sales assistant that handles lead qualification,
    product recommendations, and order processing.
  
  capabilities:
    - name: "qualify_lead"
      description: "Evaluates lead potential using ML scoring"
      input_schema:
        type: "object"
        properties:
          company_size: {type: "string", enum: ["startup", "sme", "enterprise"]}
          budget: {type: "number", minimum: 0}
          timeline: {type: "string", format: "date"}
      output_schema:
        type: "object"
        properties:
          score: {type: "number", minimum: 0, maximum: 100}
          category: {type: "string", enum: ["hot", "warm", "cold"]}
          next_actions: {type: "array", items: {type: "string"}}
      
      examples:
        - input: 
            company_size: "enterprise"
            budget: 100000
            timeline: "2025-Q2"
          output:
            score: 85
            category: "hot"
            next_actions: ["Schedule demo", "Send proposal"]

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// Live code execution in documentation
class DocInteractions {
    // Execute agent examples in sandbox
    async executeExample(agentName, exampleId) {
        const sandbox = await this.createSandbox(agentName);
        const result = await sandbox.execute(exampleId);
        return {
            output: result.response,
            performance: result.metrics,
            cost: result.token_usage
        };
    }
    
    // Generate documentation from agent runtime
    async generateLiveDocs(agentName) {
        const agent = await this.loadAgent(agentName);
        const capabilities = await agent.introspect();
        
        return {
            api_reference: this.generateAPIRef(capabilities),
            usage_examples: this.generateExamples(capabilities),
            performance_metrics: await this.getMetrics(agentName)
        };
    }
}

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# Sales assistant blueprint
blueprint:
  metadata:
    name: "crm-sales-assistant"
    version: "1.0.0"
    author: "AIMatrix Team"
    description: "Production-ready sales assistant with CRM integration"
    tags: ["sales", "crm", "lead-qualification"]
    
  parameters:
    - name: "crm_system"
      type: "string"
      enum: ["salesforce", "hubspot", "pipedrive"]
      required: true
      description: "Target CRM system for integration"
    
    - name: "lead_scoring_model"
      type: "string"
      default: "gradient_boosting"
      description: "ML model for lead scoring"
    
    - name: "qualification_threshold"
      type: "number"
      default: 70
      minimum: 0
      maximum: 100
      description: "Minimum score for qualified leads"
  
  template:
    agent:
      name: "${blueprint.name}-${parameters.crm_system}"
      version: "${blueprint.version}"
      
      tools:
        - name: "crm_integration"
          implementation: "mcp-${parameters.crm_system}"
        
        - name: "lead_scorer"
          model: "${parameters.lead_scoring_model}"
          threshold: ${parameters.qualification_threshold}
      
      workflows:
        - trigger: "new_lead"
          actions:
            - score_lead
            - update_crm
            - notify_sales_rep

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# Blueprint discovery and installation
class BlueprintMarketplace:
    def search_blueprints(self, query, filters=None):
        results = self.api.search({
            "query": query,
            "category": filters.get("category"),
            "min_rating": filters.get("min_rating", 4.0),
            "compatible_with": self.platform.version
        })
        
        return [
            Blueprint(
                id=bp["id"],
                name=bp["name"],
                description=bp["description"],
                rating=bp["rating"],
                downloads=bp["downloads"],
                author=bp["author"],
                verified=bp["verified"]
            ) for bp in results
        ]
    
    def install_blueprint(self, blueprint_id, parameters=None):
        blueprint = self.download_blueprint(blueprint_id)
        
        # Validate parameters
        validation = blueprint.validate_parameters(parameters)
        if not validation.valid:
            raise InvalidParametersError(validation.errors)
        
        # Generate agent from blueprint
        agent_config = blueprint.generate_config(parameters)
        
        # Install to workspace
        return self.workspace.create_agent(agent_config)

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# Blueprint creation template
blueprint_spec:
  # Blueprint metadata
  metadata:
    name: "${input.name}"
    description: "${input.description}"
    author: "${user.name}"
    version: "1.0.0"
  
  # Parameterization
  parameters: ${input.parameters}
  
  # Validation rules
  validation:
    - type: "security_scan"
      rules: ["no_hardcoded_secrets", "safe_tool_usage"]
    - type: "performance_test"
      requirements: ["<500ms_response", "memory<100MB"]
    - type: "compatibility_check"
      platforms: ["aimatrix_2.0+", "kubernetes_1.24+"]
  
