ruby-production

byA.H. AJAY CHARAN

Build a production-ready multi-tenant SaaS called **AgentControl**. ## Vision AgentControl is a control plane for companies running autonomous AI agents. Position it as: **“The operating system for production AI agents.”** It must allow companies to observe, evaluate, secure, govern and control AI agents. Do NOT build a generic chatbot or AI wrapper. ## MVP Build the first version around: **AI Agent Flight Recorder + Runtime Guard** A developer connects an AI agent using an SDK/API. AgentControl records and controls its execution. ## Target users * AI startups * SaaS companies * enterprise AI teams * CTOs * AI/ML engineers * security engineers * platform engineers ## Main navigation Dashboard Agents Executions Live Activity Security Policies Approvals Incidents Evaluations Costs Audit Logs API / SDK Settings ## Authentication and SaaS Implement: * secure signup/login * organizations * teams * projects * multi-tenancy * role-based access * API keys * usage tracking * subscription-ready billing Roles: Owner, Admin, Developer, Viewer, Security. Every tenant's data must be isolated. ## Agent registration Allow users to create/register agents with: * name * description * environment * owner * model * model version * framework * tools * permissions * risk level * status Generate a unique Agent ID. ## Agent SDK Create a Python SDK and API for connecting agents. Record: * executions * model calls * prompts/responses * tool calls * tool results * decisions * errors * latency * token usage * estimated cost * metadata Example: ```python from agent_control import AgentMonitor monitor = AgentMonitor( api_key="YOUR_KEY", agent_id="AGENT_ID" ) with monitor.trace("customer_support_task"): response = agent.run(user_request) ``` Design the SDK so TypeScript/JavaScript can be added later. ## AI Agent Flight Recorder This is the central feature. Record the complete execution timeline: User request → model call → tool selection → tool/API call → result → final response Each event should contain: * timestamp * duration * input/output metadata * model * tokens * cost * tool * status * risk score * policy result Create a detailed execution timeline UI. Do not expose hidden chain-of-thought. Store safe execution metadata, tool calls and concise explanations. ## Dashboard Create a polished enterprise dark-mode SaaS dashboard. Show: * total executions * success rate * total cost * blocked actions * risk events * agent health * live activity * execution trends * risk severity * recent security events * pending approvals The dashboard should look like a serious developer/security product. ## Runtime Guard Before sensitive agent tool actions execute, evaluate them against policies. Possible decisions: **ALLOW** **BLOCK** **REQUIRE APPROVAL** Example: Agent attempts to delete a customer. Policy: Production agents cannot delete customer records. Result: **BLOCKED** Record the policy decision and reason. ## Permissions Allow organizations to define agent permissions. Example: Customer Support Agent: CRM: READ/WRITE CRM DELETE: DENIED Payments: READ Payments WRITE: DENIED Database: limited READ Enforce permissions wherever technically possible. ## Risk Engine Score agent actions: LOW MEDIUM HIGH CRITICAL Consider: * sensitive data * financial operations * destructive actions * privilege level * external communication * unusual behavior * policy violations * suspicious tool calls Create a security-events dashboard with filtering. ## Human Approval For risky actions: Agent → policy engine → REQUIRE APPROVAL → human approval/rejection → continue or stop Create an approval inbox. Example: Sales Agent wants to send a ₹12,00,000 quotation. Risk: HIGH Buttons: Approve / Reject Record every approval decision. ## Incidents Automatically create incidents for important security events. Incident fields: * ID * severity * agent * execution * event * policy * timestamp * status * assignee * resolution Statuses: Open, Investigating, Resolved, Ignored. ## Audit Logs Record: * logins * API key creation * agent changes * policy changes * permission changes * blocked actions * approvals * incidents * administrative actions Audit logs must be protected from normal users. ## Cost Monitoring Track: * tokens * model * cost * execution * agent * project * time Show cost trends and detect unusual cost increases. Example: “Support Agent cost increased 62% compared with the previous 7-day average.” ## Demo Agent Create a built-in simulated AI agent so users can immediately experience the product. The demo should: 1. receive a request 2. call an AI model or simulated model 3. access simulated CRM 4. access simulated database 5. attempt a restricted action 6. trigger the policy engine 7. block the action 8. create a security event 9. create an incident The user should see the complete event in real time. This demo is important for product-led growth. ## API Build an API-first architecture. Core API areas: * authentication * organizations * projects * agents * executions * traces * tools * policies * permissions * risks * incidents * approvals * audit logs * usage * billing Automatically document APIs. ## Technical architecture Use: Frontend: Next.js + TypeScript Backend: Python/FastAPI Database: PostgreSQL Queue/cache: Redis Deployment: Docker Build the architecture so it can later deploy on AWS, Azure or GCP. Use secure environment variables and never expose secrets. ## Security Implement: * tenant isolation * RBAC * secure authentication * API-key protection * encryption for secrets * rate limiting * input validation * secure headers * audit logging * least privilege * sensitive-data redaction Allow configurable telemetry retention. ## Product principles Clearly distinguish: **OBSERVED** **EVALUATED** **BLOCKED** **APPROVED** **EXECUTED** Do not claim an action was blocked unless the runtime guard actually prevented it. ## MVP completion criteria A user must be able to: 1. create an organization 2. create a project 3. register an agent 4. create an API key 5. connect the SDK 6. run an agent 7. view execution traces 8. view model/tool activity 9. view cost and latency 10. create policies 11. trigger a policy violation 12. block an action 13. view the security event 14. create/view an incident 15. require human approval 16. approve/reject an action 17. view audit logs 18. use the demo agent 19. manage team members 20. view usage ## Build process Before coding: 1. analyze requirements 2. produce architecture 3. design database schema 4. design API contracts 5. design security and tenant isolation 6. divide work into milestones Then implement milestone by milestone. Do not create only a frontend mockup. Build real backend functionality. Prioritize security, reliability, multi-tenancy, observability and extensibility. ## Future roadmap Design the architecture so these can be added later: ### Agent Evaluation Test agents automatically before deployment. ### Agent Security Detect prompt injection, data leakage, privilege escalation and unsafe tool usage. ### Agent Governance Policies, permissions, approvals and compliance. ### Agent Optimization Detect expensive, slow and unreliable agents. ### Autonomous Improvement Detect problem → investigate → generate fix → evaluate → approve → deploy → monitor. The long-term goal is: **Become the control plane through which enterprises safely operate autonomous AI agents.**