Build & scale AI \ agents as microservices with IAM
Open-source control plane for production-grade autonomous software. It unifies a Kubernetes-style Agent Execution and Scaling Fabric with an in-built Zero-Trust Identity and Auth Layer. This provides the complete backend system to deploy agents as distributed microservices and enforce trust at every step with cryptographic identity, authorization policies, and verifiable audit trails.
If you've ever said "I'm just going to call the OpenAI API directly", this is for you.
We get it. You ditched LangChain because you were tired of fighting 5 layers of abstraction to change one thing. You don't need a framework to write f-strings.
Six months ago, we asked ourselves: What would agent infrastructure look like if we designed it from scratch with production in mind?
Not another agent framework. Not another LLM wrapper. Real infrastructure.
When your agents are just Python/TS scripts calling APIs, how do you: - Know which agent can talk to what? - Stop one agent from accessing another's data? - Debug when an agent chain fails at 3am? - Scale from 1 agent to 10k?
Agentfield gives you the infrastructure without the framework.
Think of it as: • The scaling fabric for agents as microservices (K8s-style, not DIY) • The identity layer your agents are missing (cryptographic, not "just trust me") • The authorization your API calls need (policy enforcement, not guidelines)
We're not telling you HOW to build agents. Build them however you want. We just make them production-ready.
Open-source, Apache 2.0 licensed.
For the "just use the API" crowd: What's your current production setup look like?
Here all day! 🛠
About Agentfield on Product Hunt
“Build & scale AI \ agents as microservices with IAM”
Agentfield launched on Product Hunt on December 11th, 2025 and earned 91 upvotes and 8 comments, placing #21 on the daily leaderboard. Open-source control plane for production-grade autonomous software. It unifies a Kubernetes-style Agent Execution and Scaling Fabric with an in-built Zero-Trust Identity and Auth Layer. This provides the complete backend system to deploy agents as distributed microservices and enforce trust at every step with cryptographic identity, authorization policies, and verifiable audit trails.
On the analytics side, Agentfield competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Agentfield performed against the three products that launched closest to it on the same day.
Who hunted Agentfield?
Agentfield was hunted by Santosh Radha. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Agentfield including community comment highlights and product details, visit the product overview.
Santosh Radha
Hey PH! 👋
If you've ever said "I'm just going to call the OpenAI API directly", this is for you.
We get it. You ditched LangChain because you were tired of fighting 5 layers of abstraction to change one thing. You don't need a framework to write f-strings.
Six months ago, we asked ourselves: What would agent infrastructure look like if we designed it from scratch with production in mind?
Not another agent framework. Not another LLM wrapper. Real infrastructure.
When your agents are just Python/TS scripts calling APIs, how do you:
- Know which agent can talk to what?
- Stop one agent from accessing another's data?
- Debug when an agent chain fails at 3am?
- Scale from 1 agent to 10k?
Agentfield gives you the infrastructure without the framework.
Think of it as:
• The scaling fabric for agents as microservices (K8s-style, not DIY)
• The identity layer your agents are missing (cryptographic, not "just trust me")
• The authorization your API calls need (policy enforcement, not guidelines)
We're not telling you HOW to build agents. Build them however you want. We just make them production-ready.
Open-source, Apache 2.0 licensed.
For the "just use the API" crowd: What's your current production setup look like?
Here all day! 🛠