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AI Engineering articles

AI EngineeringSep 11, 2026

Shared memory between AI agents is a state management problem

Shared memory can help multiple AI agents coordinate, but an unstructured common store creates stale context, conflicting updates, and security risk. Learn how to design agent memory as governed production state.

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AI EngineeringAug 24, 2026

Your Agent Isn't Done Until It Reports Back

An agent that completes the work but never delivers the outcome has not completed anything. The report-back is a first-class step in the harness, with the same engineering rigor as the task itself. Here is how to build it.

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AI EngineeringAug 7, 2026

Agent Plugin 1.0 - A Unified Framework for Agent Plugins

Agent Plugins is a new open, vendor-neutral spec for packaging Agent Skills and MCP servers into portable plugins, backed by Amazon, Cursor, Microsoft, OpenAI, Vercel, and now Google. Here is what the format actually does, who is behind it, and why our agent harnesses, including PRESHos, support it.

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AI EngineeringJul 8, 2026

Agent RBAC: Five Ways to Scope What an Agent Can Do (And When to Use Each)

Role-based access control for agents is not user RBAC with a new label. An agent acts, so its permissions are a matrix of what it can see, suggest, and execute. Here are the five strategies, when each fits, the tradeoffs, and how to implement them in Claude Code, Codex, and your own harness.

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AI EngineeringJul 1, 2026

The Verify Step: Why Agents Fail in Production (And How to Catch It Before the Customer Does)

The verify step is where an agent proves the work actually happened before anyone calls it done. It is the single cheapest addition to a harness and the one that separates a demo from something you can run near a customer's quote, ticket, or data.

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AI EngineeringJun 24, 2026

The Model Is Not the Moat. The Harness Is.

Enterprise AI strategy is shifting from model selection to the harness around models: routing, fallbacks, context, permissions, memory, observability, and review.

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