Case study

Tandem: Governed AI Execution

A workflow runtime that turns human intent into governed automation that can plan, execute, validate, repair, and hand off real work.

Problem

Teams exploring agentic AI keep running into tools that only focus on chat or unreliable prompt chains.
Coding agents lack clear connections to broader operational and cross-team workflows.
Unbounded "autonomous agents" are difficult to inspect, nearly impossible to debug, and untrustworthy for real business impact.
Execution needs approvals, validations, checks, and repair-aware states to guarantee reliable output without endless human babysitting.

Built Tandem as a governed workflow runtime that handles the complete mission: plan, execute, validate, repair, and handoff.
Shifted the system focus from a "desktop app" to an execution engine, where chat is just an interface for a wider task-driven backend.
Enabled issue-driven workflow generation so that complex tasks (like Jira/Linear tickets) can be mapped directly into targeted AI automation.
Incorporated validations and repair-aware states to automatically solve breakages mid-flight, rather than pausing abruptly.
Made reusable workflow bundles (packs and presets) a core primitive, allowing proven setups to be executed across the org.
Protected the work with explicit approvals and checkpoints to ensure human alignment before executing destructive state changes.

Engineering teams who need structured execution to convert issues/tickets into validated pull requests without pure chat manipulation.
Internal platform and automation teams that want to codify useful organizational processes without writing brittle agent wrappers.
Enterprises seeking auditable and repeatable automation that provides a clear trace of decisions and repair history across longer-running tasks.