From Working Agent to Production Agent: Systems Engineering Handbook
Stop building fragile AI toys. Engineer resilient, enterprise-grade agentic systems.There is a massive gap between a script that works locally and an autonomous agent that survives the chaos of a live production environment. When you deploy AI workloads, you are instantly hit with API rate limits, catastrophic infinite loops, token-driven billing spikes, and prompt injection attacks.From Working Agent to Production Agent: Systems Engineering Handbook is a no-nonsense, comprehensive PDF manual designed to bridge that gap. This handbook strips away the theoretical hype and focuses strictly on the practical architecture, security patterns, and cloud deployment manifests required to build robust, fail-safe AI systems.📦 What You Will Get InsideThis handbook delivers six deep-dive modules focused entirely on production rigor, complete with ready-to-use code, container configurations, and architectural blueprints: Module 1: Advanced Error Taxonomy & Jitter Recovery – Learn to classify failures (Transient, Deterministic, Contract Drift) and implement a production-ready Python decorator using tenacity for exponential backoff and dynamic Retry-After parsing. Module 2: Breaking Infinite Loops – Build the "Hamster Wheel" circuit breaker. Discover how to hash incoming tool arguments and utilize a sliding window cache to block repetitive invocations and halt runaway agent loops. Module 3: Gateway-Level Cost Controls & Semantic Caching – Set up hierarchical token and dollar budgets using gateway proxies. Follow a step-by-step guide to building a Valkey/Redis semantic cache using KNN cosine distance to serve responses in 27 ms for zero upstream token cost. Module 4: Dual-Layer Observability & the RAG Triad – Separate standard APM telemetry from LLM-specific tracing. Implement OpenTelemetry spans to map 50-step execution trees and run automated evaluators to catch silent hallucinations. Module 5: FastAPI Web-Worker Containers & Cloud Deployments – Decouple public endpoints from slow agent planning using background queues (Celery/Amazon SQS). Includes unprivileged Dockerfiles and step-by-step YAML manifests for AWS ECS Fargate and Azure Container Apps. Module 6: Zero-Trust Security & Latency-Optimized Guardrails – Wrap your models in deterministic, infrastructure-level policies. Implement a blazing-fast dual-pass guardrail system using Llama Prompt Guard 2 to catch prompt injections in under 80 ms. 🎯 Who Is This For? Software Engineers & Architects: Developers transitioning from building AI prototypes to architecting scalable, secure, and highly available agentic systems in the cloud. DevOps & Platform Engineers: Cloud professionals tasked with containerizing AI workloads, setting up semantic caches, and enforcing strict financial guardrails. AI Practitioners: Builders looking to stabilize their execution loops, eliminate retry storms, and secure their endpoints against adversarial prompt injections. ✨ Why This Handbook?"Production stability requires practical engineering logic, not just better prompts."Standard AI tutorials stop once the agent prints a response to the terminal. This handbook starts exactly where they leave off. It provides the strict, production-ready frameworks required to run asynchronous queues, manage state, and deploy securely to AWS or Azure without exposing your architecture to infinite billing cycles.Format: High-Resolution PDF Length: 6 Comprehensive Systems Engineering Modules Style: Clean blueprints, practical code implementations, and strict architectural standards.Click "I want this!" to download the handbook and upgrade your deployment architecture today.
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