Chapters
- 01 What Is an AI Orchestration Example? Real-World Systems That WorkWhat AI orchestration means, with patterns for sequential, fan-out, router, human-in-the-loop and agentic flows, durable execution and fallbacks.
- 02 Is MCP the Same as HTTP? The Truth Developers Need in 2026MCP is not HTTP. See how a context protocol for AI agents differs from a web protocol, when each fits, how MCP runs over HTTP, and common misconceptions.
- 03 The AI Agent Deployment Pipeline CI/CD Reality CheckWhy standard CI/CD falls short for AI agents: pipeline options, evaluation as a release gate, blue-green deploys for agent memory, pitfalls, stack choice.
- 04 The Hard Truth About AI Agent Production Deployment ChallengesWhy AI agent prototypes fail in production: framework traps, timeouts, state management, scaling, observability, cost control and model choice.
- 05 ChatGPT Codex Enterprise Deployment: A Practitioner’s Guide to Production AI at ScaleDeploying ChatGPT Codex in an enterprise: what it can do, context window limits, security, prompt specs, inference latency, monitoring and trade-offs.
- 06 Agentic Workflow Production Best PracticesWie bringt man agentische Workflows stabil in Produktion? Evaluierung, Observability nach MELT, Guardrails, gestufter Rollout, Fallbacks und Infrastruktur.
- 07 AI Agent Scaling Production Challenges: A Field GuideWhy AI agents that work in a demo break in production, and what to fix: error handling, latency, observability, cost control and context window limits.
- 08 Agents.md AI Agent Documentation: The Blueprint for Production AI in 2026How to write an Agents.md file covering an AI agent's purpose, guardrails, knowledge sources and actions, and how it ties to RAG versus fine-tuning.
- 09 AI Agent Monitoring and Observability Tools: Field Guide 2026What to measure when monitoring AI agents: tool-call completeness, argument validation, per-step hallucination and memory drift, plus tracing tools.
- 10 The Real Cost of AI Agent Architecture on AWS in 2026What drives AI agent costs on AWS beyond tokens: orchestration, memory, state, tool errors, data transfer, and Bedrock, Step Functions and EKS compared.
- 11 The A2A Protocol: A Production Deployment ExampleRunning the A2A agent protocol in production: architecture, agent cards, task lifecycle, observability, error handling, security and a checklist.
- 12 A2A and MCP for Multi Agent Orchestration: The 2026 PlaybookMCP connects agents to tools; A2A connects agents to each other. Covers discovery, state, auth, observability, security and when to skip both protocols.
- 13 A2A Protocol vs API for Agents: The Buying Guide You Actually NeedWhen to use the A2A protocol and when plain REST APIs are enough for agent communication, with trade-offs in security, performance and adoption cost.
- 14 AI Agents Are Just Distributed Systems With PretensionsWhy AI agents fail like distributed systems: missing observability, unbounded retries and weak communication layers, and how to build them reliably.
- 15 Agentic Workflow Deployment vs Traditional Deployment: The 2026 PlaybookHow deploying LLM agents differs from deploying ordinary services: observability, scaling, testing, guardrails, human review and common mistakes.
- 16 AI Agent vs Workflow Automation Production: The 2026 Field GuideAI agent or workflow automation? The difference is the failure mode. Why workflows come first, when a full agent pays off, and how to monitor and deploy.
- 17 Agentic AI Production Readiness Checklist: The 2026 Field GuideA production readiness checklist for agentic AI: memory, guardrails, observability, human oversight, cost controls, security, evaluation, rollback.
- 18 Canary Deployments for AI Agents: The Only Guide You'll NeedHow to canary an AI agent release: shadow mode, traffic split, playback and cost-and-latency canaries, defining good behaviour, instrumenting and ramping.
- 19 Gemini 3.5 Flash Computer Use: What Actually Works in ProductionWhat Gemini 3.5 Flash computer use is and how to build on it: the agent loop, context management, action limits, enterprise fit, cost, architecture traps.
- 20 The Agentic Workflow Rollback Strategy: Why Your 2026 AI Agents Need ItHow to undo what an AI agent did: snapshot and restore, replay with correction, and compensation patterns, a code approach and when not to roll back.
- 21 What Size GPU Cluster Do I Need for AI Agents?Sizing a GPU cluster for AI agents: define the workload, estimate throughput per GPU, factor in concurrency and latency, and weigh on-prem against cloud.
- 22 AWS vs Kubernetes for Multi-Agent Systems: A Practitioner's GuideAWS managed services or Kubernetes for multi-agent AI systems? Compares control, cost, GPU pricing and monitoring, with a framework for deciding.
- 23 AI Agent Production Infrastructure: The Missing GuideWhat AI agents need to run in production: a runtime built like a distributed system, memory, latency, incident response, observability and safety guards.