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Some AI models answer instantly. Others, often called reasoning or thinking models, take extra time to plan, work through steps, and check themselves before answering.

In practice: Use fast models for drafts and summaries. Switch to a thinking model for analysis, planning, or anything with several steps.

An agent doesn’t just answer questions. It takes action: it reads files, sends messages, and updates systems. Anything that can act needs an owner, a clear job, and limits.

In practice: For every agent, write down who owns it, what it can access, what it can do without asking, and how you would switch it off.

Agents work in loops: plan, act, check the result, adjust, and repeat until the job is done. The quality comes from how the loop is designed, especially what it checks and when it stops.

In practice: Define what “done” means up front, build in a step that tests the result, and put a person at the points where a mistake would be costly.

The harness is everything around the model: your instructions, tools, data connections, guardrails, and the tests that prove it works. Models change every few months. A strong harness lets you swap them without starting over.

In practice: Keep your instructions, test examples, and tool connections in systems you control, not inside one vendor’s chat window.

Models are becoming a commodity. What makes AI valuable to you is your organization’s knowledge. Satya Nadella recently called this the reverse information paradox ↗: to get value from AI you share your expertise, and that expertise can end up improving someone else’s product.

In practice: Use enterprise AI tools that commit not to train shared models on your data, keep your own evaluations and learning in-house, and set clear rules for consumer AI apps.

Sources: The New Stack ↗ · CXOToday ↗

Zero trust means nothing is trusted by default, inside or outside the network. It rests on three rules: verify explicitly, use least-privilege access, and assume breach. Agents need the same treatment as people: their own identity, only the access the task requires, and logs of everything they do. Enterprise platforms that carry your existing permissions, labels, and audit logs into AI give you a head start.

In practice: AI surfaces whatever people can already reach. Clean up overshared files, label sensitive content, and give each agent its own identity before you roll it out.

Sources: NIST SP 800-207 ↗ · Zero Trust overview ↗