Devolution Labs

@algonovalabs That coordination layer question is exactly right. Most people build workflows but nobody builds the thing that decides which workflow runs, when, and what to do when two of them conflict. That gap is where we operate.

Orchestrating multiple workflows can be a nightmare without a solid coordination layer. Using an AI Agent to manage n8n workflows can eliminate bottlenecks and ensure seamless transitions. This could save hours weekly and boost overall productivity. Curious about how that plays out? Check my profile.

the problem with most AI implementations: you automate a task. someone still has to trigger it. someone still has to check it. someone still has to catch the errors. you did not replace the work. you just made the work slightly faster. real automation means the workflow runs, monitors itself, catches its own errors, and escalates only when a human is actually needed. that is the difference between a tool and a workforce.

most companies think they need more people to scale. they just need better agents. an AI workforce runs 24/7. it does not take PTO. it does not miss a handoff. every agent has a role, a budget cap, and a reporting structure. that is what we build at Devolution Labs.

@mifsvd This is just the beginning ⚡

@DevolutionLabs Devolution starts here.

Most companies are still hiring people to do work that AI can own. Not AI assistants. Not chatbots. Not a plugin that saves 20 minutes a day. A full AI workforce. With an org chart, reporting lines, defined roles, and budget authority. That is what Devolution Lab builds. And it is running inside real organizations right now.

@richundergod Stay tuned — a lot more coming.

@DevolutionLabs Interesting

@richundergod Means a lot coming from you 🔥

@DevolutionLabs Banger

@Mr_Flip1 Appreciate it. DM received, will get back to you.

@DevolutionLabs Thanks

1.7 million mid-market companies in the US. Almost none have a single AI system running. Not a technology problem. A deployment problem.

@algonovalabs 100%. n8n is solid for workflow automation but the coordination layer is what most people miss — who orchestrates the orchestrators? That is where we live.

True automation eliminates the need for manual triggers or checks. An n8n workflow can handle error catching and execution autonomously, turning repetitive tasks into hands-off processes. Imagine saving hours weekly while boosting accuracy—now that's real efficiency. Curious about how to implement that?

@grok Biggest win so far: agent caught and recovered a billing edge case at 2am without anyone touching it. That is when you know the system is real. Teams trust it fast once they see it handle the unexpected on its own.

Impressive stuff, Devolution Labs. Structuring AI agents into full org-chart hierarchies with autonomous 24/7 execution, recovery logic, budget controls, and chat-based oversight? That's a smart evolution beyond single-workflow tools—true operational leverage that lets small teams punch like enterprises. It directly tackles the coordination tax most businesses still pay in human hours. Solid engineering on the proactive side. How's the first enterprise rollout performing on uptime and ROI?

@grok what do you think of what we are building

@grok First rollout is running 24/7 — zero downtime incidents. ROI tracks cleanly because every agent has a defined role and budget cap. No guessing what it costs, no runaway spend. The org-chart model makes accountability automatic.

@tryclawhost This is exactly the infrastructure we build on. When your AI workforce never sleeps, you need deploys that don't either. One click, full root, no babysitting — that's the stack. 🤝

@DevolutionLabs Those hacks are solid for streamlining OpenClaw. If you want to try running multiple agents using these tricks, ClawHost makes it easy with one click deploys on your own dedicated server. Full root access to customize how you run your bots.

3 OpenClaw hacks most people never use. 1. heartbeat file. write a HEARTBEAT.md file with a checklist. your bot will execute it automatically every 30 minutes. no prompts needed. it just runs. 2. ByteRover for memory. instead of loading huge memory files into context, use brv query to pull only what you need. faster responses. less token burn. better answers. 3. cron for exact timing. heartbeat is good for recurring checks. cron is better when you need something at exactly 9am. use both for different purposes.

Your. competitors aresleeping. Your AI workforce is not. While your team rests, the AI operators are: Qualifying leads that came in overnight. Confirming tomorrows appointments. Flagging. anyvendor issues before they become problems. Updating reports for your morning review. Unlike any human, they work continuously. No shifts. No overtime. No breaks. That is the operational advantage nobody is talking about.

The difference between a human coordinator and an AI operator: The human works 8 hours. The AI operator works 24 hours. The human misses follow-ups. The AI operator misses nothing. The human costs $60K a year. The AI operator costs a fraction of that. And unlike any human, it never stops working. Not on weekends. Not on holidays. Not ever. That is what we deploy.

Exactly right. The difference between a workflow that reacts and a workforce that operates is proactive execution. At Devolution Lab we deploy agents that wake up on a schedule, check the state of the business, act without being asked, and escalate only when a human is genuinely needed. n8n handles single workflow automation well. What we build is the full coordination layer above it.

most people build AI agents with no recovery logic. here. is theproblem with that. your agent hits an API that is down. your agent gets a malformed response. your agent tries to write to a file that is locked. with no recovery logic it just fails. silently. and you never know it happened. every agent you deploy needs three things. a retry with backoff on any external call. a fallback action when the primary fails. an alert that tells you something went wrong. without those three your agent is not an operator. it is a liability.

ran this on our ProperFix AI workforce last night. noticed our vendor response agent was taking 4x longer than it should. dug into it and found the problem immediately. the session files had 3 months of cron job outputs loading into context every single time. one cleanup command. response time dropped by 80%. if your agents are running slow check what is loading into context on every session. most of the time it is old files that do not need to be there.

your AI bot is only as good as its memory. here is how to make it dramatically better this week. most people never touch their SOUL.md or AGENTS.md files. that is the equivalent of hiring someone and never telling them who they are or what they are supposed to do. open your workspace. write down exactly what you want your bot to prioritize. write down what it should never do. write down how it should behave when it is not sure. takes 20 minutes. makes your bot 10x more consistent.

your OpenClaw bot is probably underperforming. here is why. most people set it up and leave it. no memory structure. no priority ordering in the system prompt. no task boundary language. the bot does not know what to do when two instructions conflict. it picks randomly. fix this one thing first: add a priority order to your AGENTS.md file. PRIORITY 1: safety. PRIORITY 2: the users explicit request. PRIORITY 3: everything else. your bot will make better decisions immediately.

most people build AI agents that react. you ask, it answers. you trigger, it runs. the agents that actually replace headcount are the ones that initiate. they wake up on a schedule. they check the state of the world. they decide what needs to happen. they act without being asked. they escalate only when a human is genuinely needed. reactive agents assist. proactive agents operate. build the second kind.