Agent Teams - Building a Local-First Desktop App for Multi-Agent Coordination
I was in the middle of a vibe-coding session, building something completely different, as I do very often these days, and the AI kept asking follow-up questions. One question led to the next, then to the next, round after round of clarification before we even got to the actual answer. Each single question made sense on its own, but taken together, a pattern showed up where I spent more time…
Agent Teams emerged from frustration with the inefficiency of AI interactions. The AI would repeatedly ask follow-up questions, making the user feel like they were being interrogated rather than collaborating. This led to the realization that if the AI could understand the problem well enough to ask for clarification, the user should not have to repeatedly explain the context to the AI.
The concept of letting multiple agents communicate with each other became the next logical step. By enabling agents to talk to one another, the user could avoid having to rebuild context for each agent, saving time and resources. To prevent each agent from having to explain the context to every other agent repeatedly, shared memory was introduced.
This memory space allowed agents within the same group to read and write information, eliminating the need for constant explanations. Quality Cascading was another crucial aspect of Agent Teams' design. It recognized that not every step in a task requires the most sophisticated model. Some steps could be handled by a faster, less expensive model, while others required more depth due to the potential consequences of the decision.
By implementing Quality Cascading as a adjustable setting rather than a fixed choice, the user could optimize the balance between speed and depth according to the task's requirements. The decision to adopt a local-first approach was not a matter of weighing tradeoffs between local and cloud storage. Privacy was an absolute necessity for the user, and local-first became the natural outcome of this principle.
Storing project files, conversation history, and provider credentials locally ensured that sensitive data remained protected. Security was also prioritized from the outset. The architecture incorporated various security measures, such as safeStorage for provider secrets, an isolated renderer, an MCP trust model, and strict origin checks on the optional REST API.
These features aimed to protect user data and prevent unauthorized access. The most significant challenge in developing Agent Teams was Quality Cascading. The real difficulty lay in determining when an agent's answer was insufficient and warranted a second, more expensive pass. The solution to this problem was found in shared memory, which allowed agents to build upon each other's work without redundancy.
This feature not only made Quality Cascading more effective but also helped manage token usage and cost more efficiently. Currently, Agent Teams is available in public beta for Windows, licensed under the MIT license. The developer is committed to continuously improving the tool based on real-world usage and feedback. The code and threat model are publicly available, providing transparency and encouraging collaboration.
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