Overview

According to a document from 2025, most teams hit the same wall with autonomous AI agents: the demo works, then production turns into chaos. The same input yields different outputs. Tool calls break with hallucinated parameters. Retries spiral into expensive loops. Latency spikes, users lose trust, and ""prompt tweaks"" stop helping. The LLMOps Handbook: Architect, Debug, and Scale Autonomous AI Agents is built for that moment. It treats agent reliability as an engineering discipline, not a prompt contest. You'll get a practical pattern language for designing agent ""supply chains,"" building mistake-proof tools, controlling non-determinism, and shipping agentic systems with the same rigor you expect from modern DevOps: testing, guardrails, observability, and cost controls. You'll learn how to: Replace the ""God Agent"" with routing, handoffs, and supervisor-worker architectures that scale cleanly. Design tool interfaces with strict schemas and validation layers that prevent illegal calls. Engineer reliability with circuit breakers, fallback chains, and self-correction loops. Debug agent behavior using tracing, DAG visualization, and OpenTelemetry-style observability. Build evaluation-driven development workflows using golden datasets and LLM judges. Ship safely with human-in-the-loop controls, token budgeting, semantic caching, and prompt blue/green rollouts. If you're an AI engineer, platform engineer, or technical lead who needs LLMOps that holds up under real traffic, this is the handbook you'll reach for when the incident page lights up. Buy The LLMOps Handbook and start building agents that are boring, reliable, and profitable.

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