Learn to govern, monitor, and scale AI agents in production. Free guides from beginner to advanced.
Learn what AI agent management means, why it matters in production, and how to set up a control plane for your agent fleet.
Learn the Model Context Protocol (MCP) — how it works, why it matters, and how to connect AI agents to 73+ tools via JSON-RPC 2.0.
Seven battle-tested practices for governing AI agents in production — from audit trails to kill-switches.
One control plane for CrewAI, LangChain, OpenAI, and custom agents. Unified monitoring, governance, and cost tracking.
Connect your CrewAI crews to Dobby for unified monitoring, cost tracking, and governance. Step-by-step integration guide.
Set up an LLM gateway in 5 minutes. Route all AI requests through one endpoint for cost tracking, security, and multi-provider support.
Route LangChain agent LLM calls through a control plane for cost tracking, governance, and multi-provider support.
Configure approval gates so AI agents pause before risky actions and wait for human review. Step-by-step setup guide.
Get started with the Dobby REST API. Create tasks, manage agents, query costs, and automate your agent fleet programmatically.
Track AI costs per agent, set token budgets, configure provider quotas, and get alerts before you overspend.
Route AI requests to the right LLM provider automatically. One endpoint for OpenAI, Anthropic, Google, Mistral, and 9 more.
Learn how to stop all AI agent activity instantly with a kill-switch. Scoped controls, fast propagation, and recovery procedures.
Map SOC 2 Trust Service Criteria to AI agent controls. Audit trails, access controls, encryption, and monitoring.
Ensure your AI agent data stays within designated regions. GDPR, data sovereignty, and regional compliance for AI workloads.
Set up enterprise SSO for your AI agent platform. Support for Okta, Azure AD, Google Workspace, and 4 more identity providers.
Run AI agents autonomously on schedules with governance pre-flight checks. Cron-driven execution with budgets, approvals, and kill-switch.
Track, analyze, and optimize AI agent costs. Per-agent breakdown, provider comparison, forecasting, and budget automation.
The architecture behind an AI agent control plane. Multi-tenant isolation, policy engines, audit trails, and gateway design.
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