Agent37
Run OpenClaw or Hermes for $3.99/mo
-
1Agree the API is the real surface. The part that bit me was auth: once the agent is calling from a backend instead of sitting in your dashboard, you need per agent credentials and scopes, not one shared key everyone reuses. How are you handling permissions at the gateway, and can you stop an agent from calling a write endpoint until a human grants it?
-
2
Great read!
-
2
This is interesting. I like the idea of making agents usable through an API instead of forcing everything into a chat UI.
Do you see Agent37 more as infrastructure for developers, or as something non-technical teams can also use directly?
-
1
That's a good question. Right now developers tend to see the value fastest because they're the ones integrating agents into existing systems. But the longer term goal is to make the underlying capabilities accessible to non technical teams as well without requiring them to think about the infrastructure underneath.
-
-
2
to answer your question: API only, always. the UI is useful for understanding what an agent can do but it's never the thing I actually want to ship. the friction you described about wiring things again for every new use case is exactly why. the interesting design question for your gateway is how you handle agent state between calls. a stateless API is easy to integrate but limits what the agent can do. a stateful one is more powerful but harder to reason about when something goes wrong
-
1
That's a great point. My instinct is that developers generally prefer having more control over state since it makes debugging and integration much easier. The challenge is finding the right balance between flexibility and simplicity without making the system feel too magical.
-
1
the question of what stops SEO farms from doing this at scale probably answers itself over time. the farms that can produce 100 articles a week at genuine quality will be the same farms that are spending so much on quality gates and human review that they're no longer farms. the economics of high-quality content haven't changed. it still costs roughly what it always did to produce something genuinely useful. AI just made the low end cheaper
-
-
-
2
I like the API-first approach. Most agent tools over-index on UI when developers just want clean APIs. Curious: do you have rate limits per agent identity, or per IP?
-
1
Appreciate that. We're still early so we're iterating on some of those platform-level controls based on how people actually use Agent37. Right now the bigger focus has been making agents easy to deploy and integrate but rate limiting is definitely something we're thinking about carefully.
-
-
2
AI makes our work much easier these days, and based on the description of the tool presented here, it will be very useful. Congratulations and good luck with what's to come...
-
1
I appreciate that! The goal with Agent37 is to make AI agents easier to deploy and integrate into real workflows, not just demo environments. There's still a lot to build, but feedback from builders and early users has been incredibly helpful in shaping the direction. Thanks for the encouragement!
-
-
2
Clear useful content
-
1
Much appreciated.
-
-
2
Exactly why the 'wrapper app' era is collapsing under its own weight. If your AI stack relies entirely on a black-box cloud API, you have zero control when it hallucinates, leaks data, or breaks a workflow.
The only real way to enforce human accountability is building on sovereign, local-first architectures. I spent the last few months mapping this exact problem out in the Operator's Field Manual—designing a dual-brain system where the local operator is always the hard anchor, not the model.
If you're building systems where failure isn't an option, sovereignty isn't a feature; it's the baseline.-
1
I get the direction you’re pointing at, especially around control and failure modes. In practice though even local first setups usually still need an external layer for coordination once you start connecting multiple services or workflows. The real problem is less about where the model runs and more about how you define boundaries, accountability and observability across the whole system. How your dual brain approach handles actions that span outside the local environment?
-
1
I see your point. Honestly, it is not some crazy structural hurdle, and if I can set this up, anyone can do it. But once your agent needs to step outside your local machine to touch external APIs or live web workflows, you are dealing with a totally different beast.
The way to handle actions outside your local setup is simply to treat the external world as an untrusted zone.
Instead of letting an agent run wild, you split up the reasoning from the actual execution.
The brain core: this stays inside your local environment and does the heavy lifting, like crunching your private data and deciding what needs to happen next. It never talks directly to the outside world on its own.The bridge layer: This is just a basic, isolated layer meant for handling outside tasks. If the core brain decides the agent needs to update a calendar or hit a third-party webhook, it sends the request here.
The real trick to keeping things accountable is putting a human gatekeeper right at that border.
Instead of full, blind automation where an agent can glitch on an external API error and loop infinitely, the local setup queues the action for a quick manual approval. A human clicks okay, the bridge layer runs the task, and only clean data is allowed back inside. The real problem is definitely system boundaries and knowing exactly what your system is doing. Isolating outside actions into simple, human-gated steps gives you complete control over what leaves your machine and what comes back in. You keep your data completely sovereign at home while still using the live web when you need it.
-
-
-
2
The UI basically stops mattering as soon as an agent touches a production workflow. What matters is the contract: a clean API, a predictable behavior and a way to understand why it decided to do something at 2am. The rest (the UI, dashboards, whatever else) is mostly onboarding and debugging, IMHO.
-
1
I mostly agree with that. Once it’s in production, the UI becomes secondary. The real system is the API contract plus observability around decisions and actions. The only place I still see UI mattering is during iteration and troubleshooting, but even that fades fast once things stabilize. Its like the core product shifts from “how you use it” to “how you trust it.”
-
-
2
I think a lot of agent products are accidentally building destinations instead of infrastructure. A UI is great for testing and demos, but the moment an agent becomes part of a real workflow, API access starts mattering more than the interface itself. Curious if you've noticed any patterns in how people are integrating it so far.
-
1
I’m seeing the same split. Early users treat it like a product but the ones getting real value quickly move toward embedding it into existing systems and workflows. At that point the UI is mostly just for setup or inspection. Integration patterns seem pretty simple so far either direct backend calls or wrapping it into scheduled jobs and internal tools. Are you seeing any cases where people still prefer keeping the agent as a standalone destination instead of embedding it?
