Evolving our calendar assistant Reclaim to be AI-native without starting over

Calendars hold more than just blocks of time. They reflect how people plan their days, coordinate with others, and make room for what matters to them. Reclaim, the Dropbox-owned calendar assistant, helps manage that complexity by using AI to find and adjust time for tasks, habits, and meetings. That approach works well when inputs and expected outcomes are clear. But people also use Reclaim for scheduling that’s less straightforward.

As AI assistants and natural-language interfaces became more capable, they opened up a new way for people to use Reclaim. We imagined situations where users could describe a scheduling goal in their own words, even when it didn’t map neatly to an existing setting or command. Words alone wouldn’t be enough to determine what should happen on a real calendar, though. Reclaim still needs the context that gives requests meaning, including what’s already scheduled, when a user is available, and which preferences and commitments impact changes.

Connecting natural-language requests with the surrounding calendar context could help people spend less time figuring out which settings or actions to use. But building the experience required more than adding an AI interface to the existing product. We needed to rethink how Reclaim turned conversational requests into calendar changes while preserving the scheduling system already in place.

For our engineers, the rebuild started with the way Reclaim already managed calendars. Calendar changes carry real weight because time is precious and scheduled events often involve other people. Any new AI capability needed to feel like a natural extension of Reclaim, and it needed to work with the scheduling logic already in place.

Before the redesign, Reclaim handled calendar changes in two primary ways. Users could make specific changes themselves, like moving an event or updating a setting. And Reclaim’s automated scheduler also worked in the background. It considered each person’s preferences and availability when finding time for tasks, focus work, and recurring routines like lunch, then adjusted those calendar blocks as schedules changed.

The emergence of AI agents introduced a potential third way for users to use Reclaim. Instead of choosing a specific setting or command, a user might prefer to describe what they want to do in their own words. An agent could then interpret that request, look at the relevant parts of their calendar, and work out how Reclaim could help. 

But we needed to address several challenges before introducing agents to the experience. Large language models can interpret the same request in different ways because they generate answers based on patterns in their training data. For example, an open-ended request like “make time for this tomorrow” could lead an agent to find an open block, create a new event, or move something already on the calendar. Each option would affect the user’s schedule differently, and some could affect other people’s calendars, too.

That variability made AI agents different from the two paths Reclaim already supported. Although they arrive at decisions differently, all three actors needed to rely on the same scheduling logic. Creating a separate AI-only path would have duplicated functionality and made Reclaim’s behavior harder to keep consistent across interfaces. We needed a system that could connect a model to both the right calendar information and Reclaim capabilities while also controlling how it used them. The result was an agent platform designed to work within Reclaim’s existing architecture.

Our agent platform gives a model relevant calendar details and access to Reclaim’s features while controlling what the model can see and do. The model interprets what the user says and figures out what they want. The agent, meanwhile, manages the larger process by providing Reclaim’s instructions, relevant calendar context, and tools (which let it ask Reclaim to look up information or request a specific action). When a tool returns information, the agent can send it back to the model to inform the next step. This repeated exchange is called the agent loop.

As part of this process, Reclaim gives the agent the calendar information and tools relevant to each request, rather than access to everything at once. This helps the agent focus on the immediate task and limits it to the appropriate actions. For a more complex request, the agent can give one specific part to a specialized subagent. Internal checklists and review steps help keep the overall request on track.

Ultimately, we opted to build the agent loop, tools, and the system for gathering context ourselves, and we connect directly to model providers instead of relying on a broad agent framework. That’s because a framework we initially explored lagged behind the provider APIs we wanted to use and included more structure than Reclaim needed. Owning these parts lets us design them around Reclaim’s scheduling needs, adopt new provider capabilities more quickly, and work with different model providers without rebuilding the rest of the platform. That leaves us with more code to maintain, though using agents to help with that code offsets some of the additional work.

The same tool system supports Model Context Protocol (MCP) in two ways. Inside Reclaim’s agent loop, an MCP client lets the agent use compatible tools from other services. Separately, Reclaim’s MCP server makes selected Reclaim tools available through supported AI clients, including Claude and ChatGPT. Those clients use their own models and agent loops, but can call the same tools used within Reclaim.

Our agent platform helped Reclaim work through open-ended requests, but we still needed safeguards around any changes it proposed. Users needed a way to review those changes before they reached their actual calendars.

Behind the scenes, Reclaim represents each scheduling capability as a Schedule Action, our internal term for a standard operation like creating or updating an event, changing an RSVP, or finding availability. Whether a request comes from a user, the automated scheduler, or an agent, the same Schedule Action type is used, including the same validation and commit process.

That shared path matters because a calendar change can affect more than one event. Moving a meeting might change someone’s availability or cause Reclaim to adjust flexible events elsewhere on a user’s calendar. If each actor used a separate implementation, engineers would have to reproduce those rules and keep every version aligned as Reclaim evolved, which would have become tedious over time. Because all three actors use the same Schedule Action, engineers can change how an operation works in one place rather than updating three separate versions. This helps Reclaim behave consistently across its interfaces.

But even when Reclaim handles a change consistently, users still need to see whether its wider effects match what they want to do. That’s why we developed Preview Mode, which gives users a temporary version of their calendar where they can review AI suggestions and changes requested through chat before applying them to their actual calendar. Essentially, they’re able to see how an event or settings change would affect the rest of their schedule before it goes live. They can then confirm that the change behaves as expected and review any effects on shared events before calendar updates are sent to other attendees.

To keep Preview Mode fast, we reworked the automated scheduler as a pure function, meaning, in part, that it can calculate a proposed schedule without saving anything to the user’s actual calendar. Reclaim can repeat that calculation whenever the user adjusts the preview. Because busy calendars and meetings with several attendees require a lot of data, Reclaim keeps a readily accessible copy in Redis, a data store designed for fast retrieval. It also updates attendee availability within the preview whenever an event moves, so the next calculation reflects the schedule being proposed.

Reclaim now has a consistent path from a natural-language request to a reviewed calendar change. The agent platform connects models with relevant calendar information and Reclaim features, Schedule Actions route proposed updates through the product’s scheduling logic, and Preview Mode makes their effects visible before anything reaches the calendar or other attendees. Together, these pieces extend Reclaim with AI while preserving the scheduling experience people already rely on.

This work also reinforced what AI needs in order to be useful inside an active workflow. A model needs the surrounding information to understand a request, a controlled way to act on it, and a review step before the result moves forward. In Reclaim, a scheduling goal can move from conversation to a proposed schedule and then to an approved calendar update without requiring the user to translate it into a specific setting or command.

The long-term value of this rebuild is the freedom to evolve without fragmenting the product. As AI changes how people ask for help, Reclaim can introduce new capabilities through the same scheduling foundation rather than adding a separate experience each time. For Reclaim, this offers a practical framework for expanding AI-assisted workflows as models improve and people find new ways to manage their time.

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