AI systems become useful when a team can define what good means, trace the evidence behind an answer and control the boundary between a proposed action and a real consequence.
Methodfield has published three new bilingual articles for those decisions. Each article combines current sources with a reusable operating artifact and an original localised diagram.
Evaluation becomes a release operation
AI Evaluation Operations: How to Know a Workflow Is Ready for Production shows how to move beyond a model benchmark.
The article connects representative cases, deterministic checks, calibrated human and model graders, regression comparison, a release gate and production feedback. Its working artifact is an Evaluation Plan: a versioned record of the workflow boundary, failure classes, evidence, release thresholds, runtime signals and rollback authority.
Enterprise knowledge receives a contract
A Knowledge Assistant You Can Trust: Beyond Basic RAG separates retrieval from knowledge governance.
Its Knowledge Contract defines source ownership, user access, authority, effective dates, citation requirements, conflict handling and refusal rules. The article also explains when direct lookup is enough and when a multi-source, multi-step retrieval path is justified.
Browser automation receives an acceptance test
Computer-Use Agents, APIs or RPA? Choose the Right Automation Boundary treats computer use as a controlled last-mile adapter rather than the default integration method.
The Browser-Agent Acceptance Test checks normal completion, layout changes, prompt injection, permissions, wrong-target actions, timeouts, verification failure and manual takeover. It keeps consequential authority in deterministic policy and accountable human approval.
Connected in the Knowledge Map
All three articles now appear as editorial nodes in the Methodfield Knowledge Map.
The evaluation article connects to case evidence, quality and post-launch ownership. The knowledge article connects to authority, quality and evaluation. The computer-use article connects agent selection and authority controls with the evaluation system required before production.
Together, the release forms one practical sequence:
Define acceptable behaviour → govern the evidence → choose the narrowest automation boundary → test before release → learn from production.