-
-
2
but it isnt hard to do that
-
1
Yes it’s not hard technically. The harder part is usually everything around it like reliability, observability and knowing what the agent actually did once it’s running inside a real workflow. That’s where most setups start to feel less “plug and play” than they look at first.
-
-
2
I’ve been seeing the same pattern. UI works fine for demos but once you start thinking about actual usage, most of it moves to backend or automated flows anyway. Feels like agents are slowly becoming infrastructure pieces rather than tools people sit inside. In that case, API first design just makes more sense.
-
1
Yeah, that’s exactly what it’s starting to feel like. UI is great for discovery and setup, but real value shows up when the agent becomes something you can embed and forget inside workflows. Once that shift happens, it stops being a “tool you use” and becomes more like infrastructure you rely on in the background.
-
-
2
Not fully convinced agent UIs are going to stay relevant for long. Most of the real use cases I’ve seen are either scheduled tasks, backend workflows or integrations into existing products. In all of those, the UI is basically unused after setup. We’re still early in figuring out what “the right interface” for agents actually is. Would be interested to hear if anyone here has a strong case where UI is still essential.
-
1
I think UI still has a role but mostly in early setup and debugging. When you’re testing prompts or behavior, it’s useful to see what the agent is doing step by step. Also for non technical users, it helps as a control layer. But for real usage, most workflows move to backend, scheduled tasks, or integrations. After that, UI is rarely needed. Feels like UI is more for setup than actual execution in most cases.
-
-
2
The API-first instinct is right. The next question that comes up once agents are embedded in real workflows is governance — not just "can I call this agent from my backend" but "who authorised this agent action, and can I prove it afterward?"
That's the gap we ran into building AlertEngine. The agent can call the API. But when it does something unexpected at 2am, you want an immutable record of what happened and why — not just logs.
-
1
Yeah this is the part most people only realize after things go live. Once agents are in real workflows, it’s not just about calling them, it’s about knowing why they acted and being able to trace it clearly when something goes wrong. Are you treating governance as a separate layer, or building it directly into the agent runtime?
-
-
2
I'd be careful treating this as a UI-versus-API decision too quickly.
The interesting question may not be how people prefer to interact with the agent.
It may be what role the agent is expected to play once it becomes part of a real workflow.
Those sound similar, but they can lead to very different conclusions about the product, the buyer, and which signals deserve confidence early on.
I wouldn't make that call casually from the current feedback.
-
1
I agree this is less about UI vs API and more about the role the agent plays in a system. Right now we’re seeing signals from how people start using it, not necessarily how it should exist long term in a workflow. Feels like the real clarity only comes once it’s running in production across a few different environments, not just early usage patterns. Curious what signals you personally would trust early on when defining that role?
-
1
Possibly.
The reason I stopped short is that I don't think the useful part is the list of signals.
I think it's the decision those signals are being used to make.
That's one of those things that can quietly shape the entire product if the wrong signal gets trusted too early.
I wouldn't try to unpack that properly in a thread.
If you're curious, drop your email and I'll put together the tighter version.
-
-
-
1
Interesting approach! Is the API public?
-
1
Strongly agree with this, and not even in the agent space specifically — I hit the same thing building a language analysis tool. The actual hard part of my backend is racing several LLM calls in parallel and handling whichever fails or times out, but none of that should leak to whatever consumes it. Once I committed to a clean API surface, the messy part (retries, fallback when a model is slow, normalizing wildly different outputs into one schema) became something I could iterate on without ever touching the consuming side.
The UI-first instinct makes sense for demos, but it quietly assumes the dashboard IS the product. The moment someone wants to embed this in an existing workflow, that assumption breaks, like you said.
One thing I'd be curious about with your gateway: how are you handling partial failures or slow responses on the API side? That's the part that always ends up leaking through "clean" interfaces for me — a 200 response that took 25 seconds doesn't feel clean from the caller's side even if the contract is simple.
-
1
I think that's where a lot of the complexity actually lives keeping the interface simple while hiding the messiness underneath. We're still iterating on that side, but my bias is that callers should have as much visibility as possible into failures and latency rather than pretending everything is fine behind a clean API. A simple contract is great but not if it hides information that users need to build reliable systems.
-
-
1
I agree with the API-first direction. For agents, the dashboard is usually useful for demos, but the real adoption happens when it can be called from an existing workflow without forcing the team to change how everything else works.
The tricky part is probably not the API itself, but making state, retries, permissions, and failures predictable enough that people trust it in production.
-
1
Absolutely. Getting an API working is one thing but making it predictable enough for production use is a completely different challenge. In my experience, trust comes less from capabilities and more from knowing how the system behaves when things go wrong. That's where a lot of the real engineering work ends up happening.
-
-
1
Strongly agree. Once agents are API-first, one thing I’d add early is cost observability per workflow. A clean API makes integration easier, but it also makes spend easier to hide because calls are happening in scripts, cron jobs, retries, background jobs, etc.
For our own AI tool work I’ve found the useful view is not just total tokens, but which workflow/model/prompt shape caused the spike. Otherwise you only notice when the bill shows up.
