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Running Agentic AI Systems: Step-by-Step Walkthroughs, a Prompt Playbook, and an Actively Maintained GitHub Repo to Test, Harden, and Ship Production-Ready AI Agents with Repeatable Workflows
Can you explain your agent’s failures—or are you guessing in production?
Right now, “agentic AI” is everywhere—yet most teams are still shipping fragile demos: prompts taped to tools, silent failures, hallucinated outputs, and workflows that collapse the moment requirements change.
Your leadership doesn’t want “cool prototypes.” They want production agents that save time and money, work with real tools and data, and stay reliable when the environment shifts.
But agents fail in the same predictable tool calls break, prompts drift, memory contaminates results, costs explode, and nobody can prove quality or safety with repeatable tests.
If you’re responsible for delivering real agents in production, you don’t need more hype or theory.
You need an engineering blueprint : clear architecture, repeatable build steps, reliability patterns, and an enforcement loop that keeps agents safe, observable, and maintainable over time.
Running Agentic AI Systems is that blueprint.
Here’s what you’ll build and
Step-by-step walkthroughs from agent architecture → tool orchestration → evals → deployment-ready workflows
A prompt playbook ***: reusable system-prompt templates, dynamic prompting patterns, and anti-patterns that prevent drift
Tool reliability schemas, validation, retries, idempotency, timeouts, and failure handling that behaves like real software
Memory that helps (not corrupts): short/long-term strategies, retrieval patterns, and “memory hygiene” to reduce regressions
Proven execution patterns (ReAct, plan-and-execute, sub-agents) with traceable, debuggable execution—not magic
Guardrails + autonomy levels: safe escalation, human-in-the-loop design, and defenses against prompt injection/tool abuse
Evals-first iteration + GitHub repo : ready-to-run repos you can clone (agent scaffolds, eval harnesses, guardrails, observability starters) to test, harden, and ship faster
Not theory. Walkthroughs + templates + checklists.
Not tied to one framework. Patterns that survive tool changes.
Not “demo-only.” Built around evals + guardrails + observability .
Not an empty bonus. Actively maintained GitHub repo with ready-to-run repos you can clone and adapt.
If you’re done shipping brittle agent demos and you want repeatable workflows to test, harden, and ship production-ready systems, this book is for you.
Click “Buy Now” and start building agents you can defend in a review—and trust in production.
Right now, “agentic AI” is everywhere—yet most teams are still shipping fragile demos: prompts taped to tools, silent failures, hallucinated outputs, and workflows that collapse the moment requirements change.
Your leadership doesn’t want “cool prototypes.” They want production agents that save time and money, work with real tools and data, and stay reliable when the environment shifts.
But agents fail in the same predictable tool calls break, prompts drift, memory contaminates results, costs explode, and nobody can prove quality or safety with repeatable tests.
If you’re responsible for delivering real agents in production, you don’t need more hype or theory.
You need an engineering blueprint : clear architecture, repeatable build steps, reliability patterns, and an enforcement loop that keeps agents safe, observable, and maintainable over time.
Running Agentic AI Systems is that blueprint.
Here’s what you’ll build and
Step-by-step walkthroughs from agent architecture → tool orchestration → evals → deployment-ready workflows
A prompt playbook ***: reusable system-prompt templates, dynamic prompting patterns, and anti-patterns that prevent drift
Tool reliability schemas, validation, retries, idempotency, timeouts, and failure handling that behaves like real software
Memory that helps (not corrupts): short/long-term strategies, retrieval patterns, and “memory hygiene” to reduce regressions
Proven execution patterns (ReAct, plan-and-execute, sub-agents) with traceable, debuggable execution—not magic
Guardrails + autonomy levels: safe escalation, human-in-the-loop design, and defenses against prompt injection/tool abuse
Evals-first iteration + GitHub repo : ready-to-run repos you can clone (agent scaffolds, eval harnesses, guardrails, observability starters) to test, harden, and ship faster
Not theory. Walkthroughs + templates + checklists.
Not tied to one framework. Patterns that survive tool changes.
Not “demo-only.” Built around evals + guardrails + observability .
Not an empty bonus. Actively maintained GitHub repo with ready-to-run repos you can clone and adapt.
If you’re done shipping brittle agent demos and you want repeatable workflows to test, harden, and ship production-ready systems, this book is for you.
Click “Buy Now” and start building agents you can defend in a review—and trust in production.
268 pages, Kindle Edition
Published November 21, 2025
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Displaying 1 of 1 review
June 8, 2026
Pretty sure this book was mostly written by AI, but I guess that is to be expected
Displaying 1 of 1 review