# AI-First Architecture: Semantic MCP for Targetprocess
## Technical Architecture Overview
This document details the technical architecture for implementing an AI-first approach to Targetprocess integration using semantic MCP servers with role-based personalities.
## Core Architectural Principles
### 1. Semantic Abstraction Layer
Traditional API approach:
```javascript
// Developer needs to understand Targetprocess data model
const tasks = await api.search({
type: "Task",
where: "AssignedUser.Id = 123 and EntityState.Name != 'Done'",
include: ["Project", "Priority", "Iteration"]
});
```
Semantic MCP approach:
```javascript
// AI understands intent
const tasks = await mcp.execute("show-my-tasks");
```
### 2. Role-Based Personality System
```yaml
personalities:
developer:
description: "Software developer focused on task execution"
capabilities:
- show-my-tasks
- start-working-on
- update-progress
- complete-task
- log-time
context:
- current_sprint
- assigned_tasks
- team_members
constraints:
- can_only_modify_assigned_items
- cannot_delete_entities
- cannot_modify_sprint_scope
product-owner:
description: "Product owner managing backlog and priorities"
capabilities:
- manage-backlog
- prioritize-features
- plan-sprint
- create-user-stories
- define-acceptance-criteria
context:
- product_roadmap
- customer_feedback
- team_velocity
constraints:
- can_modify_any_backlog_item
- cannot_modify_time_tracking
- cannot_assign_tasks
```
### 3. Multi-Instance Orchestration
For complex agent scenarios requiring multiple perspectives:
```typescript
class ReleaseManagerAgent {
private instances: {
productOwner: MCPInstance;
scrumMaster: MCPInstance;
techLead: MCPInstance;
};
async planRelease(requirements: string[]) {
// Get prioritized features from Product Owner perspective
const features = await this.instances.productOwner
.execute("prioritize-features", { requirements });
// Assess team capacity from Scrum Master perspective
const capacity = await this.instances.scrumMaster
.execute("calculate-team-capacity", {
duration: "3 months",
teams: features.teams
});
// Validate technical feasibility from Tech Lead perspective
const feasibility = await this.instances.techLead
.execute("assess-technical-debt", { features });
return this.orchestrateReleasePlan(features, capacity, feasibility);
}
}
```
## Integration Architecture
### 1. MCP Service Deployment Model
```
┌─────────────────────────────────────────────────────────────┐
│ AI Platform (watsonx) │
├─────────────────────────────────────────────────────────────┤
│ Agent 1 Agent 2 Agent 3 │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │Developer │ │ P.O. │ │ SM │ │
│ │ MCP │ │ MCP │ │ MCP │ │
│ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │
└───────┼───────────────┼─────────────────┼──────────────────┘
│ │ │
└───────────────┴─────────────────┘
│
MCP Protocol
│
┌───────────────────────┴─────────────────────────────────────┐
│ MCP Service Layer │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ │
│ │ Semantic │ │ Context │ │ Workflow │ │
│ │ Mapper │ │ Manager │ │ Orchestrator │ │
│ └─────────────┘ └──────────────┘ └─────────────────┘ │
│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ │
│ │ Role │ │ Feature │ │ Hint │ │
│ │ Filter │ │ Registry │ │ Generator │ │
│ └─────────────┘ └──────────────┘ └─────────────────┘ │
└──────────────────────────┬──────────────────────────────────┘
│
│
┌──────────────────────────┴──────────────────────────────────┐
│ Targetprocess API / Graph DB │
└──────────────────────────────────────────────────────────────┘
```
### 2. Semantic Operation Flow
```typescript
interface SemanticOperation {
// User intent
intent: "show-my-tasks";
// Role context
personality: "developer";
// Execution context
context: {
user: { id: 123, team: "Alpha" };
workspace: { project: "Banking", sprint: "Sprint 42" };
conversation: { previousOps: ["login", "check-notifications"] };
};
// Semantic mapping
mapsTo: {
operation: "searchEntities";
params: {
type: "Task",
where: "AssignedUser.Id = ${context.user.id} and Sprint = ${context.workspace.sprint}",
include: ["Priority", "BlockedBy"]
};
};
// Workflow hints
provides: {
nextSteps: [
"Select a task to work on",
"Check for blocked items"
];
suggestions: [
"start-working-on ${mostUrgentTask.id}",
"show-blocked-tasks"
];
};
}
```
## Implementation Patterns
### 1. Context-Aware Execution
```typescript
class ContextAwareExecutor {
async execute(operation: string, params: any, context: ExecutionContext) {
// Enrich parameters with context
const enrichedParams = await this.enrichWithContext(params, context);
// Apply role-based filters
const filteredParams = await this.applyRoleFilters(
enrichedParams,
context.personality
);
// Execute operation
const result = await this.performOperation(operation, filteredParams);
// Generate contextual hints
const hints = await this.generateHints(result, context);
return {
data: result,
nextSteps: hints.workflow,
suggestions: hints.operations,
context: this.updateContext(context, result)
};
}
}
```
### 2. Workflow Orchestration
```typescript
class WorkflowOrchestrator {
async executeWorkflow(
workflowId: string,
params: any,
context: ExecutionContext
) {
const workflow = this.workflows.get(workflowId);
const results = [];
for (const step of workflow.steps) {
// Check if step should execute
