Adopt agentic engineering as a discipline and your organization moves from demo-grade agents to production-grade systems with named decision boundaries across reasoning, execution, governance, operations, and economics. TL;DR Agentic engineering is the discipline of running AI agents as production systems — bounded autonomy, named decision boundaries, runtime governance. Once a system can plan, call tools, … Read more
You can ship agentic AI into production with predictable unit economics, audit-ready governance, and the ability to swap models/vendors without rewriting pipelines. TL;DR Adopt a Decoupled Planes architecture (Control/Data/Model/Experience) so models can change without moving data or reworking governance. Implement Hybrid Retrieval (metadata gate → vectors → rerank → full logging) to improve precision while … Read more
Adopt this model and your teams can ship agentic systems faster by placing controls at the right decision boundaries instead of forcing every use case through the same manual governance path. TL;DR Use a composable control stack: governed zones for structural segregation, policy-bound data products for reusable AI inputs, and runtime enforcement at retrieval, memory, … Read more
Teams that adopt this architecture move from opaque prompt experiments to production-grade workflows — with explicit decision boundaries, bounded autonomy, and measurable rollback paths. The patterns below echo Anthropic’s Building Effective Agents (Dec 2024), Google’s SRE Workbook practices on bounded systems and failure budgets, and a hard operational reality: most agent failures in production are … Read more
Readers will be able to assign the right governance pattern to each agentic AI use case, place controls at the correct decision boundary, and reduce compliance risk without defaulting to a one-pattern-fits-all architecture. TL;DR Classify every AI use case by risk before design approval, then select a primary governance pattern using the default rule: high-risk … Read more