Insights
Notes from the work.
Practical writing on building enterprise AI that reaches production.
Bedrock Managed Agents treats an AI agent like a workload, not a chatbot
AWS and OpenAI put IAM roles, durable sessions, approvals and CloudTrail inside the agent runtime. What that changes for AWS-heavy enterprises, and what it doesn't.
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When a Copilot agent keeps working after you leave, who owns what it does?
Microsoft's Autopilot, heading into private preview, turns delegation into a standing mandate. The governance questions that creates for enterprises running on Microsoft 365.
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Generating enterprise apps got easy. Copilot Managed Runtime is about the hard part.
Microsoft now hosts and governs the code people create with Copilot. What a managed runtime solves for AI-built internal apps, and what it leaves to the business.
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What should an enterprise build once and reuse across every AI agent?
Salesforce's Trusted Enterprise AI Harness names six shared capabilities and a control plane. The same layers show up at AWS, Microsoft and Google. Which ones you should own.
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Model safety is becoming an enterprise architecture layer, not just a provider policy
Anthropic's Enterprise Frontier Safeguards split custody: the provider runs misuse detection, the customer holds the data, keys and review. Who does what, and what it costs you.
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Gemini can now act inside Microsoft 365. Does your collaboration suite still pick your agent platform?
Google's preview actions for OneDrive, Outlook, SharePoint and Teams make cross-vendor agents practical. The real decision moves to identity, permissions and governance.
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In life sciences AI, model access is becoming a governed asset
Anthropic's Life Sciences Verification Program ties capabilities to verified organizations, scoped grants and monitored use. What it means for pharma and biotech teams.
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OpenAI's Agents API turns the agent loop into a managed service. The rest is still yours.
Managed harness, subagents, context compaction and now computer use. What an enterprise can hand to the model provider, and what it cannot.
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What a production-ready enterprise AI stack actually needs in 2026
Five core layers, six cross-cutting controls and one owner: a field guide to what separates an enterprise AI system from a model and a prompt.
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