agentops-cockpit 0.3.0

Production-grade Agent Operations (AgentOps) Platform

🕹️ AgentOps Cockpit

"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. While optimized for ADK, it provides framework-agnostic governance, safety, and cost guardrails for the entire agentic ecosystem—from CrewAI to LangGraph. Based on the Google Well-Architected Framework for Agents.

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🏗️ The Agentic Trinity

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

• ⚙️ 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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🌐 Framework Agnostic Governance

The Cockpit isn't just for ADK. It provides Best Practices as Code across all major agentic frameworks:

Whether you are building a swarm in CrewAI, a Go-based high-perf engine, or a Streamlit dashboard, the Cockpit ensures your agent maps to the Google Well-Architected Framework.

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🚀 Key Innovation: The "Intelligence" Layer

🛡️ Red Team Auditor (Self-Hacking)

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 safety filter bypasses.

🧠 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 & Framework Detection

Every agent in the cockpit is graded against a framework-aware checklist. The Cockpit intelligently detects your stack—Google ADK, OpenAI Agentkit, Anthropic Claude, Microsoft AutoGen/Semantic Kernel, AWS Bedrock Agents, or CopilotKit—and runs a tailored audit against corresponding production standards. Use make arch-review to verify your Governance-as-Code.

🧗 Quality Hill Climbing (ADK Evaluation)

Following Google ADK Evaluation best practices, the Cockpit provides an iterative optimization loop. make quality-baseline runs your agent against a "Golden Dataset" using LLM-as-a-Judge scoring (Response Match & Tool Trajectory), climbing the quality curve until production-grade fidelity is reached.

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⌨️ Quick Start

The Cockpit is available as a first-class CLI on PyPI.

# 1. Install the Cockpit globally
pip install agentops-cockpit

# 2. Audit your existing agent design
agent-ops arch-review

# 3. Stress test your endpoint
agent-ops load-test --requests 100 --concurrency 10

# 4. Scaffold a new Well-Architected app
agent-ops create my-agent --ui a2ui

You can also use uvx for one-off commands without installation:

uvx agentops-cockpit arch-review

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📊 Local Development

The Cockpit provides a unified "Mission Control" to evaluate your agents instantly.

make audit-all         # 🕹️ Run ALL audits and generate a Final Report
make reliability       # 🛡️ Run unit tests and regression suite
make dev               # Start the local Engine + Face stack
make arch-review   # 🏛️ Run the Google Well-Architected design review
make quality-baseline # 🧗 Run iterative 'Hill Climbing' quality audit
make audit         # 🔍 Run the Interactive Agent Optimizer
make red-team      # Execute a white-hat security audit
make deploy-prod   # 🚀 1-click deploy to Google Cloud

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🧭 Roadmap

• One-Click GitHub Action: Automated audits on every PR.
• Multi-Agent Orchestrator: Support for Swarm/Coordinator patterns.
• Visual Mission Control: Real-time observability dashboard.

View full roadmap →

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🤝 Community

• Star this repo to help us build the future of AgentOps.
• Join the Discussion for patterns on Google Cloud.
• Contribute: Read our Contributing Guide.

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Reference: Google Cloud Architecture Center - Agentic AI Overview