Mindra

Agent Teams You Can Actually Delegate To

Productivity

Marketing

Artificial Intelligence

Visit WebsiteSee on Product HuntTwitter ⧉

The cost unpredictability is why a lot of teams still hesitate on autonomous agents, even when the ROI math works. Are you guys implementing hard token limits or retry budgets that the agents respect, or is there more of a safety net approach where you catch runaway behavior and adjust?

The governance and human oversight angle is what stands out here. I scaled an engineering org from 15 to 120 people, and the hardest part of delegation was never the task itself - it was knowing when to trust the output and when to step in. That problem is 10x harder with AI agents. The fact that you're building permissions and collaboration between agents rather than just chaining them feels like the right foundation. Curious how teams are handling the handoff when an agent hits a wall - does it escalate cleanly or do you end up debugging the whole chain?

How can I understand your limitations? For example, I need an agent that will go to LinkedIn, scan posts there, and analyze them for mentions of my services. Will your system be able to create such an agent or not?

no product, just a landing page where you need to book a demo. so its not really released and accessible.

Full message visibility between agents is underrated. Half the reason people don't trust these systems is they can't tell why something happened, not just what happened. That alone would make me comfortable handing off a real workflow.

The insight behind Mindra was simple: the people who most need AI are exactly the people who can't deploy it. Non-technical teams shouldn't need an engineering backlog to automate their work. We built the product that finally closes that gap.

For someone currently doing this with LangGraph or CrewAI — what's the migration story? Is there an import path, or is it 'start clean'? Asking because the switching cost is usually what kills adoption, not the feature gap.

The 'agentic team you can actually delegate to' framing is what nails it for me — I run finance content (financial modeling YouTube at https://www.youtube.com/@Mod3Loop) on top of a day job in M&A, and the bottleneck has never been ideas, it's the production layer (briefs, thumbnails, repurposing, distribution). Curious how Mindra handles agents that need long-running domain context (e.g. a recurring "audit my LBO model walkthrough script for IRR/DSCR accuracy before publishing") vs. one-off tasks. Where does the human governance step usually slot in?

How do you handle agent disagreement / drift over time ? Most multi-agent systems I've tried collapse into either chaos or echo-chamber consensus after a few iterations. What's the orchestration model under the hood?

Who doesn't love non-sleeping agents!? Congrats @zeynep_yorulmaz and the team! I assume there is an agentic team behind the launch as well :)))

Really like the shift from fragile chains to adaptive agent teams, feels closer to how humans actually collaborate. Curious, how do you see the balance between autonomy and human oversight evolving as workflows get more complex? Congratulations!

the agent-team angle is interesting — most agent products today still ship as a single agent that pretends to be a team. real delegation needs a coordinator that owns task decomposition + retry logic, not just parallel chat windows. curious what you're using under the hood for the orchestration layer? we hit a similar problem on a different category of agent work and ended up writing the orchestrator as a state machine because LLM-driven orchestration was too non-deterministic for production. great to see this shipped today, congrats.

Don't forget to book a personal demo:
https://mindra.co/

Or join our online launch party:
https://luma.com/dmph2nle

but how did u adapt to these agents to rapidly changing world what are the scenarios your system works best and worse

Looks nice, if we can integrate with legacy systems it can solve a lot of problems in traditional business lines like Insurance

Strong pitch agents teams you can trust and built-in governance is exactly where most agent tools currently fail. Real test will be whether users can predict and audit what agents are doing without feeling like it’s a black box.

The word actually is doing a log of heavy lifting here, and honestly it needs to. What makes Mindra’s delegation stick when most agent tools fall apart on complex multi-step task?

For the always-on 24/7 piece, how do you keep costs predictable? An agent team that re-plans and retries sounds great until I'm staring at an LLM bill that 10x'd because something kept failing overnight.