A cloud-native development operating model turns delivery into a paved road for the common case—automated, observable, and governed—while keeping exceptions explicit and reviewable.
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Platform engineering vs DevOps isn’t either/or. Use the right operating model for the bottleneck you actually have—ownership and feedback loops for DevOps, and self-service to reduce delivery toil for platform engineering.
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Human review in AI workflows works best when you decide what the human actually owns: decide, escalate, or stop—then measure whether the boundary helps.
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An agent eval harness is a versioned, scenario-based way to decide whether an agent workflow is safe and acceptable to ship. This guide shows how to design scenarios, golden tasks, trace assertions, and thresholds that stay useful as prompts, tools,
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A cost-aware architecture review keeps spend tied to design decisions. Use this framework to name the dominant cost driver, surface hidden costs, and make reliability-vs-spend tradeoffs explicit—before the design is locked.
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Agent autonomy boundaries turn “let the agent decide” into an operational policy: decide what it can do, escalate what it must route, stop real execution paths, and review scope changes to prevent autonomy creep.
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The agent loop should think and propose. The agentic system control plane should decide—enforcing policy, approvals, audit logging, retries, rollback/compensation, and escalation before side effects happen.
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Hiring authenticity meaning, in plain English: a credibility and consistency check. A candidate is authentic when their claims are honest, coherent, and reasonably verifiable.
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Candidate authenticity isn’t about catching liars. It’s about comparing a candidate’s claims across resume, interviews, and references—then documenting uncertainty as open questions so hiring decisions stay fair and defensible.
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In a safe production agentic system, the agent loop proposes actions while the control plane enforces policy, manages approvals/retries, records audit state, and contains failures.
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