Last quarter, our head of sales, Sarah, was drowning. Her calendar looked like a war zone, and every external meeting took three internal emails and two Slack messages just to pin down. The constant back-and-forth for “what time works for you?” wasn’t just annoying; it was a productivity black hole, sucking up hours from her executive assistant and, frankly, Sarah herself. We needed something to cut through that noise, something more than just a shared calendar link. We started looking at AI Cal.com assistants for executives, not just for Sarah, but for the entire leadership team.
The promise of an AI agent handling all this seemed like a godsend. Imagine: an agent that understands preferences, time zones, travel times, and even the relative importance of meetings, then just makes it happen. No more email ping-pong. No more missed slots. Just a perfectly optimized schedule. That’s the dream, right? The reality, as always, is a lot messier.
The Dream vs. The Reality: Building Custom Agents
We started, like many do, by trying to build something custom. Our initial thought was to use a framework like LangGraph. The idea was to chain together a few LLM calls: one to parse the meeting request, another to check Sarah’s calendar and preferences, a third to propose times, and a fourth to handle confirmations and re-scheduling. We spent a good two weeks on a proof-of-concept. It worked, mostly, for simple cases. But then came the edge cases. What if Sarah had a soft hold on a slot? What if the other party proposed a time that was technically open but required her to sprint across campus? What about recurring meetings that needed to shift slightly? The agent would silently fail, or worse, book something suboptimal that required manual correction anyway. Debugging these silent failures in a multi-step LLM chain felt like trying to find a specific grain of sand on a beach. LangSmith helped, sure, but the iteration cycle was slow, and the cost of all those token calls during development added up fast. We realized quickly that building a truly dependable, production-grade scheduling agent from scratch was a much bigger undertaking than we’d anticipated. It wasn’t just about chaining prompts; it was about dependable state management, error handling, and a deep understanding of human scheduling heuristics.
Commercial Platforms: What Works, What Breaks
So, we pivoted to commercial platforms. We looked at a few options, including Lindy and Bardeen. Lindy, for example, promises to be your “AI assistant that handles all your tasks.” For scheduling, it does a decent job of taking a natural language request and finding a slot. It integrates with Google Calendar and Outlook, which is a must. The initial setup was straightforward enough. We gave it access to Sarah’s calendar, set some basic preferences (e.g., “don’t book before 9 AM or after 5 PM,” “always leave a 15-minute buffer between meetings”). For simple, one-off external meetings, it performed well. It cut down the back-and-forth significantly. This was a concrete love: the sheer reduction in email volume for simple external bookings was noticeable. Sarah’s EA reported saving about an hour a day just on this task.
But then came the gripes. Lindy’s pricing starts at $49/month for its “Pro” plan, which is what you need for any serious executive use. That’s fair for what it delivers on basic scheduling. However, customization is limited. If Sarah had a specific, nuanced preference – say, “only book calls with investors on Tuesdays and Thursdays, and never back-to-back with internal strategy meetings” – Lindy struggled. It’s good at following explicit rules, but less so at inferring complex, context-dependent preferences (a common failing for many current AI tools, honestly). We also found that when a meeting needed to be rescheduled multiple times, the agent could sometimes get stuck in a loop, proposing the same unavailable times or just giving up without a clear error message. This meant human intervention was still required for anything beyond the most straightforward scenarios. The promise of full autonomy was still a distant dream.
Another area we explored was integrating an AI meeting tool for post-meeting tasks. Scheduling is one thing; making those meetings productive is another. We tried a few options for meeting note taker review and transcription. Fathom.video, for instance, records, transcribes, and summarizes meetings, and it can even identify action items. This was a huge win for our team. Instead of someone frantically typing notes, we had a dependable transcript and a decent summary generated automatically. The ability to quickly search past conversations for decisions or commitments is invaluable. For an executive, having a concise summary of a 60-minute call, highlighting key decisions and next steps, is a massive time-saver. It’s not just about scheduling; it’s about making the entire meeting lifecycle more efficient. I actually use Fathom myself for internal team syncs; the free tier is enough for solo work, but the team plans are well-priced for the value they deliver, starting around $24/user/month for unlimited meetings.