Tiny plug since it is directly related: I’m building TokenBar for Mac to keep token usage visible while working with LLMs: https://tokenbar.site/
-
1
That's a really good point. Once agents move into background workflows, visibility becomes a lot harder than when everything happens through a UI. I like the idea of breaking usage down by workflow and model rather than just showing aggregate token counts. In many cases, the expensive part isn't the agent itself- it's a retry loop, a prompt change or a workflow that quietly scales beyond what you expected. Will definitely be paying more attention to observability as Agent37 evolves. How are you handling costs across multiple providers/models?
-
-
1
Agree the UI stops mattering once an agent is in a real workflow. But going API-first quietly changes who your buyer is. A UI tool sells itself in a demo. An API tool lives or dies on time-to-first-successful-call, your buyer is now a developer who bounces if the first integration takes more than ten minutes. So the gateway README and a copy-paste example matter more than any dashboard. One thing I learned running an integration-heavy business for years: what keeps people embedded is not the API, it is never breaking the contract. The first time a silent change breaks someone's pipeline at 2am, they rip you out and do not come back. Version hard, deprecate loudly.
-
1
noice
-
1
Completely agree with the API-first approach. Once you move past the demo phase, you just want a reliable endpoint to plug into an existing stack. Out of curiosity, how are you planning to tackle complex orchestration or rate-limiting/queues when these API-driven agents start chaining multiple long-running tasks?
-
1
This resonates. The UI isn’t the problem — the “can I actually integrate this into my workflow?” question is.
A clean API also forces the right constraints: clear inputs/outputs, idempotency, retries/timeouts, and observability. Otherwise the UI hides a lot of flaky glue (auth, rate limits, context size, tool failures) until you try to productionize it.
Curious: how are you handling versioning + reproducibility?
Do you pin model/tool versions per request?
How do you log inputs/outputs safely (PII) while keeping runs debuggable?
-
1
API over UI is the right call. but it concentrates vendor dependency harder. when your agent provider gets acquired - and several have recently - you're not just swapping a URL, you're re-benchmarking every workflow that assumed consistent behavior.
-
1
That's a fair point. A clean API makes integration easier, but it doesn't eliminate dependency risk underneath. I think that's one reason abstractions and provider flexibility matter so much. The less business logic that's tied to a specific provider's behavior, the easier it is to adapt when the landscape inevitably changes.
-
-
1
the "wiring things again for every new use case" problem is exactly why most agent tools feel like demos rather than infrastructure.
treating the agent as a service behind an HTTP endpoint rather than a UI makes it composable — that's the right instinct.
curious how you're handling auth and rate limiting on the gateway layer. that's usually where the "simple API" gets complicated fast.
-
1
Good point, that’s where it gets real. Auth is scoped API keys at workspace level, with permissions per agent and action. Rate limits are split between workspace level caps and per endpoint limits. Hardest part is not basic limits, it’s keeping things stable when agents start chaining calls. That’s where we’re now thinking about queues and backpressure.
-
1
queues and backpressure for chained agent calls is the right problem to be solving — that's exactly where most agent frameworks fall apart in production.
are you building the queue layer yourself or leaning on something like BullMQ or Inngest? curious what tradeoffs you're hitting at this stage..
-
-
-
1
We are looking for someone who can lend our holding company 175,000 US dollars.
We are looking for an investor who can lend our holding company 175,000 US dollars.
We are looking for an investor who can invest 175,000 US dollars in our holding company.
If you lend our holding company 175,000 US dollars, I will return your money to you as 350,000 US dollars on 30/03/2027.
If you invest 175,000 US dollars in our holding company, I will return your money to you as 350,000 US dollars on March 30, 2027.
I will return your money to you as 350,000 US dollars on March 30, 2027.
I will repay the 175,000 US dollars you lent to our holding company as a loan to you as 350,000 US dollars on March 30, 2027.
We will use the 175,000 US dollars you invested in our holding company to grow our business, and on March 30, 2027, we will return your money to you as 350,000 US dollars.
I will return the 175,000 US dollars you invested in our holding company to you as 350,000 US dollars on March 30, 2027.
You will receive your money back as 350,000 US dollars on March 30, 2027.
I will refund your money to you as 350,000 US dollars on March 30, 2027.
You will be earning twice as much in a few months.
You will have doubled your money in a few months.
So how will we increase your money?
With the 175,000 US dollars you lent to our holding company, we will establish an artificial food production company.
With the 175,000 US dollars you invested in our holding company, we will establish an artificial food production company.
With the 175,000 US dollars you invested in our holding company, we will establish an artificial food company that produces tastier, higher quality, healthier, protein-rich, and nutritious food products.
In the artificial food company we will establish, we will use the genetics of animals, vegetables, plants, and fruits to artificially produce tastier, healthier, and protein-rich food products.
We will produce a wide variety of artificial food products.
We will produce fruits, vegetables, snacks, meat products, dairy products, artificial water, beverages, and other artificial products.
Since the food products we will produce are artificial foods, we will have prevented famine.
Many countries today are experiencing famine and drought; by establishing an artificial food company, we will be able to prevent both famine and drought.
We will be able to produce more protein-rich, healthier, and tastier food products in a laboratory environment without the need for agriculture or animal husbandry.
We will produce new types of fruits and vegetables; these new types of fruits and vegetables will greatly attract people's attention.
Thanks to our artificial food project, humanity will be able to easily produce food without agriculture and animal husbandry.
As wars increase around the world, leading to increased famine, the artificial food products we will produce will be in high demand globally.
By developing systems that can produce water from the air, we will also be able to prevent water scarcity.
As our capital increases, we will open artificial food production facilities in all countries of the world. Our goal is to sell the artificial food products we produce to the whole world.
