Every claim carries an evidence status — and you can filter the archive by it.
Before funding an AI pilot, agree in writing what would stop it: the evidence, the owner, the review date, and the action attached to each condition.
Before approving a new cluster for an AI experiment, ask which requirement makes it necessary — and choose the smallest route that meets the workload's actual constraints.
Before adding another AI tool, measure outcomes, workload quality, reliability, useful-outcome economics, authority failures, and actual usage.
A practical architecture pattern for separating what an agent can do from what it is allowed to do AI systems do not become production
An architecture review can sound decisive even when the underlying evidence is thin. A demo completes, two AI agents agree, or a vendor benchmark
Eight review dimensions, the evidence to ask for in each, and the red flags to watch for before an agentic system earns more autonomy.
Before building an AI operating system, test whether the real problem is ownership, integration, one bounded AI workflow, or repeated cross-workflow governance.
MCP gives agents connectivity. Enterprise architecture still needs identity, delegated authority, policy, evidence, and revocation.
A comparative synthesis of OpenAI, Anthropic, and DeepSeek harness architectures—and why the harness should be treated as a replaceable runtime layer, not the whole AI system.
A framework for finding when better models and more automation stop being the constraint—and authority, economics, policy, coordination, or judgment take over.
A governed AI architecture starts with actors, state, authority, policy, evidence, and workflow before models, agents, databases, or orchestration are selected.
A practical test for deciding when persistent state, authority, workflows, and decision memory justify an AI operating-system pattern—and when a simpler tool is better.