Tale

Handing work to AI agents is easy; keeping track of what they did and whether it is good enough is harder. Tale focuses on that second part. It wraps agent work in the familiar structure of projects and tasks, then adds review, approval and audit trails so a team can see exactly what an agent produced before it is accepted.

• Runtime choice lets each task go to Claude, Codex, Gemini, Cursor or OpenCode, billed through your own accounts with no markup from Tale.
• Persistent sandboxes keep an agent's working files between sessions and make them easy to inspect.
• Governance tools cover roles, single sign-on, spending budgets and guardrails for larger teams.
• Automations package repeatable agent workflows, while Arena compares two models on the same question.
• The open MIT codebase means the Community edition includes every feature when you host it yourself.

Tale suits engineering, operations and research teams that already use several AI agents and want a shared, auditable place to coordinate them. A practical tip: begin by delegating small, well-scoped tasks with clear acceptance criteria, then use the review step to build a library of good examples before moving larger projects onto agents.