The products we will produce will be longer-lasting, and because they will be longer-lasting, we will also save money. We will produce environmentally friendly products.
Since the food products we will produce will be artificial foods, people will be able to consume the same fruits and vegetables in both summer and winter.
People will be able to consume summer fruits and vegetables at cheap prices even in winter.
We will sell the artificial food products we produce at a low price, so people will be able to buy healthier, tastier, higher-quality, and longer-lasting food products at a low price, and in this way, we will prevent global food shortages.
The products we produce will have a long shelf life; since we will be producing long-lasting food products, they will be environmentally friendly, and in this way, we will also save money.
Thanks to our artificial food project, people will now be able to consume high-quality food products at affordable prices.
How will we market the artificial food products we produce?
We will sell the artificial food products we produce internationally to the whole world.
By selling our artificial food products internationally, we will generate more profit.
We will ensure that our products are sold internationally by using 13 different methods.
We will market our product using 13 different methods.
1. Method: By making agreements with supermarkets, grocery stores, and food retailers in many countries, we will ensure that our products are sold by supermarkets and all retailers in many countries.
2. Method: As our capital increases, we will open our own markets worldwide to sell our artificial food products directly to consumers.
3. Method: We will launch a dedicated e-commerce website for our artificial food products. This platform will allow customers worldwide to purchase our products online, which will significantly boost our global sales volume.
4. Method: We will offer our artificial food products for sale on the online sales sites of many countries.
5. Method: By selling our products to wholesalers in many countries, we will enable them to sell our products to local retailers in their own countries. Thanks to the wholesalers, our products will sell even faster.
6. Method: We will implement a referral system. If people find customers for our products and help sell them, they will receive a 25% commission for each customer and thus earn income.
7. Method: We will partner with high-reach influencers across platforms like Instagram, TikTok, and YouTube to promote our products. This strategy will allow us to leverage their large audiences and significantly increase our sales.
8. Method: By utilizing advertising platforms such as YouTube Ads, Google Ads, and Facebook Ads, we will reach a global audience with our promotional videos, which will rapidly accelerate our sales growth.
9. Method: We will attract millions of customers to our product using our own advertising techniques. Thanks to our strong advertising network, our artificial food products will sell quickly.
10. Method: We will promote our artificial food products by placing posters in areas with high population density in many countries.
11. Method: We will pay news and blog sites to advertise our product, thereby making it known to a large international audience.
12. Method: We will send a promotional text about our product to millions of emails using email marketing.
13. Method: We will advertise our product internationally on television channels.
By using various advertising techniques, we will achieve significant profits within a few months, and our products will become even more popular internationally.
How much profit will we make, on average, from the artificial food products we produce?
We decided to enter the artificial food sector because it is an innovative sector; our goal is to become a pioneer in the artificial food sector.
Our products will sell very well internationally because we will be producing healthier, tastier, more nutritious, and protein-rich artificial food products.
Even if we sell 800,000 kilograms of tomatoes at 3 American dollars per kilogram in just 4 months, our earnings for those 4 months would amount to 2,400,000 American dollars.
Just selling tomatoes alone would bring us millions of dollars in revenue within 4 months.
Because we will be producing and selling not just tomatoes but also various types of artificial food products, we will generate billions of dollars in revenue within a few months.
The artificial food products we will produce will be tastier, richer in protein, and healthier, so they will be in high demand internationally and will bring us billions of dollars in revenue.
Since we will be producing new types of fruits and vegetables, it will attract a lot of interest from people and will increase our sales rate even more.
We will open our first artificial food production company in Brazil.
We will establish our first production company in Brazil because it is a country where we can both source production materials and make more sales.
How can you contact us?
For detailed information about our artificial food project, please send a message to my Telegram username or Signal contact number below. I will provide you with detailed information.
To learn how you can lend our holding company 175,000 US dollars, please send a message to my Telegram username and Signal contact number below. I will provide you with detailed information.
To learn how you can increase your money by investing 175,000 US dollars in our holding company, send a message to my Telegram username or Signal contact number below. I will provide you with detailed information.
To learn how you can invest 175,000 US dollars in our holding company and participate in our artificial food project, please send a message to my Telegram username or Signal contact number below. I will provide you with detailed information.
For more detailed information, please send a message to my Telegram username or Signal contact number below. I will provide you with detailed information.
My Telegram username:
@adenholding
Signal contact number:
+447842572711
Signal username:
adenholding.88
-
1
Great updates! The new chat UI makes Hermes much easier for non-technical users, and the $9.99 unlimited plan is a solid move for accessibility. Excited to see how people use this!
-
1
Absolutely! Simplifying the experience for non-technical users opens up a whole new world of possibilities. This approach could definitely make more complex tools more user friendly
-
-
1
Such a great update with the new UI! A clean chat-style interface really lowers the barrier for non-technical users. Curious—have you come across anything similar on other platforms, or is this a fresh approach from what you’ve seen?
-
1
I am glad that you liked the plan. Affordable pricing seems to be a big trend and it’s great to see it starting to take hold in this space. It could make platforms much more accessible for casual users and testers
-
-
1
This pricing plan makes it so much easier to experiment with the platform. Unlimited tokens at $9.99, Big win for users! Do you think affordable pricing could become the new standard in this space?
-
1
Well I hope so! Affordable pricing makes experimentation accessible to everyone and I think as more platforms see the success of this approach, it could definitely catch on. Let’s see how it plays out..