  # Template generation
  template:
    source: "${input.agent_definition}"
    transformations:
      - parameterize_values
      - inject_security_defaults
      - add_monitoring_config

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// Workflow definition with visual editor support
const workflow = {
    name: "customer_onboarding",
    version: "1.0.0",
    
    nodes: [
        {
            id: "lead_received",
            type: "trigger",
            config: {
                event_type: "webhook",
                source: "website_form"
            }
        },
        {
            id: "qualify_lead",
            type: "agent",
            config: {
                agent: "lead-qualifier",
                timeout: "30s"
            },
            inputs: ["lead_received.data"]
        },
        {
            id: "route_decision",
            type: "decision",
            config: {
                condition: "qualify_lead.score >= 70"
            }
        },
        {
            id: "hot_lead_flow",
            type: "parallel",
            config: {
                branches: [
                    "notify_sales_rep",
                    "create_crm_record",
                    "schedule_followup"
                ]
            }
        },
        {
            id: "nurture_flow",
            type: "agent",
            config: {
                agent: "lead-nurturer",
                schedule: "weekly"
            }
        }
    ],
    
    edges: [
        {from: "lead_received", to: "qualify_lead"},
        {from: "qualify_lead", to: "route_decision"},
        {from: "route_decision", to: "hot_lead_flow", condition: "score >= 70"},
        {from: "route_decision", to: "nurture_flow", condition: "score < 70"}
    ]
};

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# Agent collaboration patterns
choreography:
  name: "order_processing"
  
  participants:
    - agent: "order_validator"
      role: "validate incoming orders"
    - agent: "inventory_checker" 
      role: "verify product availability"
    - agent: "payment_processor"
      role: "handle payment transactions"
    - agent: "fulfillment_coordinator"
      role: "coordinate shipping and delivery"
  
  message_flows:
    - from: "order_validator"
      to: "inventory_checker"
      message_type: "order_validated"
      
    - from: "inventory_checker"
      to: ["payment_processor", "fulfillment_coordinator"]
      message_type: "inventory_confirmed"
      
    - from: "payment_processor"
      to: "fulfillment_coordinator"
      message_type: "payment_completed"
  
  error_handling:
    - condition: "inventory_unavailable"
      action: "trigger_backorder_agent"
    - condition: "payment_failed"
      action: "notify_customer_agent"

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// VS Code extension configuration
{
    "aimatrix.workspace": {
        "features": {
            "intellisense": true,
            "live_testing": true,
            "performance_profiling": true,
            "cost_estimation": true
        },
        
        "linting": {
            "enabled": true,
            "rules": [
                "agent_naming_convention",
                "tool_signature_validation", 
                "security_best_practices",
                "performance_guidelines"
            ]
        },
        
        "debugging": {
            "breakpoints": true,
            "step_through_execution": true,
            "variable_inspection": true,
            "conversation_replay": true
        }
    }
}

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# Agent development commands
aimatrix agent create sales-assistant \
  --blueprint=crm-assistant \
  --crm-system=salesforce \
  --template=enterprise

aimatrix agent test sales-assistant \
  --scenario=lead-qualification \
  --verbose

aimatrix workflow create order-processing \
  --agents=validator,inventory,payment \
  --trigger=webhook

aimatrix deploy \
  --agent=sales-assistant \
  --environment=staging \
  --monitoring=enabled

# Blueprint management
aimatrix blueprint search "customer service"
aimatrix blueprint install crm-assistant --version=1.2.0
aimatrix blueprint create my-template --from=existing-agent

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# Agent testing with conversation scenarios
class AgentTester:
    def test_conversation_flow(self):
        agent = self.load_agent("sales-assistant")
        
        # Simulate conversation
        conversation = ConversationSimulator(agent)
        
        # Test lead qualification flow
        response1 = conversation.send("I'm interested in your product")
        assert "tell me about your company" in response1.lower()
        
        response2 = conversation.send("We're a 500-person enterprise")
        assert conversation.context["company_size"] == "enterprise"
        
        response3 = conversation.send("Budget is around $100k")
        assert conversation.context["budget"] >= 100000
        
        # Verify qualification outcome
        qualification = agent.qualify_lead(conversation.context)
        assert qualification["score"] > 70
        assert qualification["category"] == "hot"
    
    def test_tool_integration(self):
        agent = self.load_agent("sales-assistant")
        
