You move from “we stood up an agent” to “we can prove it’s safe, compliant, and improving”—with enforceable controls, testable acceptance, and audit-ready telemetry. TL;DR Convert stakeholder intent into 8–12 word, binary pass/fail criteria and wire them into automated evals. Embed governance in the prompt/context + tool pipeline (PII detection, masking, policy checks, structured telemetry) … Read more
You move agentic workflows into production with provable accountability—who authorized what, what data was touched, and what side effects occurred—without slowing the business into approval gridlock. TL;DR Implement “No Delegator, No Action,” require scope IDs on every tool call, and tier actions by blast radius with approvals. It works because enforcement happens at the tool … Read more
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
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