We started Agentplace as a builder for AI websites.
Now we're building something bigger. Agentplace lets you create specialized AI agents for real tasks and workflows, like AI teammates that help you get work done.
Build, test, and deploy agents fast, and improve them as you go.
This is the 3rd launch from Agentplace. View more
Agentplace AI Agents
Launched this week
Start with ready agents for common workflows or create your own in minutes.
Agentplace lets you build specialized agents for tasks like lead routing, research, document analysis, scheduling, and internal support. Use them yourself, share them with your team, or connect them to the tools you already use.
Agentplace handles the infrastructure so you can focus on the workflow.
Free Options
Launch Team / Built With
Agentplace
We started as a builder for AI websites. Good product. But the more we talked to users, the more we realized they didn't want better websites, they wanted better work. So we went bigger.
Agentplace lets you create specialized AI agents for real tasks and workflows. Think AI teammates that actually help you get things done. Generative UI, voice, browser memory, agents that adapt to each user over time. A unified workspace where you can switch between agents and get real work done.
And we built around one core insight: the trick isn’t “build the perfect agent.” It’s to ship, use, fix, and repeat fast.
Work mode gets your agent running. Edit mode brings you back the moment something breaks or a better model drops. Republish in minutes.
We're genuinely excited to hear what works, what breaks, and what we should build next. Every comment here shapes the product.
Looking ahead, we’re doubling down on this idea of AI teammates.
We think the future isn’t just better agents, but a shared workspace where agents and people work together.
Agents will handle more work end-to-end, talk to each other, and run tasks autonomously. People stay in the loop, see what’s happening, and step in where judgment matters.
Over time, this becomes a new kind of work environment, where humans focus on decisions, and agents handle execution. That’s the direction we’re building toward.
What's the first agent you'd build?
FuseBase
@vlad_yanch I'm testing brief builder agent now, looks like it can save much time in client briefing, fits great in my selling process
Agentplace
@vlad_yanch @kate_ramakaieva Thanks so much for trying it out, really glad to hear it fits naturally into your sales process
@vlad_yanch Nice direction! I’d probably start with something simple, an agent that handles routine stuff like collecting info, summarizing it, and organizing tasks (basically saving me from constant context switching)
Agentplace
@vlad_yanch @julia_zakharova2 Exactly! that’s the sweet spot to start with. Even a simple agent for collecting info, summarizing it, and organizing tasks can save a lot of time by cutting down context switching.
Agentplace
@julia_zakharova2 it is the best way to start
@vlad_yanch What's one workflow where an agent has broken for you before, and how does Agentplace's edit/re-publish loop fix that in practice?
Agentplace
@swati_paliwal Usually the thing that breaks is the prompt, you write it, test it a few times, and then some real user asks something you didn't think of and the agent does something weird. Also, it's hard for users to build agents right away, it's usually iterative process where there's a lot of space for experiments, so versioning and edit/re-publish help with that.
Agentplace
@swati_paliwal Yeaah, most agents don’t break on the happy path, they break on real edge cases. That’s why being able to quickly tweak, test, and re-publish matters so much.
Agentplace
@swati_paliwal I learn every day how I can push the agent to do more; it is really a continuous process.
@vlad_yanch Hey, my first agent was processing calls after a note tracker and collecting tasks from them. If you agreed on something during a call or a meeting, it turns those into tasks.
@slavaakulov Great use case, thanks for sharing!
Scade.pro
I keep seeing agent products but most of them still feel kinda abstract.
This one feels more grounded tho
Is there still a learning curve for non technical people, or is setup actually lightweight?
Agentplace
@maria_anosova We spent a lot of time trying not to invent new stuff to learn. There are a few things you'll need to pick up but they're the same in any agent tool, like skills (what your agent can do) and MCP (what agent can do and how it connects to other services ). After that you're mostly just telling it what you want in plain words.
You build it, hit publish, pick who can see it, and it's live on its own URL, so I would say it's pretty lightweight
Agentplace
@maria_anosova That was a big goal for us, make setup feel more like describing what you want than learning a whole new system
Agentplace
@maria_anosova we will make it even easier soon! keep tuned
FuseBase
Congrats with a new launch @polina_semina @vlad_yanch
This is cool bc it feels closer to how adoption actually works inside companies. Does one person usually own an agent or can a few people manage it together? That part matters a lot for teams.
Agentplace
@polina_semina @vlad_yanch @kate_ramakaieva
No team management yet, one person owns each agent right now. We do have remix concept though, which helps with this. Someone builds an agent, publishes it, turns on "allow remix". They share a link, anyone who opens it gets their own copy of agent. They change whatever they want from there. It's not shared ownership but if someone on your team figures out a good workflow. You can also publish an agent with restricted access, so only specific people or your company's email domain can use it. That way you share the agent itself, not just the template.
Agentplace
@vlad_yanch @kate_ramakaieva Thank you so much!
Congrats on the launch! 🚀
how do you see your main audience at this stage? more developers building custom workflows, or non-technical users exploring agents for the first time?
@julia_zakharova2 Thanks! Honestly, both. Non-technical users can build fully functional agents just through chat, no code required. But developers will find a lot to love here too: full code access, custom integrations and MCP tools support. It just makes the whole process way faster. What used to take days now takes minutes.
@kaysinb Got it, makes sense. Who’s actually using it more so far, devs or non-technical users?
@julia_zakharova2 So far mostly non-technical users, but ones with some experience using agents in tools like Claude Code, so they already get the general concepts.
Agentplace
@julia_zakharova2 Thank you for your support Julia!
Jinna.ai
Congrats on the launch! Agents instruct agents… Does you tool work over a codebase to tailor an agent for it? Asking because generic agent instructions is something I believe Claude itself can generate, wondering how it works in your product
@nikitaeverywhere Great question! Yes, our Builder agent works directly with the agent's codebase. It reads files, edits code, runs commands, checks logs, even takes screenshots of the running preview to verify things look right. So it's not just generating a prompt and hoping for the best. It's iteratively building and refining a full working app with UI, tools, server logic, the whole thing. Think of it more like an AI developer pair-programming your agent into existence, not a prompt generator.
Agentplace
@nikitaeverywhere Thanks! Not just generic instructions, the idea is to shape agents around real workflows, tools, and context. So yes, you can tailor them much more specifically than a one-off prompt generated by a model.
Agentplace
@nikitaeverywhere the magic is in the SKILLS set
Congrats on the launch! What happens when a model gets updated or replaced? How much work is it to re-test and adjust an existing agent?
@ermakovich_sergey Thanks! Good question. On our side, we have internal benchmarks for the Builder agent, so when a new model drops we can test and adapt pretty quickly, usually a day or two. As for the agents users have already built, we don't remove access to older models, so everything keeps working as before. If a user wants to switch to a newer model, we'd recommend testing it on their end to make sure things behave as expected. But nothing breaks automatically.
Agentplace
@ermakovich_sergey, adding to Boris's comment, you can connect any eval tool to enable a controlled change.
Huddle01 Cloud
Interesting that this is built around workflows and not just 'we have AI now.' Quick question: how easy is it for an ops person to pick this up without looping in engineering every time? That's usually where these things break down.
Agentplace
@shalini_umrao Thank you for bringing this up. That's actually who we built this for. The whole point is that an ops person can build and update agents on their own. There's no code to write, you describe what you want the agent to do in plain text, test it in the same window, and hit publish. If something needs fixing you just open the editor, change the prompt, test, publish again. No pull requests, no deploys, no waiting for engineering
Agentplace
@shalini_umrao Exactly. If every small change needs engineering, adoption usually stalls.