A complete technical learning path from Python and APIs to reliable workflows, retrieval and safe AI agents—with a field guide to turning capability into automation projects.
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Learn to handle duplicate events, malformed data, outages and uncertain model outputs. Worked code, evaluation practices and recovery paths connect a prototype to responsible delivery.
A payment or lead event may be delivered more than once. A durable identifier and state transition keep retries from creating duplicate work.
Test the replay, partial failure and recovery path. Confirm the final state before telling the operator the job is done.
A simplified teaching model. The complete book includes worked applications and their limitations.
The book, mobile edition and reader support the same curriculum. Your capability grows through completed work and critique.
Technical foundations, runnable patterns, operational reasoning and AI-system boundaries.
Work through the task before comparing your reasoning with the reference answer. Keep better-supported alternatives.
Build an artefact, test it, describe its limits and make your contribution visible. Synthetic work stays clearly labelled.
Twelve technical chapters build progressively; an additional field guide connects workflow craft to diagnosis, pilots and service delivery.
Variables, control flow, data structures, files, errors and tests.
Schemas, SQL, transactions, migrations and durable state.
HTTP, authentication, validation, pagination, retries and reconciliation.
Triggers, idempotency, queues, observability, exception paths and handover.
Representative tests, scorecards, baselines and release decisions.
Capability boundaries, prompts, context and structured outputs.
Redaction, consent, permissions and the limits of sanitisation.
Chunking, retrieval, citations, freshness and missing evidence.
Bounded tools, approval paths and reversible actions.
Latency, reliability, budgets, logging and incident recovery.
Least privilege, prompt injection, kill switches and audit evidence.
Portfolio projects, client diagnosis, bounded pilots and workflow-service offers.
A good automation offer begins with a recurring job, its exceptions and a bounded improvement the buyer can evaluate.
Map triggers, handoffs, duplicate work and recovery. Quantify a baseline before choosing a tool.
Agree a scope, permission boundary, acceptance tests and the condition that stops the pilot.
Explain operation, alerts, reconciliation and ownership. Keep demonstrations distinct from client results.
Choose an edition for the work you want to practise.
The complete PDF and EPUB together in one ZIP, plus access to the corresponding guided reader. PDF and EPUB downloads are included in the stated price.
No. It provides concepts, practice and a system for building evidence. Outcomes also depend on completed work, independent critique, experience, market fit and the decisions of employers or buyers.
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Teaching examples use fictionalised or synthetic situations and credited public sources. Confidential personal, employer and client details have been excluded. The author's name appears as publication credit.
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