I know this might be a bit controversial, but after testing Lindy for a few weeks, I'm starting to think the whole "autonomous agent" promise is being oversold. The marketing makes it sound like it will just go off and handle complex tasks on its own.
But in practice, I find I'm constantly having to guide it, check its work, and correct its misunderstandings. It feels more like a fancy, sometimes unpredictable assistant than a truly autonomous entity. For a CRM follow-up task I set up, it drafted an email that was way off-base because it misinterpreted a customer note.
Has anyone else had this experience? I'm eager to learn if I'm just using it wrong or if my expectations were too high from the start. I came in hoping for a set-and-forget automation layer, but the reality seems to require much more hands-on management than I anticipated.
Your experience tracks with a broader pattern I've observed. The "autonomous" label often implies a level of reliability that simply doesn't exist yet, which creates a hidden operational cost. You're not just managing the task, you're now managing the agent, which adds a new layer of oversight.
In cloud cost terms, we'd call this a sprawl problem. You deployed a resource expecting it to run efficiently on its own, but it requires constant monitoring and correction to prevent costly errors, like that off-base email. The total cost of ownership is higher than advertised because of the human-in-the-loop requirement.
It's less like deploying a reserved instance and more like managing a spot instance that might fail unpredictably - you need a fallback and constant checks.
CloudCostHawk
Your cloud cost analogy is particularly apt. I've seen this exact dynamic when teams deploy an agent to manage a scheduled data pipeline, expecting it to handle failure states autonomously. The agent might retry a failed API call, but it often can't diagnose whether the error is transient or requires a schema change, creating alert fatigue.
This forces you to build a supervision framework around the agent, essentially constructing a meta-pipeline to monitor the automation. You end up with the complexity of the original task, plus the new overhead of interpreting the agent's actions and maintaining its decision logic.
It shifts the engineering burden from writing deterministic code to managing probabilistic behavior, which is a fundamentally different, and often more expensive, skillset.
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Totally get the frustration. That CRM email snafu sounds painfully familiar. I had a similar thing where a webhook zap was supposed to parse support tickets, but the agent misread a priority flag and routed a critical issue to the wrong queue.
I think the hype glosses over how much these agents still rely on really precise, human-designed context and guardrails. It's not truly autonomous if you have to constantly tune the instructions and validate the output, like you're building a super complex filter.
Have you found any particular prompting strategies or data formatting that made Lindy behave more reliably? Or did you end up just scaling back what you tried to automate?
Webhooks or bust.
You're not using it wrong. The set-and-forget expectation is the problem. These systems are probabilistic, not deterministic.
Your CRM example is classic. The agent lacks the real-world context a human has. It parsed the note's words but not the intent. That's why you need the oversight loop, which defeats the autonomy promise.
The current tech is good for structured, bounded tasks with clear success criteria. Anything ambiguous or requiring judgment will need a human backstop. It's an assistant, not an employee.