Architecting Production-Ready Gen AI and Agentic AI Systems - Version 4 - November 2026
Architecting Production-Ready GenAI & Agentic AI Systems — The AI Architect's Playbook is a technical guide to answering those questions across the enterprise AI lifecycle.
WHAT EDITION 4 STRENGTHENS - Consequential agent writes and enterprise integration. Follow a proposal through independent human approval, a scoped invocation, a system-of-record write and post-action reconciliation. Examine canonical command integrity, idempotency keys, write barriers, revocation races, uncertain outcomes and duplicate or out-of-order events.
- Governed context and semantic architecture. Decide when to use authoritative APIs, structured business data, semantic definitions or RAG. Design hybrid dense/sparse retrieval, re-ranking, chunking and embedding/index lineage; enforce source authority, effective dates, tenant entitlements, freshness watermarks, traceable citations and fail-closed retrieval.
- The production agent harness and runtime. Design the planner, tool broker, policy engine, memory/state boundaries, execution budgets, human approvals, durable checkpoints and revalidation on resume. Separate user, agent and workload identities; constrain delegation and prevent tool discovery from becoming execution permission.
- MCP, external tools and multi-agent handoffs. Treat tool schemas, protocol servers and agent-to-agent messages as versioned trust boundaries. Assess authentication, tool side effects, prompt injection, sandbox isolation, shared state, conflicting specialist recommendations and cross-agent failure containment.
- A self-service engineering golden path. Connect maintained templates, gateways and isolated AI-assisted development to scoped credentials, instrumentation and CI/CD. Apply static analysis, dependency/secret checks, unit and integration tests, agent-behaviour evaluations, risk-based approvals, canary releases and rollback when prompts, tools or permissions change.
- Evidence-led evaluation and release decisions. Measure retrieval, response, agent trajectory, business outcome and operational quality separately. Build held-out datasets, critical-case slices, confidence-aware thresholds, negative tests and regression gates. Link prompt aliases, model routes, tool contracts and production traces to versioned release evidence.
- Resilience, FinOps and operating ownership. Engineer bounded queues, backpressure, retry/timeout behaviour, approved degradation and recovery from partial completion. Compare managed and self-hosted model economics using cost per accepted task, latency, token/tool budgets, human-review burden and incident operating costs.
ONE CONTINUOUS ENTERPRISE CASE The fictional NovaCred case runs from AI-first classification and a six-layer reference architecture through platform selection, knowledge retrieval, agent orchestration, enterprise APIs, runtime enforcement, evaluation, threat modelling, observability, FinOps, governance and a 34-gate engagement checklist.
Worked artefacts include retrieval contracts, source manifests, tool and authority records, prompt registry entries, evaluation gates, trace/evidence requirements, cost models and release decisions.
Designed for Enterprise AI Architects, Solution Architects, AI/Platform Engineers and engineering leaders, Edition 4 shows how to turn architecture into contracts, enforceable boundaries and testable release criteria.
Its NovaCred examples are illustrative designs and specified tests—not claims that production controls have already been executed or independently certified.
