Unlike traditional software, AI agents operate in real time, and interact directly with users and systems. This makes version control critical: even small changes can impact customer experience. Operator’s versioning system ensures safe, structured iteration, enabling confident testing, controlled rollouts, and easy rollbacks when needed.

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Versioning model

Every saved change to an agent’s configuration generates a new version. Versions are immutable snapshots that capture the full state of the agent at that point in time.

Changes that trigger a new version include:

  • Prompt edits: Updates to instructions, templates, or conversation flow
  • Tool changes: Adding, or removing access to tools
  • Settings updates: Modifications to voice, knowledge of channel configurations

Each time you click Save, a new version is created with an incremental identifier (v1, v2, v3, etc.). These versions are immutable and fully traceable.

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IDs

Identifiers follow a structured format:

  • Agent ID: ca_xxxx (points to the currently released version)
  • Pinned version: ca_xxxx@v3 (refers to a specific immutable version)

When you reference an agent without specifying a version, Operator defaults to the released version. This guarantees that production always runs on the intended config, while allowing developers to test against specific versions as needed.

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Version binding

When a conversation starts, the platform locks in a specific agent version for the entire session:

  • Default behavior: New conversations automatically use the currently released version
  • Override behavior: When creating a conversation via API, you can specify a pinned version ID (e.g. ca_xxxx@v3) to bind the session to a specific version.

This ensures version consistency from start to finish, even if newer versions are released during the conversation.

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Development workflow

1

Edit

Make prompts, tool, or configuration changes in the dashboard

2

Test

Validate the unreleased version using dashboard previews, API calls or evals

3

Release

Deploy the version to production when you’re ready

We designed the versioning system to accomodate both simple use-cases as well as complex deployments that require more vetting before pushing the changes to production.

If you are just starting out, we recommend iterating quickly, making many small changes and hitting Release often.

As you grow and your deployment scales, we recommend building up a robust evals set and metrics to ensure the quality of your agents as the behavior instructions evolve.

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Testing

Use any of the following methods to test versions before release:

  • Dashboard: Make test calls directly from the agent editor
  • API: Target specific versions in your test API requests
  • Evals: Run automated evaluations on a version for quality and regression tracking

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Rollbacks

If a released version causes issues, you can quickly rollback by releasing a previous version:

  1. Navigate to the version history
  2. Select a stable previous version
  3. Click Release to restore previous functionality

You can also roll back via the API by specifying the desired version.

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Advanced features

The following features are currently in development and will be available soon.

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Partial rollouts

  • Traffic splitting: Route percentages of conversations to different versions
  • Gradual rollouts: Ramp up gradually to a new version while monitoring performance

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Version comparison

  • Side-by-side metrics: Compare success rates, latency, and satisfaction
  • A/B testing tools: Evaluate different agent versions
  • Regression alerts: Flag underperforming versions automatically