-
-
1
Such a smart move with the new UI! simple chat interface will make it so much easier for non-technical users to dive in.Have you seen other platforms doing something similar,or is this a first in your experience?
-
1
While there are some tools with chat interfaces, I think this type of simplicity is a bit rare for complex platforms like this one. It is all about making things more approachable without losing functionality
-
-
1
Love how you’re making powerful tools more accessible! The new UI and affordable plan are definitely going to help a lot of people get started. Do you think more platforms should focus on simplifying their UI to attract non-technical users?
-
1
It seems great to be able to utilize Telegram or Discord as Hermes control centers How secure are those integration?
-
1
Only authorized users are able to submit orders and receive updates thanks to role-based access control and fully encrypted Telegram and Discord integration. You can be sure that your data is secure since we take security very seriously.
-
-
1
Continuous workflow sounds great but how does Hermes handle data storage? Is there a way to manage and backup data from ongoing tasks?
-
1
Well hermes stores all your data securely and allows you to manage and back it up as needed. We ensure everything is encrypted and you have full control over your files and task histories, ensuring easy access when you need it.
-
-
1
I adore the concept of not requiring a VPS setup. When you first set up Hermes, how quickly does the environment launch?
-
1
You are up and running in about 60 seconds.. we have put a lot of effort into making the setup quick and simple, with no waiting for configuration and provisioning
-
-
1
To be honest,anyone in charge of several agents or workflows could save a ton of time with this.What do you think Agent37s main advantages over conventional self-hosting options are?
-
1
Simplicity and constancy are the main benefits.. Agent37 eliminates the need for manual configuration, server uptime concerns and infrastructure management. You simply log in and begin utilizing it which ultimately saves you a great deal of time and work.
-
-
1
This method of job automation works well! Maintaining context between sessions appears to be quite beneficial for debugging and automating repetitive tasks. How it manages bigger datasets and more complex procedures intrigues me.
-
1
Well even with big datasets, the system is built to manage complicated workflows well. Hermes can pick up where you left off thanks to the persistent memory, allowing you to continue without the typical disruptions. Tell me if you want to test it out.
-
-
1
Self-hosting has always been a headache for me. This appears to be the solution I've been looking for! I would like to know whether you have any demos or trial versions so I can see how well it works before committing.
-
1
I appreciate your help. Free trial is good option. Before you commit, we want to ensure that it is a good fit for your workflow.
-
-
1
It's a great concept to have a continuous workflow without thinking about Docker or servers. How does the real-time integration with Discord and Telegram operate? That seems like it woulld be really useful for short coding assignments.
-
1
I am glad you believe that! Yes you may communicate with your Hermes instance while on the go thanks to the integration with Telegram and Discord. You don't need to open a browser in order to send orders and get updates. It all comes down to improving the efficiency of your workflow.
-
73 Comments
this is useful. congrats!
Thank you for the encouragement!
This is one of those products where the value becomes obvious once you've tried managing agents in production. Building is fun. Maintaining everything around it is usually the hard part.
Yeah, that’s exactly what we kept running into. Prototype stage feels simple, but once you start handling real users, the “everything around the agent” layer becomes the actual product — state, isolation, failures, retries, tool drift, all of it starts compounding. Have you seen any part of that become the biggest pain for you specifically?
this is the part most builders underestimate. shipping the agent is easy, keeping it stable across thousands of users is the hard work
Exactly. Getting an agent to work is often the easy part. Keeping it reliable, consistent, and observable across thousands of users is where the real engineering challenge begins. That's the gap we kept seeing, and a big reason we decided to focus on the operational layer rather than the agent itself.
The architecture question I'd push back on: in enterprise deployments, the hard part isn't provisioning one agent per customer — it's defining what each agent is authorized to decide vs. what it needs to escalate. We've had clients where agents technically completed tasks correctly, but made decisions that required human sign-off under their compliance framework. Nobody had written that down before the agent went live. The "one API call" abstraction is elegant. The accountability layer underneath it is where most B2B deployments stall.
That's a great point. Making agent deployment simple doesn't remove the need for clear boundaries around what an agent can and can't do. In many enterprise environments, the challenge isn't whether a task can be automated—it's knowing when human review or approval is required. The infrastructure layer solves one part of the problem, but accountability, governance, and decision boundaries are equally important for real-world deployments. Appreciate you highlighting that distinction.
Creating the instance looks like the easy part. Keeping hundreds of stateful agents observable and upgradeable is probably where things get messy.
How are you handling rollbacks and version changes without breaking customer memory or files?
You're absolutely right that creating the instance is only the beginning. Once you have hundreds of persistent agents, observability, upgrades, and reliability become much bigger challenges than provisioning itself. A lot of the complexity lives in making changes safely while preserving the state and continuity that users depend on. Those operational concerns are exactly what pushed us toward focusing on the infrastructure layer in the first place.
the operations side is definitely the harder problem long term. building the agent is a weekend project now, but the moment you need per-customer isolation with persistent memory and reliable tool access, you're basically running a mini cloud provider. smart to abstract that away — most teams I've seen underestimate the infra cost until they're 3 months in and spending more time on container orchestration than on the actual agent logic.
Appreciate this. That's exactly the shift we kept seeing. Building an agent has become increasingly accessible, but operating large numbers of customer-specific agents reliably is a very different challenge. The "mini cloud provider" comparison is surprisingly accurate. Once you add isolation, persistence, integrations, and reliability requirements, a lot of the work moves away from the agent itself and into the infrastructure around it. That's the layer we're trying to simplify for builders.
