AI Agent Orchestration & Infrastructure for Software Engineers

₹600

You already know how to build backend systems. This guide shows you how AI agents fit into what you already know. If you write backend code, you're closer to building production AI agents than you think. Queues, retries, microservices, logs — they all carry over. What's new is a model that makes decisions, and a handful of patterns for keeping it in line. AI Agent Orchestration & Infrastructure for Software Engineers is a focused, 15-page quick-start guide that walks through exactly those patterns — one idea per chapter, in plain language, each with a concrete, practical example you can map directly onto systems you already run. What you'll learn: • What an AI agent actually is (LLM + tools + memory + a loop) — and when you don't need one • Function calling and the Model Context Protocol (MCP) — the "USB-C for AI tools" • Orchestration patterns: chaining, routing, parallelization, orchestrator–worker, evaluator–optimizer • Memory and RAG — how agents "remember" anything at all • Multi-agent communication: handoffs, shared state, and the A2A protocol • Production infrastructure: durable execution, retries, idempotency, sandboxing, cost control • Observability and evals — catching failures that don't throw errors • Security: prompt injection, least privilege, and containment strategies • A full end-to-end worked example (a support-ticket triage agent) • A pre-ship checklist and a step-by-step learning path for your next few weekends Who this is for: Backend and software engineers who want a fast, practical on-ramp to building real AI agents — without wading through academic ML papers or bloated framework docs.