Access foundational models from one workspace

A product team wanted to speed up research, writing and decision support without standardizing on a single model provider. In practice, different parts of the organization had different preferences: some workflows benefited from stronger long-form writing, others from fast iteration, and others from cost-sensitive summarization. The team needed a single workflow that could route requests to the most appropriate provider while keeping ownership, context and a reliable audit trail in one place.

The team operated across product, engineering and operations, with shared responsibilities for customer-facing documentation and internal enablement. They were already using multiple AI providers, but the switching cost was adding up: repeated prompt setup, inconsistent storage of outputs and limited visibility into what had been generated and why.

The challenge

• Different workflows required different strengths. Writers cared about consistency and tone, analysts needed structured summaries and extraction, while engineers prioritized speed and predictable formatting.
• Switching between AI provider interfaces caused context loss and duplicated work. Prompts, constraints, and reference material were repeatedly rebuilt from scratch across tools.
• Outputs were difficult to review later. In many cases, the team could not easily answer which model produced a result, which input was used or who approved the final version.
• Cost tracking was fragmented. Usage was visible per AI provider, but hard to attribute to projects, teams or task categories without additional manual reporting.

How Mini Agent helped

• Requests were created as tasks and routed to a chosen AI provider per task type (for example: long-form drafts, structured extraction or quick iteration), without requiring users to move between interfaces.
• Each task preserved the working context: the original request, relevant constraints, attachments and the resulting output remained linked in the same timeline for later review.
• Ownership and review stayed explicit. Team leads could assign tasks, request revisions and track completion without relying on side conversations or copy/paste handoffs.
• The team established lightweight internal guidelines (which task types go to which AI provider, when to request a second pass and what required human review) all without changing day-to-day collaboration habits.

Outcome

Over the first few weeks, the team reduced handoffs and rework caused by duplicated prompts and missing context. The more important change was operational: requests, inputs and outputs were easier to review later, plus it was clearer who owned the next step at any point in time. Mini Agent became the shared entry point for foundation model work, while still allowing the team to choose AI providers based on the needs of each task rather than a one-size-fits-all decision.