Mindra
Agent Teams You Can Actually Delegate To
900 followers
Agent Teams You Can Actually Delegate To
900 followers
Mindra is the command center for your non-sleeping, 24/7 awake agentic team. Explain your task, and Mindra will create the best agentic team for you. Automate your marketing, supply chain and more. With Mindra's built-in governance, human oversight, and support for your existing stack, you can finally trust your agents.
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.
Mindra
@jeanette__walls Thank you Jeanette! Mindra displays every decision, all messages between agents, and every tool call with their parameters to prevent them from feeling like black boxes.
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?
Mindra
@paul_seen Great question. This is one of the hardest parts of making multi-agent systems useful beyond a demo.
We try not to let agents “free-form debate” forever. Mindra is more task-orchestration than group chat. The orchestrator owns the goal, decomposes the work, assigns bounded tasks to specialist agents, and then checks their outputs against the original user intent, available tools, execution state, and failures.
A few design choices matter:
Agents get scoped responsibilities, not vague autonomy.
Tool access is explicit, so an agent can’t drift into capabilities it shouldn’t have.
The orchestrator tracks execution state, tool results, auth failures, retries, and incomplete runs instead of relying only on the agents’ prose.
Follow-up delegation happens only when there’s a concrete missing piece or failed subtask, not because agents keep riffing.
The final answer is synthesized by the orchestrator, not voted into existence by the group.
So disagreement is useful only when it exposes missing evidence or a failed assumption. Otherwise the system converges through task state and tool results, not social consensus between agents.
@ilker_yoru Really appreciate the depth here. The "orchestrator owns the goal vs. agents voting consensus" distinction is the part most multi-agent demos quietly skip. Saving this thread.
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.
Mindra
@sounak_bhattacharya You can bring your agents from customize tab on mindra platform. You can plug them as external agents. It takes a few minutes.
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?
Mindra
@mykyta_semenov_ Of course our system can do it. It has LinkedIn and apify (allows to scrape linkedin posts), and even LinkedIn Ads integration. The limitations are on the integration and data level. There is not a limit of creating a specialized agent but only the limit of actions depending on the integration
Looks nice, if we can integrate with legacy systems it can solve a lot of problems in traditional business lines like Insurance
Mindra
@burak_gunduz01 Hi Burak, thanks for the comment. Many other tools fail at this specific moment, but of course we are obsessed with the connectivity aspect of things. As long as a system has an api that you can talk to, the orchestrator of Mindra will be able to use it creatively in your automations!
The interesting thing here is to treat orchestration reliability as a standalone issue instead of assuming the workflows will always run smoothly. I am curious how the coordination of agents is dealt with when they disagree on the next action.
Mindra
@aditya_bagde1 Hey Aditya, the orchestrator manages it and does not take an action if all good.
but how did u adapt to these agents to rapidly changing world what are the scenarios your system works best and worse
Mindra
@yusuf_sertkaya We completely change static workflow logic of n8n or Zapier. Mindra decides which agent to choose and use in runtime. This way even if an agent fails in a changed environment, Mindra can come up with creative combinations of its tools!
@denizsoylular ye I understand that but there are very know situations where agents do good job and very niche situations that cant be really automated how mindra would respond
Mindra
@yusuf_sertkaya The great thing is, you can also bring working agents to Mindra. So if you already have a pretty successful agent, you can simply use it in Mindra.
But coming to your question, if you need to train a model to perform well on a case, Mindra might have a difficult time as we don't have that feature yet.
What would happen if it encounters a situation that it is unable to do? It would try its best and come up with external integrations that it can use. Hope this answers it :))