The part I’d be most interested in is the runtime/action boundary.
If a customer can spin up a persistent agent with memory, files, tools, scheduled runs, terminal access, and integrations, the security question is not only whether the instance is isolated. It is also:
who authorized this agent to exist,
which tools is it allowed to use,
what source triggered this action,
what evidence supports it,
and why is it allowed to execute now?
Sandboxing protects tenants from each other. But high-impact actions inside the sandbox probably need their own gate too, especially for exports, record changes, messages, workflow triggers, terminal commands, or billing/security-sensitive operations.
The "one API call per customer" approach is exactly how this should work. Most frameworks force you into using their specific dashboard or UI, but if you're actually trying to build a native feature into an existing app, you just want a clean backend service you can query. "Render for agents" is a great way to look at this. Nice work, Vishnu.
Thank you, really appreciate that. That was a big part of the thinking behind it. Many teams want to embed AI agents directly into their existing products, not force users into a separate interface. The goal is to make the infrastructure disappear into the background so builders can create the experience they want on top of it. Glad the approach resonates, and thanks for the support.
This is the part that feels most important to me too: prototype UX is not the same as production reliability.
I am working on Tokens Forge from the other side of the stack, mostly model routing, usage tracking, and giving builders one OpenAI-compatible API for multiple model families. The pattern I keep seeing is similar: the agent itself is only one layer; the hard parts are isolation, routing, observability, cost controls, and clear failure states.
For your customer-specific agent instances, how are you thinking about debug visibility for the end customer? Do they get enough trace/history to understand why an agent failed, or is that mostly an operator-facing tool right now?
That's a great question. We think visibility is a critical part of making agents usable in production. Infrastructure is only half the problem—when something goes wrong, builders need enough context to understand what happened and debug it quickly. We're spending a lot of time thinking about observability and traceability because an agent that can't be inspected is difficult to operate at scale.
Smart to charge per always-on agent instead of per seat. It ties your revenue to your customers' growth, which compounds when they scale, but it also means your MRR rides on their retention, so the founders you onboard need to be ones who actually ship. At $3.44 a month the moat won't be the infra, it will be the integrations catalog and uptime, because that is the piece a customer can't rebuild on a weekend.
Really appreciate this perspective. You're right that long-term value comes from much more than simply running an agent. Reliability, integrations, and the overall developer experience become increasingly important as customers scale and depend on the platform for real workloads. I also agree that our success is closely tied to our customers' success. The more they can ship, grow, and serve their own users without infrastructure becoming a bottleneck, the better the outcome is for everyone involved.
The infrastructure problem you're pointing to is real and it's still massively underestimated. Getting an agent to work in a prototype is genuinely easy. Making it work reliably for 50 different customers, each with their own memory, their own tool connections, their own execution history, is a completely different challenge. One thing I'd push on is that isolation is only half the answer. The other half is shared context across the system — not shared between customers, but shared between the agents serving a single customer across different sessions. Without that, agents restart every conversation with no awareness of previous decisions, previous constraints, or previous failures. You end up with inconsistency that's hard to diagnose because nothing technically breaks. One thing worth thinking about early: logging the "why" of a decision alongside the output, not just the output itself. When something goes wrong at scale, having decision traceability is what separates teams that debug quickly from teams that start over. What's your current approach to surfacing when agent behaviour drifts from expected output across different customer instances?
Really appreciate this thoughtful perspective. I completely agree that isolation alone isn't enough. Persistent context, decision history, and continuity across sessions become incredibly important once agents are handling real workflows over time. The point about logging the "why" rather than just the output also resonates—many of the hardest issues aren't outright failures, they're subtle behavior shifts that only become visible later. Observability and traceability are definitely areas we're spending a lot of time thinking about, because as you said, reliability at scale depends as much on understanding agent behavior as it does on running the infrastructure itself.
AI costs are becoming a real issue for small teams. I've been experimenting with multi-model setups to reduce cost.
Absolutely. As teams move from experimentation to production, AI costs become much more visible. Multi-model setups are an interesting approach because not every task needs the most expensive model. We've seen more builders thinking about routing workloads based on cost, latency, and capability rather than relying on a single model for everything.
the framing of "infrastructure became the real challenge" past prototype is the part that rings true, the demo of "give your customer an AI agent" is easy, the part that actually eats months is exactly what you listed, isolated state per customer, monitoring, what happens when one instance misbehaves. curious what the actual breaking point was for you, was there a specific moment building your own product where the infra work started outweighing the product work, or did it become obvious gradually
This hits on such a real pain point. So many founders nail the prototype but then drown in infrastructure once they try to give every customer their own persistent agent with memory, tools, and isolation.
The “one API call per customer” approach looks super clean. How’s the pricing structured? Is it usage-based or per agent?
Also curious — who do you see as your early users? SaaS companies embedding agents for their own customers, or smaller teams that want to offer AI agents without managing all the DevOps headache?
Nice tool.
The distribution challenge here is wild — not the tech, but getting from "one customer has an agent"to "100 customers adopt it." How are you thinking about expansion? Land-and-expand per customer, vertical wedge where one industry unlocks the rest?
This is a massive pain point for developers building multi-tenant AI systems. The architecture looks clean, and managing custom VPS/containers
for each user manually is indeed a huge operational overhead. Congrats on the launch!
Thank you, really appreciate that! That's exactly the problem we kept running into. Managing isolated environments for customers can become a significant operational burden long before teams expect it. Glad the approach resonates, and thanks for the support.
