FlowTask 2.0 - Company brain for AI Agents
by•
Company Brain for AI agents, Fragmented communication and data of a company which is left on channels Emails, Slacks, WhatsApp and LinkedIn connect it all in one place with approval layer attach so personal and works chats don't gets mixed and connect it with any ai agents simultaneously, where records are keep getting updated minutes by minutes and so AI agents are getting updated as well so use AI agents with less cost of context and don't have to repeat.
Replies
FlowTask
A company brain for AI agents (the question is why)
So I posted one simple question on Reddit about a frustration I have while using AI.
got 8.5k views in 5hrs with 45+ comments
Almost nobody said the problem didn't exist, instead everyone shared their own workaround.
Some use Chatgpt Projects. Some maintain PROJECT.md or CLAUDE.md files.
Some build knowledge graphs. Some create automation pipelines with n8n or Zapier.
Some use RAG connectors and custom memory systems.
The pattern was surprisingly consistent.
The real problem is Claude doesn't know what's happening minute to minute people said use an API or connect it manually. But then you're still feeding it everything including the useless stuff so we added an approval layer you decide what goes to AI combined with MCP, no need for Make, Zapier, or n8n it just works.
And what's the output
AI agents 10x better, knows what's happening minutes by minutes + no daily updates
PicWish
@bibhash_dutta the approval layer for whatsapp and slack is smart to block personal chats but if a team sends 100s of messages a day. does someone actually have to sit there and manually approve every single one before the agent sees it?
both the Google Workspace and Slack connectors are broken which means there's no way to actually proceed into the app and try it out. bit disappointing and not a great experience for new potential customers
FlowTask
@adam_sardo That was on us, a bug on both connectors that's now fixed. Genuinely appreciate you taking the time to try FlowTask and flag it instead of just bouncing, that's exactly the kind of feedback that helps us catch things fast. Would mean a lot if you gave it another go, and if anything else feels off, tag me directly, I'll jump on it right away.
Congrats on the launch. The approval layer is the part I like most — most tools that pull from Slack and WhatsApp just take everything, so personal and work chats get mixed. Letting you decide what actually enters the brain is a smart call.
One thing I keep thinking about: when the memory updates every few minutes from so many channels, how do you handle facts that go stale or contradict each other? Like a decision made in Slack last week that gets reversed today — does the brain catch that on its own, or does someone have to approve the change?
Voquill
I like that you're tackling the context problem instead of trying to cram more data into the model. Congrats on the launch!
Very Happy to announce FlowTask on Today's Product Launch. After working for months on out SaaS we have finally launched FlowTask 2.0 which has much better Operations Management, now comes with a larger AI context for companies with large number of employees. Hope ya'll enjoy it!! Happy Coding.
"No more stale CLAUDE.md files" is the right problem to name — we run agents across eight repos and those files rot faster than anyone gets around to updating them.
The thing I'd worry about with an always-updated memory is a quieter kind of staleness: a fact that was true the day it was written and silently stopped being true when the code changed underneath it. Nothing in the original Slack thread ever says it expired. Does the approval layer carry any notion of a fact aging out, or is it mainly a gate on what gets in?
the staleness questions above are the obvious concern, but there's a related one nobody's asked yet: if Claude and ChatGPT are both reading from the same brain at the same moment via MCP, and the brain is updating minute by minute, could two agents working in parallel end up citing two different 'current' answers to the same question just because one queried a few minutes before an update landed and the other after? not a bug exactly, just a consistency question that matters more once you've got multiple agents acting on the same context instead of one person reading it.
@omri_ben_shoham1 good catch, and I'd push it one step further - it's not just two agents citing different facts, it's two agents acting on different facts. if Claude reads the brain before an update and goes and sends an email based on the old state while ChatGPT reads it a minute later and takes a different action based on the new state, you don't get a wrong answer sitting in a chat window, you get two real-world actions that contradict each other and nobody notices until someone has to clean it up. curious if there's any kind of version stamp on a read, so at least you could trace which snapshot an action was taken against after the fact
The approval layer on top of the ingestion pipeline is what makes this safe to use with real company data — without it, connecting WhatsApp and Slack is effectively giving every AI session access to every conversation that got pulled in. The thing I want to understand is the MCP server lifecycle: when an agent session connects to the FlowTask MCP, does the knowledge snapshot stay consistent for the duration of that session, or can it update mid-session as the brain ingests new messages? That matters for multi-step workflows where context drift mid-task would break the run.
This is something I say to every C-level I talk to: if a person's knowledge doesn't become the company's knowledge, the hire was meaningless. Most teams lose that expertise the moment someone's out sick or leaves. Turning scattered Slack, email, and WhatsApp threads into one living memory solves something I run into constantly. Well done.