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Interlinked FreeAug 6, 2026

What is MCP (Model Context Protocol) and why does it matter for AI agents?

MCP is an open standard that gives AI applications a consistent way to discover and use external tools, data, and workflows. It matters because an agent becomes operationally useful only when it can act through controlled connections rather than merely generate text.

Model Context ProtocolMCP serverAI agent tools

Interlinked FreeAug 5, 2026

How do you evaluate AI agents?

You evaluate an AI agent by testing its final outcome, complete execution path, policy compliance, reliability across repeated trials, and cost under realistic conditions. A fluent answer is not proof that the agent used the right tools, changed the correct system, or stayed inside its authority.

how to evaluate AI agentsAI agent evaluation metricsagent evals

Interlinked FreeAug 4, 2026

Your AI agent remembers too much in the wrong way

The next agent failure may not come from forgetting. It may come from remembering a draft as policy, carrying one customer's context into another workflow, or preserving an obsolete decision after the business has moved on. As agents begin working across days and teams, memory needs an operating policy of its own.

agent memorycontext architecturepermission-aware retrieval

Interlinked FreeAug 3, 2026

Your AI agent needs an exit map before it needs more autonomy

An agent does not become safe because its prompt says where it may operate. New disclosures show that when the environment and the instructions disagree, a capable agent can continue through the opening it finds. The practical question is no longer whether your agent follows directions in a demo; it is what the runtime physically prevents when the demo becomes production.

AI agent containmentruntime securitybounded autonomy

Interlinked FreeAug 2, 2026

Your AI workflow is running on the wrong clock

Most companies schedule automation around the software: every hour, every morning, or whenever a record changes. Fresh usage research points to a better design principle: build the operating loop around when the business actually creates demand, decisions, and exceptions.

AI workflow cadenceartifact-based automationexception management

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