Web
Chat with people and agents, open shared projects, and run the same hosted workflows from any browser.
Build and ship agents, evaluate models, and prepare training runs from real conversations. Share the same agents, projects, sandboxes, and usage with your team across every surface.

People and agents share one workspace without sharing Personal accounts. Team Chat, agents, projects, sandboxes, Tasksets, and cloud usage can all belong to the Team.
People and agents share channels, direct messages, project context, and the same execution history.
Agents, projects, sandboxes, and Tasksets belong to the Team instead of one developer's Personal account.
One Team funds cloud execution while limits, runs, and audit remain attributable to each member.

Web and Desktop are two views of the same runtime. The CLI, TUI, connected channels, and hosted agents use the same source-backed packages and execution model.
Chat with people and agents, open shared projects, and run the same hosted workflows from any browser.
Keep local files, source review, model settings, approvals, and terminal state close to the conversation.
Use the same runtime from scripts, terminals, CI, or an interactive terminal chat without inventing another agent contract.
Publish the same source-backed agents to Team Chat, Slack, Microsoft Teams, or another machine.

Use the Agent SDK when a workflow needs code, actions, setup requirements, or evals. Packages live in a git-backed profile and remain discoverable from Web, Desktop, CLI, TUI, and cloud execution.

Keep orchestration and review close to the developer while giving execution a clean environment built for long-running work and teammate handoff.
Keep chat, model settings, approvals, subagent placement, local files, and source review on your machine.
No OpenPond account is required for local work.
Hosted sandboxes handle commands, dependencies, browser work, scheduled jobs, snapshots, and long-running processes.
The agent package and review workflow stay the same.
Select the work worth repeating, turn it into an explicit Taskset, and inspect every verifier and grade before preparing a portable training bundle.
Choose conversations that demonstrate a repeatable job.
Turn selected evidence into explicit Taskset inputs and outcomes.
Add deterministic checks or calibrated model graders.
Run attempts and inspect the grading evidence.
Build a portable bundle for a configured destination.

The app, runtime packages, clients, Agent SDK, and training workbench are public. Run locally, inspect the contracts, and keep the agents and Tasksets you create as ordinary source.