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Release governance: guardrails for agents at scale

Sierra Blog ·

A source describing release governance features for AI agents built on the Sierra platform: formalized safety processes for large-scale deployments, automated pre-deployment validation via Agent Checks, and incremental deployment using split traffic releases. Lee 3 puntos de vista con sus evidencias y enlaces a las fuentes.

Sachi Shah
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De un vistazo

  • Release safety at scale requires formalized governance

    For large companies building agents on Sierra—with hundreds of people collaborating across hundreds of customer journeys and serving millions—informal checks or individual memory are insufficient for safe agent releases.

    Ver el momento de apoyo · Párrafo 1

  • Agent Checks surface high-impact configuration issues before deployment

    Agent Checks acts as a linter that proactively identifies costly, easy-to-miss issues—such as referenced but unavailable tools, conflicting instructions, role confusion between lookup and action tools, response failures in voice contexts, or insufficient authentication for sensitive data lookups—and prioritizes them by severity, with inline Ghostwriter fixes.

    Ver el momento de apoyo · Párrafo 5

  • Split traffic releases enable canary-style rollouts for agent changes

    Split traffic releases let organizations incrementally deploy agent changes to a subset of customer traffic before full rollout—enabling verification of model upgrades, authentication redesigns, or behavioral changes—and are already used in production by a large airline, a travel marketplace, and a fintech company.

    Ver el momento de apoyo · Párrafo 13

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Párrafo 1 (se abre en una pestaña nueva)
release governance for AI agents

Release safety at scale requires formalized governance

Extracto original

Some of the world’s largest companies build their agents on Sierra: hundreds of people working inside a single agent, across hundreds of journeys, serving millions of customers. A small change to the agent can instantly reshape how it behaves with every customer. At that scale, releasing an agent safely can’t rely on an informal check or on someone remembering to double-check a change.
Sachi Shah·
Párrafo 13 (se abre en una pestaña nueva)
incremental deployment

Split traffic releases enable canary-style rollouts for agent changes

Extracto original

Our new split traffic releases let organizations incrementally roll out a release to a portion of customer traffic before expanding it to everyone — the same canary strategy software teams have relied on for years. If you’ve upgraded your model, redesigned authentication, or made a sweeping behavioral change, you can verify the release behaves as expected before rolling it out more broadly. A large airline, travel marketplace, and fintech company are already using split traffic to control how rollouts reach their customers.
Sachi Shah·
Párrafo 5 (se abre en una pestaña nueva)
automated agent validation

Agent Checks surface high-impact configuration issues before deployment

Extracto original

It catches the issues that are easy to miss and expensive to ship: a tool your prompt references but never made available, conflicting instructions to the agent, a lookup tool doing an action tool’s job, a response that works on screen but falls apart on a call, or a sensitive-data lookup with insufficient authentication. Checks are prioritized by severity, helping teams distinguish issues likely to impact customers from lower-priority quality improvements, and most come with a suggested fix from Ghostwriter that can be applied in place.
Sachi Shah·

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