Agentic AI Systems Engineering: Planning, Tool Use, Memory, Verification, and Reliable Autonomous Workflows

Agentic AI Systems Engineering is a guide to building systems where a language model plans, calls tools, maintains state, and acts over many steps without a human approving each one. An agent is a control system with a model inside it. The model supplies judgment; the surrounding system supplies bounds, verification, permissions, and the ability to stop. Most production agent failures trace to that surrounding system rather than to the model's reasoning - which is why this book spends far more time on execution loops, tool schemas, and permission scoping than on prompting. Fourteen chapters cover the full agentic stack: - Agentic systems as software architectures: goals, state, policies, and loop design - Planning and task decomposition: explicit task graphs, verifiable subgoals, bounded replanning - Tool use and external actions: call boundaries, strict schemas, scoped permissions, untrusted results - Memory and persistent state: working memory, durable facts, measured retrieval, bounded summarization - Verification and self-checking: output validation, genuinely independent cross-checks, domain constraints - Workflow reliability: classified retries, measured timeouts, idempotent writes, designed recovery - Human oversight and escalation: meaningful approval gates, checkable exception criteria, informative handoffs - Security and permission boundaries: phase-scoped privilege, tool-layer credentials, sandboxing, audit logs - Observability and evaluation: decision-level traces, outcome-based success, actionable failure taxonomies - Cost and latency management: per-run attribution, per-step model selection, caching, latency budgets - Multi-agent coordination: justified decomposition, message contracts, partitioned state, global termination - Testing agentic systems: deterministic harnesses, adversarial inputs, failure-mode coverage - Production agentic platforms: bundled deployment, model version pinning, layered cost limits >Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and a short set of practical checks. Each chapter closes with a worked scenario showing how these decisions interact under real pressure. Five appendices cover architectural comparisons, a four-level agent maturity model, a failure-mode reference organized by symptom, adoption sequencing, and common questions. Written for engineers building AI agents, copilots, and autonomous workflows in production.