# 📄 Product Requirements Document (PRD): AgentOps Cockpit

**Version**: 1.0 (Post-v0.6.0 Release)
**Status**: Active / Production-Ready
**Owner**: Agentic AI Engineering Team

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## 1. Executive Summary
The **AgentOps Cockpit** is a production-grade operations and governance platform for AI agents. It addresses the "Day 2" challenges of agentic development: cost management, security hardening, architectural alignment, and operational visibility. By implementing the **Agentic Trinity** (Engine, Face, Cockpit), it provides developers with a framework-agnostic "Mission Control" to transition agents from prototypes to reliable production services.

<div align="center">
  <img src="diagrams/trinity.png" alt="Agentic Trinity Architecture" width="80%" />
</div>

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## 2. Problem Statement
Most AI agent development today stops at a single script or a basic chat interface. Developers lack:
- **Governance**: No standardized way to verify if an agent follows "Well-Architected" principles.
- **FinOps**: No visibility into the true cost of agentic reasoning vs. potential optimizations.
- **Security**: Limited tools for automated adversarial testing (Red Teaming) specific to LLMs.
- **Velocity**: Detailed audits often slow down the dev loop, leading to skipped safety checks.

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## 3. Target Audience & Personas
- **The AI Engineer**: Responsible for building and fine-tuning agent logic. Needs fast feedback on code quality and tool reliability.
- **The SecOps Lead**: Needs to ensure agents don't leak PII or become vectors for prompt injection attacks.
- **The Platform Architect**: Needs to standardize agentic patterns across multiple frameworks (LangChain, AutoGen, ADK).
- **The FinOps Analyst**: Responsible for monitoring and reducing LLM spend through caching and model routing.

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## 4. Critical User Journeys (CUJs)
- **CUJ 1: The Fast-Path Developer Audit**
  - *User:* AI Engineer
  - *Action:* Runs `make audit` or `uvx agentops-cockpit report --mode quick`.
  - *Outcome:* Receives a sub-second score on architecture, security, and cost-efficiency without context-switching.
- **CUJ 2: Pre-Production Security Hardening**
  - *User:* SecOps Lead
  - *Action:* Executes `make red-team` against a high-stakes customer service agent.
  - *Outcome:* Identifies PII leakage vulnerabilities and receives auto-code fixes to implement scrubbers.
- **CUJ 3: Cross-Framework Modernization**
  - *User:* Platform Architect
  - *Action:* Migrates an agent from OpenAI to Vertex AI.
  - *Outcome:* The **Conflict Guard** identifies incompatible state loops and the **Situational Auditor** recommends enabling Context Caching for a 90% cost reduction.

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## 5. Functional Requirements
### R1: Situational Optimization (Triple-State Analysis)
- Must detect if required SDKs are missing.
- Must provide version-aware situational workarounds for legacy environments.
- Must identify modernization paths (e.g., Context Caching, Structured Outputs).

### R2: Master Orchestration
- Must coordinate multiple specialized auditors (Arch, Security, Quality, Cost).
- Must generate a unified Markdown report (`cockpit_final_report.md`).
- Must support parallelized execution of sub-tasks.

### R3: Conflict Guard
- Must detect architectural anti-patterns when mixing agentic libraries (e.g., CrewAI vs. LangGraph).
- Must verify "Synergy Pairings" (e.g., MCP Hub + Google ADK).

### R4: Red Team Evaluation
- Must launch automated attacks: PII extraction, Prompt Injection, Jailbreaking.
- Must support multilingual safety testing (e.g., Cantonese, Spanish).

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## 6. Technical Stack
- **Engine Layer**: Python 3.10+, FastAPI.
- **Operations (Cockpit)**: Typer (CLI), Rich (Terminal TUI), Pydantic (Schema).
- **Frontend (Face)**: React 18, Vite, TypeScript, A2UI Protocol.
- **Connectivity**: MCP (Model Context Protocol) for unified tool transport.
- **Deployment**: Google Cloud Run (Compute), Firebase Hosting (Web), GKE (Orchestration).

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## 7. Success Metrics (KPIs)
- **Dev Velocity**: Audit latency in Quick Mode must stay under **200ms**.
- **Efficiency**: Use of Semantic Caching should demonstrate a **40-60%** reduction in repeated token costs.
- **Security**: 100% of PII leakage patterns detected by Red Team must have an associated A2A fix.
- **Adoption**: Reach **10,000 GitHub stars** via community framework connectors.

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## 8. Roadmap & Future Scope
- **Q1 (Foundation)**: [x] Red Team Auditor, [x] Quick-Safe Build, [x] Framework Connectors.
- **Q2 (Intelligence)**: [ ] VPC-Native Hive Mind, [ ] Visual Mission Control (Real-time Dashboard).
- **Q3 (Ecosystem)**: [ ] GCP Marketplace Integration, [ ] Community Plugin System.

## 9. Enterprise Positioning & Limitations
The AgentOps Cockpit is a **Governance-as-Code** platform. It is designed to complement—not replace—specialized enterprise suites:
- **Performance**: Use for "Safe-Build" sanity checks; use **JMeter/Locust** for 100k+ user stress testing.
- **Security**: Use for automated regression; use **Snyk/Checkmarx** for deep static analysis (SAST).
- **Compliance**: Provides heuristic evidence; use **Google Cloud Security Command Center** for live infrastructure drift detection.

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*Created by the AgentOps Cockpit Orchestrator.*
