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AI Engineering

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 & Automation

Your AI Agent Is Not Safe Just Because the Prompt Says So

AI agents move too fast for prompt-based safeguards and endless approvals. Production autonomy requires scoped access, auditability, hard stops, and rollback.

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AI Engineering

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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More from PRESHai

AI & AutomationAug 3, 2026

The AI Fallback Plan That Works Under Pressure

A fallback plan belongs inside every AI workflow that matters to the business. Build the route, name the owners, and test the handoff while the stakes are low, not during an outage.

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AI & AutomationAug 3, 2026

Your AI Strategy Needs an Exit Plan

A company that brings an AI provider deep into the business needs an exit path from the first conversation: who controls the policy layer, who owns the data path, and how to keep the ability to choose the next turn.

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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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AI EngineeringJun 16, 2026

What Is an Agent Harness? (And Why It Matters More Than the Model)

An agent harness is everything wrapped around the model that makes it reliable: the loop, tools, context management, guardrails, and the verify step. Here is what that means in plain terms, and why it decides whether AI agents actually work in the IT channel.

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