This is very useful. I am also planning to launch new AI tool and it would be great help to me. I will dig how to use this in my tool.
Thank you, really appreciate that! Best of luck with your AI tool launch. If you're building customer-facing agents, hopefully this can save you some time on the infrastructure side and let you focus more on the product itself.
Wishing you a successful launch!
operating it reliably, 100%. building the agent is the fun part. the moment you have real users with real data, you're suddenly debugging edge cases at 2am that you never imagined. we had a similar thing building aisa.to — the conversational AI itself was the easy bit, making it score consistently across thousands of different users was a completely different problem. your infra play makes sense because most builders underestimate the ops side until they're drowning in it
Appreciate this, and that resonates a lot. The 2am edge case debugging is very real. Things that never show up in testing suddenly appear once you have real users, real data and thousands of different interactions flowing through the system. Consistency and reliability at scale end up being a completely different challenge from getting the AI working in the first place. That's exactly the gap we're trying to help teams avoid having to build from scratch.
The infrastructure-per-customer problem is genuinely underrated, most people don't hit it until they're already mid-scale and it becomes painful fast. What I find interesting here is the pricing model, $3.44/month per always-on agent is low enough that it doesn't become a blocker for the founder selling to their own customers. The isolated environment approach also solves a compliance and data separation headache that nobody talks about until a customer asks "where does my data live?" Curious how you're handling agent cold start times when an instance hasn't been active for a while, that's usually where the "persistent" promise gets tested.
Really appreciate this thoughtful breakdown. You're right that many of these challenges don't become visible until teams have enough users and agent instances for operational complexity to start compounding. Data isolation and customer-specific environments are also things that often seem secondary early on, but quickly become important once customers start asking deeper questions around reliability, security, and ownership of their data. On pricing, a big goal was to make the operational layer affordable enough that teams can focus on building their product rather than constantly worrying about infrastructure costs.
This hits close to home — we ran into the exact same isolation problem building ThirdOS (AI website manager for local businesses).
Our architecture ended up being kind of wild: we boot the agent environment from a VHDX image — either directly on the client's machine as a VM, or on an NVIDIA Jetson Nano sitting on their local network. The Nano approach is underrated for small business clients who don't want cloud latency touching their site edits.
The piece that made it all click: a reverse SSH tunnel proxy back to our central infrastructure. The client machine or Nano initiates the outbound connection — no open ports, no firewall drama, no IT guy required. The tunnel keeps the agent's state tethered back to home base even when the edge device is behind a random router.
On top of that we run what we call StateOS internally — a persistent environment layer that keeps the AI's memory, tool context, and session state alive across reboots. Context drift was wrecking us until we treated state as a first-class citizen living OUTSIDE the model context window.
We went per-client domain licensing instead of per-instance because local businesses want to feel like they OWN their AI, not rent a slice of one. Different GTM but same isolation philosophy as what you're building.
Curious — how are you handling state persistence across instance restarts? Is memory attached to the container or externalized?
Really appreciate you sharing this. The reverse SSH tunnel + StateOS approach is fascinating. We came to a similar conclusion that state needs to be treated as a first-class concern rather than living purely in the context window. Different GTM models, but very similar thinking around isolation and ownership. Always interesting to see how teams converge on the same challenges at scale.
One of my friends is a non-technical founder with customers waiting for their agent, but having issues building and shipping it in a way that works. Have you thought about packaging this for everyone to be able to create it from UI, and even be able to just prompt to create and edit their agent?
That's a really interesting direction, and something we've thought about quite a bit. Right now we're focused on making the infrastructure layer simple for developers and teams building AI products. But the idea of letting non-technical founders create, configure, and manage agents through a UI or even through prompting—is definitely compelling. We've seen the same pattern where the demand for AI agents often exists before the technical resources to build and operate them. Lowering that barrier could open up a much broader set of use cases. Really appreciate you bringing it up.
Good work
The per-customer isolation is the right call, but the failure mode I'd watch isn't the agent misbehaving — it's config drift between instances. I just lost hours to a bug where a local .env baked into a Docker image silently overrode my production secrets: same code, different environment, no error thrown, completely different behavior. Multiply that across N isolated customer environments and "works in instance A, silently wrong in instance B" becomes the scary one. Any guardrails planned for surfacing when an instance's effective config doesn't match what the dev expects?
ran into this building pm workflow agents. the infrastructure problem is real - you've abstracted the hard part. but what nobody talks about: when a customer's agent does something wrong, who explains it? one API call doesn't answer that.
That's a great point, and I completely agree. Making agent deployment a one-API-call experience doesn't remove the responsibility of understanding what the agent did and why. In many cases, explainability, observability and debugging become just as important as the agent itself. Our focus is on reducing the infrastructure burden but you're right that operational visibility is a critical part of making agents usable in production. An agent that can't be understood when something goes wrong is difficult to trust, no matter how easy it is to deploy.
yeah that's the part that bites you in production. had a case where the agent did exactly what I asked - but the intermediate steps were wrong in a way I only caught because I kept a trace. without that it would've just looked like a success.
“This resonates — we’ve been seeing the same pattern building AI agents: context drift after long sessions is the #1 silent failure mode. Curious how you’re handling token cost as conversations grow? We ended up building real-time compression for exactly this.”
This definitely resonates. Context drift is one of those issues that often stays hidden until agents have been running for a while with real users. It's easy to overlook during demos but it becomes a major reliability challenge over time. Real-time compression sounds interesting. We've found that long term memory management ends up being just as important as the model itself once conversations and agent histories start growing.
this is cool and really usefull
Thank you for the appreciation!