        # Mock CRM responses
        with mock.patch('crm.create_lead') as mock_crm:
            mock_crm.return_value = {"id": "lead_123", "status": "created"}
            
            result = agent.create_lead({
                "name": "Test Company",
                "email": "test@company.com"
            })
            
            mock_crm.assert_called_once()
            assert result["crm_id"] == "lead_123"

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# Load testing configuration
performance_tests:
  - name: "concurrent_conversations"
    type: "load_test"
    config:
      concurrent_users: 100
      duration: "5m"
      ramp_up: "30s"
    
    assertions:
      - avg_response_time < 500ms
      - 95th_percentile < 1s
      - error_rate < 0.1%
  
  - name: "memory_usage"
    type: "resource_test"
    config:
      duration: "10m"
      monitoring_interval: "10s"
    
    assertions:
      - memory_usage < 512MB
      - cpu_usage < 50%
      - no_memory_leaks

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# Security validation pipeline
security:
  static_analysis:
    - tool: "semgrep"
      rules: ["aimatrix-security-rules"]
    - tool: "bandit" 
      config: "aimatrix-bandit.yaml"
  
  secret_detection:
    - tool: "truffleHog"
      entropy_threshold: 4.0
    - tool: "detect-secrets"
      baseline: ".secrets.baseline"
  
  dependency_scanning:
    - tool: "safety"
      database: "latest"
    - tool: "npm audit"
      level: "moderate"
  
  agent_specific:
    - validate_prompt_injection_protection
    - check_tool_permission_boundaries
    - verify_data_handling_compliance

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# GDPR compliance checker for agents
class ComplianceValidator:
    def validate_gdpr_compliance(self, agent_config):
        issues = []
        
        # Check data collection practices
        if self.collects_personal_data(agent_config):
            if not self.has_consent_mechanism(agent_config):
                issues.append("Missing consent mechanism for personal data")
        
        # Verify data retention policies
        if not self.has_retention_policy(agent_config):
            issues.append("No data retention policy specified")
        
        # Check for right to be forgotten
        if not self.supports_data_deletion(agent_config):
            issues.append("Agent doesn't support data deletion requests")
        
        return ComplianceReport(
            compliant=len(issues) == 0,
            issues=issues,
            recommendations=self.generate_recommendations(issues)
        )

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# Deep platform integration
class PlatformConnector:
    def sync_with_core_platform(self):
        # Sync agent definitions
        agents = self.workspace.get_all_agents()
        for agent in agents:
            self.platform.register_agent(agent)
        
        # Update deployment configurations
        deployments = self.workspace.get_deployments()
        for deployment in deployments:
            self.platform.update_deployment(deployment)
        
        # Sync monitoring configurations
        self.platform.configure_monitoring(
            self.workspace.get_monitoring_config()
        )

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# BigLedger workspace synchronization
bigledger:
  sync:
    entities:
      - customers
      - products
      - orders
      - invoices
    
    permissions:
      read: ["sales_team", "customer_service"]
      write: ["order_processor", "billing_agent"]
    
    events:
      - trigger: "new_customer"
        action: "create_agent_workspace"
      - trigger: "order_completed"
        action: "update_agent_knowledge"

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# Development standards
standards:
  naming:
    agents: "kebab-case" # sales-assistant
    tools: "snake_case" # qualify_lead
    workflows: "kebab-case" # order-processing
  
  structure:
    max_tools_per_agent: 10
    max_workflow_depth: 5
    documentation_coverage: 90%
  
  performance:
    response_time_target: 500ms
    memory_limit: 512MB
    token_efficiency: optimize
  
  security:
    input_validation: required
    output_sanitization: required
    audit_logging: enabled

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# Create new workspace
aimatrix workspace create my-enterprise-workspace \
  --template=enterprise \
  --git-provider=github \
  --organization=my-company

# Initialize repository structure
aimatrix workspace init \
  --blueprints=marketplace \
  --docs=auto-generate \
  --ci-cd=github-actions

# Install development tools
aimatrix tools install \
  --vscode-extension \
  --cli-completion \
  --git-hooks

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# Create agent from blueprint
aimatrix agent create my-first-agent \
  --blueprint=customer-service \
  --interactive

# Test agent locally
aimatrix agent test my-first-agent \
  --conversation-file=test-scenarios.yaml

# Deploy to staging
aimatrix agent deploy my-first-agent \
  --environment=staging \
  --monitor=true