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’ll ship agentic workflows that are economically repeatable and operationally governable—without defaulting to expensive autonomy, unbounded tool access, or prompt-only “safety.” TL;DR Implement dual-mode execution: use a constrained single-shot path for low-risk tasks and reserve an autonomous harness for multi-step work. Route by risk (blast radius + tool scope + change type) so cheaper models … 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
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