Last month, my dev team was drowning. Not in code, but in meetings. Stand-ups stretched to an hour, sprint reviews became philosophical debates, and decision-making calls spiraled into “let’s sync again next week.” We were hitting that wall where productivity wasn’t just stalling; it was reversing. I needed to figure out how AI reduces meeting time, not just in theory, but in practice, because the alternative was burnout or more hiring, and neither felt right. We’d tried all the usual tricks — strict agendas, timekeepers, “no-meeting Wednesdays” that lasted exactly one week. Nothing stuck. That’s when I decided to throw proper agent tooling at the problem.
What I Tried and What Broke
My first thought was transcription and summarization. Obvious, right? I started with some of the popular services. Lindy.ai meeting agents, for example, is pretty good at this. It’ll join your call, transcribe, and then give you a bulleted summary. It even pulls out action items, which is a concrete love of mine — having a machine actually identify what needs doing from a rambling conversation is magic. For a few calls, it felt like a win. People could skip parts of meetings they didn’t need to be in, then just read the digest. Cost-wise, Lindy’s basic plan is around $49/month, which I think is fair for what it delivers if you’re drowning in calls.
But here’s the gripe: the “action items” aren’t always what you’d expect. Sometimes it’d pull out a passive suggestion as a mandate, or completely miss a nuanced decision. We’d still have to manually review and refine them, which added a new kind of overhead. It didn’t truly reduce the meeting itself, just the post-meeting cleanup for some. Plus, when you’re talking about sensitive client data or internal strategy, letting a third-party AI join every call raises some serious compliance eyebrows. We had to be super careful about which meetings it could even attend, and that meant a lot of manual configuration and exceptions. It wasn’t the “set it and forget it” solution I’d hoped for.
Orchestrating Decisions with AI Agents
The real breakthrough came when I stopped thinking about just transcribing and started thinking about pre-meeting preparation and post-meeting follow-through. This is where agent frameworks like CrewAI and AutoGen started to look interesting, even if they’re a heavier lift. We built a simple pre-meeting agent using CrewAI that would consume our project management tickets, recent Slack discussions, and pull requests related to a specific topic, then generate a concise pre-read document with proposed discussion points and even some suggested solutions. This wasn’t about replacing the meeting; it was about making sure everyone came in informed and with context, cutting down the “what are we even talking about?” phase.
For this, we’re running it on our own infrastructure, so the direct cost isn’t a subscription, but the developer time and compute adds up. LangSmith became indispensable here for debugging these agents, by the way. You don’t realize how much you need observability until your agent silently fails to pull in a crucial document, and then your meeting goes off the rails because of missing context — and good luck finding docs for this without something like LangSmith. Its tracing capabilities are honestly the only way I’d actually pay for an agent debugging tool at scale. The free tier of LangSmith is enough for solo work, but once you’re running multiple agents in production, you’ll need the paid version, and it’s not cheap, but it’s essential.
The agent would summarize key points, identify potential roadblocks, and even suggest a few options for discussion, complete with pros and cons. This meant our stand-ups, for instance, became genuine decision-making sessions, not just status updates. We’d spend 15 minutes debating actual solutions, not 45 minutes getting everyone up to speed.