if (await this.shouldExecute(step, results, context)) {
// Execute step with accumulated context
const stepResult = await this.executeStep(step, {
params,
previousResults: results,
context
});
results.push(stepResult);
// Update context for next step
context = this.mergeContext(context, stepResult.context);
// Check for early termination
if (stepResult.terminate) break;
}
}
return this.composeWorkflowResult(results, workflow);
}
}
```
### 3. Semantic Hint Generation
```typescript
class SemanticHintGenerator {
generateHints(
operation: string,
result: any,
context: ExecutionContext
): WorkflowHints {
const hints = {
workflow: [],
operations: []
};
// Analyze result state
const state = this.analyzeState(result);
// Generate workflow guidance
if (state.hasBlockedItems) {
hints.workflow.push("Address blocked items before starting new work");
hints.operations.push(`resolve-blockers`);
}
if (state.hasHighPriorityItems && !state.isWorkingOnHighPriority) {
hints.workflow.push("Consider focusing on high-priority items first");
hints.operations.push(`start-working-on ${state.highestPriority.id}`);
}
// Role-specific hints
if (context.personality === 'developer' && state.hasUnestimatedTasks) {
hints.workflow.push("Some tasks need estimation");
hints.operations.push(`estimate-tasks`);
}
return hints;
}
}
```
## Advanced Features
### 1. Graph Database Integration
Future enhancement for semantic relationships:
```cypher
// Neo4j query for semantic task relationships
MATCH (u:User {id: $userId})-[:ASSIGNED_TO]->(t:Task)
WHERE t.status <> 'Done'
MATCH (t)-[:BELONGS_TO]->(s:Sprint)-[:CURRENT]->(p:Project)
OPTIONAL MATCH (t)-[:BLOCKED_BY]->(blocker)
OPTIONAL MATCH (t)-[:DEPENDS_ON]->(dependency)
RETURN t, s, p, blocker, dependency
ORDER BY t.priority DESC, t.createdDate ASC
```
### 2. Learning and Adaptation
```typescript
class AdaptiveMCPService {
async learn(interaction: Interaction) {
// Track operation sequences
await this.patternTracker.record(interaction);
// Identify common workflows
const patterns = await this.patternAnalyzer.analyze(
interaction.personality,
interaction.sequence
);
// Suggest workflow optimizations
if (patterns.frequency > threshold) {
await this.workflowOptimizer.suggest({
pattern: patterns,
optimization: this.createCompositeOperation(patterns)
});
}
// Adapt hint generation
await this.hintAdapter.update(interaction.feedback);
}
}
```
### 3. Multi-Platform Support
```typescript
interface PlatformAdapter {
// watsonx adapter
watsonx: {
formatRequest: (req: WatsonxRequest) => MCPRequest;
formatResponse: (res: MCPResponse) => WatsonxResponse;
handleStreaming: (stream: MCPStream) => WatsonxStream;
};
// Claude adapter
claude: {
formatRequest: (req: ClaudeRequest) => MCPRequest;
formatResponse: (res: MCPResponse) => ClaudeResponse;
};
// Generic OpenAI-compatible
openai: {
formatRequest: (req: OpenAIRequest) => MCPRequest;
formatResponse: (res: MCPResponse) => OpenAIResponse;
};
}
```
## Deployment Architecture
### 1. Container-Based Deployment
```yaml
version: '3.8'
services:
mcp-gateway:
image: targetprocess/mcp-gateway:latest
environment:
- AUTH_MODE=oauth2
- RATE_LIMIT=1000/min
ports:
- "8443:8443"
mcp-developer:
image: targetprocess/mcp-service:latest
environment:
- PERSONALITY=developer
- MAX_INSTANCES=100
deploy:
replicas: 3
mcp-product-owner:
image: targetprocess/mcp-service:latest
environment:
- PERSONALITY=product-owner
- MAX_INSTANCES=50
deploy:
replicas: 2
context-store:
image: redis:alpine
volumes:
- context-data:/data
metrics-collector:
image: targetprocess/mcp-metrics:latest
environment:
- BILLING_ENDPOINT=${BILLING_API}
```
### 2. Scaling Strategy
- **Horizontal Scaling**: Personality-specific instance pools
- **Caching Layer**: Redis for context and frequent queries
- **Load Balancing**: Route by personality and operation type
- **Circuit Breakers**: Prevent cascade failures
- **Rate Limiting**: Per-customer and per-operation limits
## Security Considerations
### 1. Authentication Flow
```
AI Platform → OAuth2 → MCP Gateway → Validate Permissions → Route to Instance
↓
Check Rate Limits
↓
Log for Billing
```
### 2. Role-Based Access Control
- Personality-level permissions
- Operation-level restrictions
- Data-level filtering
- Audit trail for compliance
## Monitoring and Observability
### 1. Key Metrics
- **Operation Latency**: p50, p95, p99 by operation type
- **Success Rate**: By personality and operation
- **Usage Patterns**: Heat maps of operation sequences
- **Context Efficiency**: Cache hit rates
- **Business Metrics**: Operations per customer, revenue per operation
### 2. Debugging Tools
- Request tracing across instances
- Semantic mapping visualization
- Context state inspection
- Hint effectiveness tracking
## Conclusion
This architecture enables Targetprocess to become truly AI-first by:
1. **Abstracting Complexity**: AI agents work with business concepts, not technical APIs
2. **Enabling Orchestration**: Multiple perspectives can be combined naturally
3. **Supporting Evolution**: Decoupled architecture allows independent innovation
4. **Driving Monetization**: Clear value delivery through metered operations
5. **Leading the Market**: First comprehensive AI-native project management platform
The semantic MCP approach transforms Targetprocess from a tool that AI can use into a platform designed for AI intelligence.