Control Center Build Path

Build it like this:

Control Center Build Path

Build it like this:

Control Center Build Path

1. Start with one decision

Do not start with agents.

Start with the executive question:

“Is this initiative likely to succeed, and what should we do next?”

Everything supports that.

2. Core flow

Intent

  ↓

Evidence

  ↓

Simulation

  ↓

Risk Score

  ↓

Decision Recommendation

  ↓

Execution Actions

That is the product.

3. MVP screens

Build only these first:

1. Portfolio Dashboard

2. Initiative Detail

3. Evidence Timeline

4. Simulation Results

5. Recommended Actions

4. Backend domains

/project

/intent

/evidence

/simulation

/risk

/recommendation

/action

These become your API boundaries.

5. First data sources

Use mock files first, then real integrations:

Phase 1: Mock Jira, GitHub, Wiki

Phase 2: Real Jira API

Phase 3: Real GitHub API

Phase 4: Confluence/Wiki

Phase 5: Cloud cost + observability data

6. Agent model

Agents should not be the architecture.

They are workers inside the control center.

Planner Agent

Evidence Agent

Risk Agent

Simulation Agent

Decision Agent

Summary Agent

Skills are the tools:

Jira Reader

GitHub Reader

Wiki Reader

Cost Estimator

Dependency Mapper

Risk Calculator

7. AWS reference architecture

React Dashboard

      ↓

CloudFront + S3

      ↓

API Gateway

      ↓

FastAPI on ECS Fargate

      ↓

DynamoDB / S3

      ↓

Bedrock

      ↓

CloudWatch / OpenTelemetry

Start with FastAPI + mock data + Docker.

Deploy later.

8. First working feature

Build this first:

POST /intent/analyze

Input:

{

  "projectName": "Customer Portal Modernization",

  "timelineWeeks": 12,

  "teamSize": 5,

  "knownRisks": ["legacy APIs", "QA bottleneck"]

}

Output:

{

  "riskScore": 72,

  "confidenceScore": 61,

  "topRisks": ["legacy API dependency", "QA bottleneck"],

  "recommendations": [

    "Assign API owner",

    "Add QA automation gate",

    "Create production readiness checklist"

  ]

}

9. What makes it different

A normal AI app says:

“Ask me questions.”

Your control center says:

“Here is the predicted outcome, evidence, risk, and recommended decision.”

That is the category.

Intent → Simulation → Evidence → Decision → Execution.

1. Start with one decision

Do not start with agents.

Start with the executive question:

“Is this initiative likely to succeed, and what should we do next?”

Everything supports that.

2. Core flow

Intent

  ↓

Evidence

  ↓

Simulation

  ↓

Risk Score

  ↓

Decision Recommendation

  ↓

Execution Actions

That is the product.

3. MVP screens

Build only these first:

1. Portfolio Dashboard

2. Initiative Detail

3. Evidence Timeline

4. Simulation Results

5. Recommended Actions

4. Backend domains

/project

/intent

/evidence

/simulation

/risk

/recommendation

/action

These become your API boundaries.

5. First data sources

Use mock files first, then real integrations:

Phase 1: Mock Jira, GitHub, Wiki

Phase 2: Real Jira API

Phase 3: Real GitHub API

Phase 4: Confluence/Wiki

Phase 5: Cloud cost + observability data

6. Agent model

Agents should not be the architecture.

They are workers inside the control center.

Planner Agent

Evidence Agent

Risk Agent

Simulation Agent

Decision Agent

Summary Agent

Skills are the tools:

Jira Reader

GitHub Reader

Wiki Reader

Cost Estimator

Dependency Mapper

Risk Calculator

7. AWS reference architecture

React Dashboard

      ↓

CloudFront + S3

      ↓

API Gateway

      ↓

FastAPI on ECS Fargate

      ↓

DynamoDB / S3

      ↓

Bedrock

      ↓

CloudWatch / OpenTelemetry

Start with FastAPI + mock data + Docker.

Deploy later.

8. First working feature

Build this first:

POST /intent/analyze

Input:

{

  "projectName": "Customer Portal Modernization",

  "timelineWeeks": 12,

  "teamSize": 5,

  "knownRisks": ["legacy APIs", "QA bottleneck"]

}

Output:

{

  "riskScore": 72,

  "confidenceScore": 61,

  "topRisks": ["legacy API dependency", "QA bottleneck"],

  "recommendations": [

    "Assign API owner",

    "Add QA automation gate",

    "Create production readiness checklist"

  ]

}

9. What makes it different

A normal AI app says:

“Ask me questions.”

Your control center says:

“Here is the predicted outcome, evidence, risk, and recommended decision.”

That is the category.

Intent → Simulation → Evidence → Decision → Execution.