In the fantastic world of AI, more and more tools are emerging, making it increasingly difficult to stand out in this veritable red ocean. Any tool that saves TIME and MONEY will naturally stand out in the market, and this tool seems to be one that could achieve that goal, which is half the battle won. Congratulations and best of luck on your journey that has just begun.
Thank you for the thoughtful comment and encouragement. I completely agree that the space is becoming increasingly crowded, which makes it even more important to solve a real problem rather than just add another AI layer. Our goal is simple: help teams save time and avoid spending months building infrastructure that isn't core to their product. We're still early, but it's encouraging to see so many builders resonate with the operational challenges around running agents in production.
This is really cool, Vishnu! I'm actually building something very similar as a feature for my current job, so I completely understand the infrastructure challenges you're talking about here.
Thank you, really appreciate that! If you're building something similar, then you probably know firsthand how quickly the infrastructure side starts taking over. The agent is usually the easy part—it's everything around persistence, tooling, isolation, and reliability that gets interesting. Wishing you the best with your project as well. It's always great to hear from people tackling similar challenges.
This solves a problem i keep hearing from teams building AI products. Getting agent to work is one thing but managing persistent state, integration, isolation and reliability across real sustomers is a completely different challenge. I like that you are focusing on the operational layer rather than just agent itself.
Really appreciate that. That's exactly the pattern we kept seeing. Most teams can get an agent working but once real customers start using it, the operational challenges quickly become the bigger problem. State, integrations, isolation, reliability and all the edge cases around them tend to consume far more time than expected. That's why we decided to focus on the infrastructure layer and make those pieces easier to manage.
Great work!
Thank you, really appreciate it!
increibleee
Thank you! Really appreciate the support
Interesting idea. How are you handling costs for agents that stay persistent but aren't actively being used?
Great question. A big focus for us has been making persistent agents affordable enough that teams don't have to constantly think about spinning instances up and down. We optimize around keeping agents available while minimizing idle infrastructure overhead behind the scenes. The goal is that builders can treat an agent more like a long lived customer workspace rather than something that needs to be recreated every time a user comes back.
This is a strong direction — and you’re clearly describing a real shift from “agent building” to “agent operations.”
What stands out here is that you’re not competing on the model or the agent logic anymore. You’re abstracting the hardest part most teams underestimate: isolation, state, tool access, and lifecycle management per customer. That’s actually closer to “Render for agents” than an agent framework.
The positioning is strongest when framed less as “build agents easily” and more as “run agents in production without infra overhead.” Because most teams can prototype an agent in a weekend — but very few can reliably run 100+ stateful, tool-using agents with memory and integrations without building internal infra.
The real validation question for something like this won’t be “can it host agents,” but:
does it remove enough operational pain that teams stop stitching together their own container + queue + memory + tool stack?
On your main question — in practice, most teams hit the same wall you’re targeting: building the agent is the fun part, operating it is the product.
Really appreciate this breakdown.
The "agent building vs agent operations" distinction is exactly what led us to build this. We found that once teams moved beyond a prototype, most of the engineering effort shifted away from prompts and workflows and toward isolation, state management, tool reliability, and lifecycle management.
I also like the "Render for agents" comparison. Our goal is to let teams focus on the agent experience itself instead of assembling and maintaining the infrastructure stack around it.
And I agree with your validation point — hosting agents isn't the challenge. The real test is whether we can remove enough operational complexity that teams no longer feel the need to build and maintain that layer themselves.
This capture a real shift in the space. Building agents is straightforward compared to everything required to run them reliably in production with real users , state and integrations. Strong execution on a very real pain point.
Appreciate that. That shift is exactly what pushed us in this direction, prototypes feel deceptively simple, but production quickly becomes an infrastructure problem more than an AI problem. Most of the real complexity shows up around state, integrations, reliability, and all the edge cases that don’t appear in demos but matter a lot with real users. That’s the gap we’re trying to abstract away.
We ran into something similar, orchestration becomes the real product once you go beyond a few agents. I like the approach here of abstracting away infra complexity so builders can focus more on actual agent behaviour.
Yeah, this resonates a lot. Once you move past a handful of agents, orchestration stops being “supporting infrastructure” and effectively becomes the product itself coordination, state flow, retries, and tool interactions start defining the system more than the individual agents. That’s exactly the layer we’re trying to hide so builders can stay focused on agent behavior and outcomes rather than spending most of their time wiring everything together.
This hits very accurately. The agent itself is usually the easy part but once you go into production, keeping state, tools and integration reliable becomes a completely different problem. Most people underestimate that shift until they hit it.
Yeah, exactly that’s the part that doesn’t really show up in demos. Everything feels clean when it’s a single agent in isolation but once real users, state, and external tools come in, the system complexity grows way faster than the actual AI part. That shift tends to surprise people the first time they run into it at scale.
This is one of those ideas that looks simple on the surface but gets complex very quickly in practice. Once you scale even small issues like memory drift or tool instability start compounding. Solid direction tackling it at the infrastructure level.
Appreciate that. That's exactly what we kept seeing in practice. A single agent demo can look great, but once you have real users, persistent state, and multiple tool integrations, small issues start compounding very quickly. A big part of what we're trying to do is remove that operational burden so teams can spend more time improving the agent itself instead of constantly managing the infrastructure around it.
This comment was deleted 2 months ago