June 8, 2026 · 4 min read
Agent Teams now work across every TaskForceAI surface
TaskForceAI Agent Teams are now available across web, desktop, mobile, and terminal workflows with consistent orchestration and progress streaming.
Shared agent-team behavior across web, desktop, mobile, and TUI
Mode and generation parity for research, images, video, and artifacts
Custom orchestration config: per-role models, team size, and budget
A cleaner handoff between local execution and hosted task history
One workflow, many surfaces
TaskForceAI is built around a simple product promise: ask once, let specialized agents coordinate the work, and keep the trail visible. This week we tightened that promise across the surfaces people actually use day to day.
Agent Teams now feel consistent whether you start from the browser, a native desktop session, a mobile device, or the terminal. The goal is not feature parity for its own sake. The goal is confidence that the same task structure, progress model, and final output survive the surface switch.
Generation parity matters
The biggest lift was making generated outputs behave like first-class results everywhere. Research runs, image generation, video generation, and file-producing workflows now share clearer execution semantics across the product.
That consistency makes advanced workflows easier to trust. A team can start with a quick terminal run, continue on desktop, and review from the web without losing the mental model of what the agents did.
- Shared mode handling for agent-driven tasks
- Consistent progress updates while work is running
- Generated results surfaced as durable outputs instead of hidden implementation details
Tune the team to the task
Not every task wants the same team. This week we exposed custom orchestration configuration so you can shape how an agent team runs before it starts.
You can assign specific models to specific roles, set how many agents participate, and put a budget on the run. Your configuration persists, so a workflow you tuned once behaves the same way the next time you reach for it.
- Per-role model selection so each role uses the right model
- Adjustable team size, from a single agent up to twenty
- Optional budget to cap how much a run can spend
What comes next
We will keep tightening the handoff between local and hosted execution. The more surfaces TaskForceAI supports, the more important it becomes that the platform feels like one system instead of a collection of clients.