Resources
AI Infra Changelogs
Implementation updates for teams building governed AI capabilities into real products and workflows.
Workflow evaluations gained clearer operating evidence
AI Infra evaluation paths now keep the intended tool configuration in view and retain a more complete timeline for review.
Read the update →Product AI results and sessions became easier to integrate
Structured results and durable sessions make it easier to connect a governed AI capability to an existing product experience.
Read the update →Workflow evaluations and integrations became easier to review
Saved evaluations and integrations now have clearer execution, result, and credential boundaries for operational review.
Read the update →API-backed AI workflows became safer to run and recover
AI Infra strengthened the path from approved product APIs to governed AI workflows with clearer contracts and durable outcomes.
Read the update →Product AI execution became easier to observe and recover
Execution visibility, conversation handling, session timing, and tool recovery received clearer operational treatment.
Read the update →Scheduled AI workflows became easier to run and operate
AI Infra improved recurring workflow execution, session-to-use-case filtering, and integration reliability.
Read the update →AI workflows can move from chat-triggered to scheduled
Recurring workflow requirements, runtime boundaries, and validation paths are now clearer for time-based use cases.
Read the update →AI workflows became easier to inspect and prove
Graph views, saved tool-call evidence, and product settings make it easier to review a workflow after it runs.
Read the update →Embedded copilots can extend into custom workflow interfaces
The path from an embedded assistant to a customer-owned workflow interface now has clearer integration and proxy patterns.
Read the update →AI workflows became easier to test before teams trust them
Evaluation, review of larger test sets, and tool instructions now have a clearer operating model.
Read the update →Teams can map and route AI workflows more clearly
Use-case structure, workflow coverage, and routing boundaries were refined to help teams identify the right work before building it.
Read the update →Embedded AI capabilities gained safer tool execution boundaries
The integration path for embedded AI capabilities, declared use cases, and approved tools was strengthened.
Read the update →Integrations gained clearer boundaries and standards alignment
Integration behaviour is easier to understand in the product interface and more reliable in the operating path.
Read the update →Internal AI experiments gained a path to governed product work
Adoption paths, customer-owned execution, tool boundaries, and tracing were clarified for teams moving beyond an internal champion.
Read the update →Use-case authoring became more governed and reusable
A clearer authoring model makes each AI use case more source-backed, bounded, reusable, and testable before runtime.
Read the update →Product AI readiness became more measurable
Readiness signals and operational analytics replaced generic dashboard indicators with more useful product evidence.
Read the update →Product demos and integration authority became clearer
The product demonstration path and product-scoped integration authority are easier for product owners and engineers to understand.
Read the update →Product AI became easier to test and integrate
Persistent evaluation cases and a public request path created two early foundations for governed workflow delivery.
Read the update →