Every week, another AI agent marketplace launches. They all promise the same thing: browse thousands of agents, find the perfect one for your task, and deploy it with a click. It sounds great in a pitch deck, but it misses the point entirely.
The problem isn't that developers can't find AI agents. The problem is that they can't deploy them, maintain them, or trust them in production systems.
I've spent the last year building ArcSphere, and we made a deliberate choice to solve a different problem than everyone else in this space. Not because we're contrarian, but because we've actually tried to ship AI agents to production.
The Discovery Problem Is Already Solved#
Let's be honest: finding AI agents isn't hard. GitHub has thousands of them. Hugging Face has models and demos. ChatGPT can write you a custom agent in 30 seconds. The internet is drowning in AI agents.
The real questions developers ask aren't "where do I find an agent?" They're:
- How do I run this agent reliably in my infrastructure?
- How do I update it without breaking my application?
- How do I know what version my users are actually running?
- How do I roll back when something goes wrong?
- How do I handle API keys and credentials safely?
These are deployment problems, not discovery problems. And most AI agent platforms completely ignore them.
Why Traditional Marketplaces Break Down#
Traditional app stores work fine for end-user applications. You download an app, it runs in a sandbox, and updates happen automatically. The user never thinks about infrastructure.
But AI agents aren't end-user applications. They're components in larger systems. They need to:
- Integrate with existing authentication systems
- Connect to private data sources
- Scale based on load
- Report metrics and logs
- Handle rate limits and quotas
- Work in air-gapped environments
When you treat agents like mobile apps, you're forcing developers to solve all these problems themselves. Every time. For every agent.
Here's what actually happens:
# Developer finds a promising agent on a marketplace
curl https://agent-marketplace.example/download/cool-agent
# Now what?
# - Where does it run?
# - How do I configure it?
# - How do I integrate it with my API gateway?
# - How do I monitor it?
# - How do I update it next month?
The marketplace solved 5% of the problem. The developer still has 95% of the work ahead of them.
ArcSphere's Bet: Publishing Over Discovery#
ArcSphere flips this model. We assume you already have an agent—whether you built it, found it on GitHub, or got it from Claude. Our focus is what happens next.
When you publish an agent to ArcSphere, you're not just uploading a file. You're creating a deployable artifact with:
- Versioning: Every publish creates a new immutable version. You can deploy v1.2.3, roll back to v1.2.2, or run both simultaneously.
- Dependency declaration: Your agent declares what it needs—APIs, models, compute resources. The deployment environment either satisfies those requirements or fails cleanly.
- Configuration management: Separate your code from your configuration. The same agent artifact runs in dev, staging, and production with different configs.
- Health checks: Define how the system knows your agent is working. Not "the process is running" but "the agent can actually do its job."
This isn't revolutionary technology. This is how we've deployed web services for the last decade. The radical part is applying these principles to AI agents.
The Technical Architecture That Makes This Work#
ArcSphere agents are packaged as standardized containers with a manifest file:
# agent.yaml
name: content-classifier
version: 1.2.3
runtime: python:3.11
dependencies:
models:
- openai/gpt-4
apis:
- name: content-api
required: true
resources:
memory: 512Mi
cpu: 0.5
endpoints:
classify: /classify
batch: /batch-classify
health:
path: /health
interval: 30s
timeout: 5s
This manifest tells the deployment system everything it needs to know. The agent developer declares requirements. The platform operator decides how to satisfy them.
Want to swap OpenAI for Claude? Change the model binding in the deployment config, not the agent code. Want to run this in a region without internet access? Deploy with a local model provider. Want to A/B test two versions? Route 10% of traffic to the new version.
These patterns are standard in modern infrastructure. They're conspicuously absent from AI agent platforms.
Why This Matters for Developers#
If you're building a product that uses AI agents, you need to ship code. Not demos, not prototypes—code that runs 24/7, handles errors gracefully, and doesn't wake you up at 3am.
The marketplace model optimizes for trying things quickly. The publishing model optimizes for running things reliably.
