agentops-cockpit 1.4.1

Production-grade Agent Operations (AgentOps) Platform

🕹️ AgentOps Cockpit

🌐 Official Website & Live Demo

"Infrastructure gives you the pipes. We give you the Intelligence."

The developer distribution for building, optimizing, and securing AI agents on Google Cloud.

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📽️ The Mission

Most AI agent templates stop at a single Python file and an API key. The AgentOps Cockpit is for developers moving into production. It provides framework-agnostic governance, safety, and cost guardrails for the entire agentic ecosystem.

• Governance-as-Code: Audit your agent against Google Well-Architected best practices with the Evidence Bridge—real-time citations for architectural integrity.
• SME Persona Audits: Parallelized review of your codebase by automated Principal SMEs across FinOps, SecOps, Architecture, and Quality.
• Agentic Trinity: Dedicated layers for the Engine (Logic), Face (UX), and Cockpit (Ops).
• A2A Connectivity: Implements the Agent-to-Agent Transmission Standard for secure swarm orchestration.
• MCP Native: Registration as a Model Context Protocol server for 1P/2P/3P tool consumption.

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🏗️ The Agentic Trinity

We divide the complexity of production agents into three focused pillars:

graph TD
   subgraph Trinity [The Agentic Trinity 2.0]
       E(The Engine: Reasoning)
       F(The Face: Interface)
       C(The Cockpit: Operations)
       S{Sovereignty & Compliance}
   end
   E <--> C
   F <--> C
   E <--> F
   E -.-> S
   F -.-> S
   C -.-> S
   style Trinity fill:#f8fafc,stroke:#334155,stroke-width:2px
   style S fill:#0ea5e9,color:#fff,stroke:#0284c7

• ⚙️ The Engine: The reasoning core. Built with ADK, FastAPI, and Vertex AI.
• 🎭 The Face: The user experience. Adaptive UI surfaces and GenUI standards via the A2UI spec.
• 🕹️ The Cockpit: The operational brain. Cost control, semantic caching, shadow routing, and adversarial audits.

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🕹️ v1.4.0: The "Ecosystem Expansion" Release (NEW)

Evolving into a full Lifecycle Management Platform for AI Agents. See the v1.4.0 Release Notes.

• 🧗 RAG Truth-Sayer SME: Dedicated auditor for retrieval-reasoning fidelity and grounding logic.
• 🚀 PR Scorecard Action: Automated maturity scorecard for pull requests to increase velocity and trust.
• 🛠️ Remediation Workbench: TUI-based review loop for approving autonomous code patches.
• 💰 ROI Waterfall: Advanced modeling for cost-per-task projections and model-tier optimization.
• 📦 MCP Tool Store: Centralized registry for discovering and scaffolding MCP tool integrations.

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🚀 Key Innovation: The "Intelligence" Layer

🛡️ Red Team Auditor (Adversarial SRE)

Don't wait for your users to find prompt injections. Use the built-in Adversarial Evaluator to launch self-attacks against your agent, testing for PII leaks, instruction overrides, and multilingual jailbreaks.

🧠 Hive Mind (Semantic Caching)

Reduce LLM costs by up to 40%. The Hive Mind checks for semantically similar queries in 10ms, serving cached answers for common questions without calling the LLM.

🏛️ Arch Review & Autonomous Evolution

Every agent in the cockpit is graded against a framework-aware checklist. The Cockpit intelligently detects your stack and runs a tailored Architecture Review. v1.3 introduces Autonomous Evolution—the ability to synthesize code fixes directly from audit findings.

🕹️ MCP Connectivity Hub (Model Context Protocol)

Stop building one-off tool integrations. The Cockpit provides a unified hub for MCP Servers. Connect to 1P/2P/3P tools via the standardized Model Context Protocol for secure, audited tool execution. Start the server with make mcp-serve.

🗄️ Situational Database Audits

The Cockpit now performs platform-specific performance and security audits for AlloyDB, Pinecone, BigQuery, and Cloud SQL.

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🛡️ Advanced Governance & Discovery (v1.3.5)

Modern agents don't just live in agent.py. The Cockpit uses a centralized Discovery Engine to intelligently map your project:

• .gitignore Compliance: Zero-noise scanning that respects your project's ignore rules.
• Multi-Target Logic: Define targets: [] in cockpit.yaml to audit distributed agents in a single pass.
• Template Isolation: Automatically ignores raw template placeholders (e.g., Jinja/Cookiecutter) to focus on the active implementation.
• Artifact Store: All data (SARIF, Evidence, HTML) is now sovereignly stored in the .cockpit/ directory.

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⌨️ Master Command Registry

The Cockpit is available as a first-class CLI and a comprehensive Makefile-based operational toolkit.

Registry	Description
🕹️ Makefile Commands	Standard local development and orchestration shortcuts.
🚀 UVX Master Guide	Portable, zero-install commands for CI/CD and automation.

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🧑‍💼 Principal SME Persona Approvals

The Cockpit now features a Multi-Persona Governance Board. Every audit result is framed through the lens of a Principal Engineer in that domain:

• 🏛️ Architecture
• 💰 FinOps
• 🛡️ Red Team
• 🧗 Quality
• 🌐 Infrastructure
• 🎭 UX/UI

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🚀 1-Click Production Pipeline

make deploy-prod triggers the following lifecycle:

1. Runs the Quick Safe-Build (make audit).
2. Compiles production frontend assets.
3. Deploys the Engine to Google Cloud Run.
4. Deploys the Face to Firebase Hosting.

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🤝 Ecosystem & Attribution

The AgentOps Cockpit is designed to leverage and secure the best-of-breed tools in the Google Cloud ecosystem. We explicitly acknowledge and leverage the excellent work from:

• GoogleCloudPlatform/agent-starter-pack: We leverage this as a core reference for the Agent Development Kit (ADK) patterns and Vertex AI Agent Engine integration.
• A2A Standard: Our implementation follow the Agent-to-Agent Transmission Protocol for swarm intelligence.

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Reference: Google Cloud Architecture Center - Agentic AI Overview