AI Agent Ops — Operating Agentic Systems in Production
From agent demos to agent operations: observability, guardrails, evaluation gates, cost control, and incident response for production LLM agents.
Get the full PDF: AI Agent Ops
View book & download →Why AI Agent Ops?
Agent demos are easy; agent operations are not. This PDF documents how Hexalian runs agentic systems in production: workflow design, evaluation, guardrails, observability, cost control, and change management when prompts and tools evolve weekly.
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What you'll learn
- Agent workflow design: boundaries, tools, and human escalation
- Evaluation gates that catch silent drift before users do
- Guardrails against prompt injection, data exposure, and abuse
- Tracing agent runs and diagnosing failures in minutes
- Token budgets, caching, and routing for cost control
- Incident response and governance for agent fleets
- Keeping RAG knowledge bases correct as documents change
Start with AI Automation
If you are earlier in the journey, read AI Automation first for n8n and workflow foundations. Add AI Agent Ops when agents become business-critical infrastructure.
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