Here's a concrete example. Let's say you're building a customer support system with an AI agent that classifies tickets. With a marketplace approach:
- Find an agent that does classification
- Download it or hit its API endpoint
- Hope it stays online
- Hope the behavior doesn't change
- Hope you can handle the load
- Hope it doesn't break when they "improve" it
With a publishing approach:
- Publish the agent to your ArcSphere instance
- Deploy version 1.0.0 to production
- The agent runs in your infrastructure
- You control when to upgrade
- You scale it based on your needs
- You own the behavior
One model treats you like a consumer. The other treats you like an engineer.
The Pushback We Get#
The most common criticism of this approach is: "But what about the long tail? Marketplaces create discoverability for small creators."
This is true, but it's solving a different problem. If you want to browse AI agents for fun, marketplaces are great. If you want to ship a product, you need deployment infrastructure.
ArcSphere isn't anti-marketplace. We're pro-production. You can discover agents anywhere—GitHub, Hugging Face, agent directories, or build your own. Once you've decided to use one, ArcSphere gives you a path from "interesting demo" to "running in production."
The second criticism is about lock-in. "If I publish to ArcSphere, I'm locked into your platform."
This misunderstands the architecture. ArcSphere agents are containers with standard interfaces. You can run them anywhere that supports the container runtime. Publishing to ArcSphere creates a versioned, deployable artifact. You can take that artifact and deploy it to Kubernetes, Cloud Run, or your own servers.
The lock-in risk is lower than traditional marketplaces, where the agent might be running on the marketplace's infrastructure with no way to self-host.
Where This Goes Next#
The AI agent ecosystem is going through the same evolution that web services went through 15 years ago. Early on, everyone ran their own servers manually. Then we got Heroku and made deployment easy. Then we got Kubernetes and made operations scalable. Then we got service meshes and made complex systems manageable.
AI agents are at the "manually running servers" stage. Most platforms are trying to jump straight to "easy deployment" without solving the underlying infrastructure problems.
ArcSphere is betting that the path forward looks like:
- Standardized packaging (where we are now): Agents need a consistent format for declaring requirements and exposing capabilities.
- Deployment automation (coming soon): Going from "here's an agent" to "it's running in production" should be one command, not one day.
- Operational tooling (on the roadmap): Monitoring, logging, tracing, and debugging agents in production systems.
- Composition patterns (the future): Building systems where multiple agents work together reliably, with clear contracts and failure modes.
None of this is as exciting as "browse 10,000 AI agents in our marketplace." But it's what you actually need when you're trying to ship.
The Developer Experience We're Building Toward#
Imagine this workflow:
# You have an agent - maybe you built it, maybe you found it
cd my-agent
# Publish it
arcsphere publish
# Deploy it to production
arcsphere deploy production --version 1.2.3
# Check its status
arcsphere status production
# Roll back if needed
arcsphere rollback production
# View logs
arcsphere logs production --follow
This is table stakes for web services. It should be table stakes for AI agents too.
ArcSphere agents are discoverable—we have a directory and search. But discovery is a feature, not the foundation. The foundation is deployment, versioning, and operations.
Why This Matters Beyond ArcSphere#
Even if you never use ArcSphere, I hope this framing influences how you think about AI agents. The industry is making the same mistakes we made with web services 15 years ago:
- Treating deployment as an afterthought
- Conflating "runs on my laptop" with "ready for production"
- Ignoring operations until systems are already in production
- Optimizing for demos instead of reliability
We know how to solve these problems. We've solved them for web services, microservices, and data pipelines. We just need to apply those lessons to AI agents instead of pretending they're fundamentally different.
AI agents are code. Code needs to be versioned, deployed, monitored, and maintained. Any platform that doesn't start with this assumption is going to make your life harder, not easier.
The Path Forward#
The AI agent ecosystem doesn't need more marketplaces. It needs better infrastructure.
ArcSphere is our attempt to build that infrastructure. We're focused on the boring, essential problems: how do you publish an agent, deploy it reliably, update it safely, and debug it when things go wrong.
If you're building products with AI agents, these are the problems you'll face. You can solve them yourself, every time, for every agent. Or we can solve them once, together, and build better tools for everyone.
That's the bet we're making with ArcSphere. Not that we'll have the most agents, but that we'll make agents actually usable in production systems.
Because at the end of the day, the best agent marketplace is the one where the agents actually work.
ArcSphere is in active development. If you're building AI agents and want infrastructure that treats you like an engineer, not a consumer, check out arcblock.io, aigne.io and arcsphere.io or join our developer community.