Brains vs Hands: How to Run AI Agents Safely in Production - Viren Baraiya

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    Viren Baraiya argues that a production agent harnessAn AI agent harness is the software framework that packages a model with tools, instructions, context management, execution controls, and user interaction. is an application, not just an LLM loop. It may coordinate background work, events, humans and multiple specialized agents over long periods, so infrastructure failures must not erase progress or completed side effects.

    The architecture separates reasoning from execution. The model proposes what to do next, while deterministic code decides how an action is performed, applies required human gatesHuman-in-the-loop command approval requires a person to review and authorize a consequential AI action before it executes. and records whether it has already happened. Viren Baraiya connects this pattern with durable workflowsDurable workflow execution records an AI workflow's progress so it can recover and continue after interruptions instead of starting again. and late-bound sagas.

    A prepared SRE demonstration compiles an agent's proposed steps into a Conductor workflow, investigates a problem, performs a rollback and verifies recovery. A later iteration recognizes the rollback already happened. The example illustrates planning combined with predictable execution; it is a staged demonstration rather than independent evidence of production reliability.

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    Viren Baraiya in a blue shirt looks toward the blue and white "DURABLE AGENTS" headline. Framed in blue with WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 8 October 2026 and duration 16m 49s.

    An agent can propose a plan while a deterministic harness owns execution, durability and required gates, making long-running production workflows easier to control and recover.