The AI-Native Engineering Handbook
Production-tested patterns for building autonomous agents
A structured, production-tested guide to the patterns that make autonomous agents reliable. Each chapter includes diagrams, pseudocode, decision frameworks, and the failure modes you will hit in production.
Start here. The handbook is a single reading path, not a reference pile: begin with Chapter 0, “The Agentic Landscape,” and each chapter builds the vocabulary and decisions the next one assumes.
Begin Chapter 0The Agentic Landscape
A map of the agent ecosystem: autonomy levels, tool use, and the design patterns every AI-native engineer should know.
Outcome: you can map autonomy levels and pick a pattern.
HDBK-001The ReAct Pattern
Learn the ReAct pattern: how LLMs interleave reasoning (Thought), tool calls (Action), and feedback (Observation) to solve complex tasks.
Outcome: you can apply the Thought / Action / Observation loop.
HDBK-002The Plan-and-Execute Pattern
Learn how Plan-and-Execute agents separate high-level planning from low-level execution using task lists, DAGs, and a re-planner.
Outcome: you can separate planning from execution and add a re-planner.
HDBK-003The Reflection Pattern
Learn the Reflection pattern: how a Generator-Critic loop with a structured rubric reduces hallucinations and improves output quality.
Outcome: you can run a Generator-Critic loop with a structured rubric.
HDBK-004Multi-Agent Collaboration
Learn when multi-agent collaboration is worth the cost: orchestrators, worker handoffs, debate, topology choices, observability, and production failure modes.
Outcome: you can judge when multi-agent collaboration is worth the cost.
HDBK-005Tool Use and Skill Registries
Design reliable agent tools and a governed skill registry with schemas, permissions, discovery, idempotency, approvals, observability, and failure controls.
Outcome: you can design governed tools and a skill registry.
HDBK-006Memory & Context Management
Design reliable agent memory with explicit context budgets, validated compaction, governed writes, permissioned retrieval, provenance, temporal consistency, and lifecycle evaluation.
Outcome: you can design memory with budgets, governed writes, and lifecycle evaluation.
HDBK-007Human-in-the-Loop
Design human-in-the-loop agent systems with risk-based autonomy, enforceable approval gates, safe escalation, exact authorization, and measurable oversight.
Outcome: you can set risk-based autonomy with enforceable approval gates.
HDBK-008Observability & Evaluation
Build observable and evaluable AI agents with correlated traces, governed telemetry, representative test sets, calibrated graders, statistical regression gates, canaries, and drift detection.
Outcome: you can wire traces, evals, and regression gates for agents.
The chapters are sequential — start at the beginning and the patterns compound.
Start with Chapter 0