Octo

Infrastructure for redeploying AI agents.

Octo is the versioning, testing, and environment comparison layer teams need to redeploy agents across customers, workflows, and runtime conditions.

By use case

Where Octo starts.

Development

Continuous Agent CI/CD

Integrate transfer tests into your existing CI pipeline. Every pull request triggers an evaluation suite. Block merges on regressions. Ship with evidence.

Evaluation

Reproducible Evaluation

Run controlled experiments across model versions, prompt configurations, and tool sets. Export signed, reproducible reports for review and sharing.

Redeployment

Environment Deltas

Compare how the same agent behaves across customers, data contexts, tools, and runtime constraints before rolling changes forward.

Scale

Deployment Branches

Keep agent variants organized as teams adapt a core system to new use cases without losing history, baselines, or rollback paths.

The evaluation lifecycle

From one working agent to many deployed variants.

01

Baseline

Capture the behavioral fingerprint of your current production agent. This becomes the reference point for all future comparisons.

02

Experiment

Branch and modify. Test new prompts, models, tools, or guardrails in isolation. Every change is tracked and diffed against the baseline.

03

Validate

Run transfer tests across environments. Review environment deltas. Attach corrective data for any regressions. Get a signed pass/fail verdict.

04

Deploy

Ship with a rollback window. Monitor live behavior against pre-deployment metrics. Automatic rollback if degradation is detected.

05

Monitor

Continuous evaluation in production. Track drift, detect new failure modes, and trigger corrective data pipelines automatically.

Have a specific use case?

We are looking for teams already adapting agents across environments, customers, or workflows.