---
title: "The \"Open Core and Managed Edges\" Strategy"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# The "Open Core and Managed Edges" Strategy

This strategy is increasingly popular, particularly for cloud-native applications. It describes an architecture where:

1.  **Open Core:** The fundamental, differentiating business logic and the core application runtime/platform are built using **open-source technologies**. This gives you transparency, flexibility, control over the core development, avoids vendor lock-in for the heart of your system, and allows you to leverage community innovation.
2.  **Managed Edges:** The system relies on **external, often proprietary or SaaS (Software as a Service), managed services, serverless offerings** for specific infrastructure capabilities. These "edges" are the components your core system interacts with for things like data storage, messaging, caching, search, AI models, etc. The key here is that these services are *managed* by a third party, abstracting away the operational complexity (hosting, scaling, patching, backups, high availability) of that specific piece of infrastructure.

In essence, you combine the power and flexibility of open source for your core value proposition with the convenience, scalability, and reduced operational burden of specialized, managed services for common infrastructure needs.

### Applying the Strategy to Your Agentic AI System

Let's map the technologies and your examples onto this strategy:

**The Open Core:**

* **Your Agentic AI Logic:** This is the most critical part of your open core. The unique decision-making processes, learning algorithms, interaction patterns, and internal state management logic of your AI agents reside here. This is your core intellectual property or innovative approach.
* **Dapr (Actors, Workflows, Building Blocks):** Dapr itself is an **open-source project**. It runs alongside your agent code (the sidecar) and provides the open APIs (Actors, Workflows, Pub/Sub, State, Bindings, etc.) that your agents use. Dapr *is* a core piece of your runtime *platform*, providing the essential framework for building distributed, stateful agents and orchestrating their activities in an open, portable manner.
* **Kubernetes:** While managed Kubernetes services exist, Kubernetes itself is **open-source**. It provides the open platform for deploying and managing your containerized agent code and Dapr sidecars. It's the open foundation your core runtime sits on.
* **Helm:** An **open-source** packaging tool. It defines how your open core components (agent deployments, Dapr configurations) are structured and configured for deployment.
* **Argo CD:** An **open-source** GitOps CD tool. It provides the open, transparent process for deploying and managing your open core (defined by Helm Charts) onto the open platform (Kubernetes) directly from Git.
* **Open-Source Observability Tools (Prometheus, Grafana, OpenTelemetry, Loki, Jaeger/Zipkin):** While cloud providers have managed versions, the core projects are open source. They provide the essential, open tooling to gain visibility *into your open core* and its interactions.

**The Managed Edges:**

* **CockroachDB Serverless:** This is a **Managed Service**. It provides a highly scalable, resilient, distributed SQL database *managed* by Cockroach Labs. Your Dapr State Management building block would be configured to use a CockroachDB component that connects to this managed service endpoint. Your agentic actors' state is stored here, but you don't manage the database clusters yourself.
* **Upstash Redis:** This is another **Managed Service** (SaaS). It provides a serverless, managed Redis instance. Your Dapr State Management or Caching building block could be configured to use an Upstash Redis component. This offers low-latency data access without you needing to operate Redis instances.
* **RabbitMQ/Kafka (Managed Service):** You would likely use a managed RabbitMQ service (e.g., CloudAMQP, or a cloud provider's offering). This is a **Managed Service** providing robust message queuing. Your Dapr Publish/Subscribe building block would be configured to use a RabbitMQ/Kafka component pointing to this managed broker endpoint.
* **LLM APIs (e.g., OpenAI API):** This is a classic example of a **Managed Edge Service** (SaaS API). It provides access to powerful AI models managed entirely by the provider (OpenAI). Your agent logic or a Dapr Binding would make API calls to this service to incorporate large language model capabilities. You don't need to deploy or manage the complex LLM inference infrastructure.

### Dapr's Crucial Role as the Bridge:

Dapr is the key enabler of this strategy in your stack. Your agent code (the Open Core) doesn't talk directly to the specific APIs of CockroachDB, Upstash Redis, Kafka, or OpenAI. Instead, your agents use the standardized Dapr building block APIs. Dapr, running as a sidecar, then translates these standard calls into the specific API calls required by the configured Managed Edge service (e.g., a Dapr State API call becomes a specific query to CockroachDB Serverless or a command to Upstash Redis based on Dapr's configuration).   

This decoupling is fundamental to the "Cloud Anywhere" aspect. You can change the configuration of Dapr components (telling Dapr to use a different managed service for state or pub/sub) via your Helm values files in Git, and Argo CD will deploy that change, without needing to modify the core business logic code of your AI agents.   


### How This Strategy Helps Build State-of-the-Art Agentic AI Systems

Combining this strategy with the discussed technologies provides significant advantages for your agentic AI system:

1.  **Focus on Core Innovation:** By using managed services for common infrastructure like databases, caches, and message queues, and consuming advanced AI capabilities via managed APIs (LLMs), your team can **dedicate most of its time and expertise to building the unique, intelligent logic within your agentic actors** – the true differentiator of your AI system. You're not spending critical engineering cycles managing databases or message brokers.
2.  **Leverage Best-of-Breed Services:** Managed edges allow you to easily integrate highly specialized and optimized services for specific tasks (e.g., a globally distributed database like CockroachDB, a low-latency cache like Upstash Redis, cutting-edge LLMs via API) without the operational overhead of running them yourself. This enables your agents to utilize state-of-the-art underlying infrastructure.
3.  **Dapr as the Universal Connector:** Dapr is the key enabler that bridges your open core (agent code) with these diverse managed edges. Your agents talk to Dapr's standard APIs, and Dapr handles the communication with the specific CockroachDB, Upstash, RabbitMQ, or even LLM API endpoint based on configuration. This **avoids coupling your precious agent logic to the specific SDKs or protocols of multiple managed services**, making your core agents much more portable and maintainable.
4.  **Scalability and Resilience (Hybrid):** The open core (agents on K8s with Dapr) can scale horizontally. The managed edges are designed by specialists to be highly scalable and resilient. CockroachDB Serverless and Upstash Redis are built for scale, RabbitMQ handles message throughput, and LLM APIs are designed to handle massive request volumes. This hybrid scalability model means your *entire system* can scale effectively.
5.  **Operational Efficiency:** You manage the Kubernetes cluster and the deployment of your agents and Dapr configuration (using Helm/Argo CD), but the operational burden of managing the underlying managed services is offloaded. This reduces the size and specialization needed in your operations team.
6.  **Consistent Deployment and Management of the Core:** Even though the edges are managed externally, the process of deploying and updating your agents and their Dapr configurations remains standardized via Helm and automated via Argo CD's GitOps flow, providing operational consistency for the core system.
7.  **Improved Observability:** Using open-source observability tools like Prometheus and Grafana, enhanced by Dapr's native integrations, allows you to gain consistent visibility into the performance and behavior of your open core agents and how they interact with the managed edges, regardless of which specific managed service is used.

The "Open Core and Managed Edges" strategy, powered by Kubernetes as the portable compute layer, Dapr as the application-level abstraction, Helm and Argo CD for automated deployment, and standard observability, creates an environment where you can maximize focus on building sophisticated AI agent logic (your Open Core) while relying on powerful, scalable, and operationally simpler managed services (your Edges) for foundational capabilities. This approach is highly conducive to developing and deploying state-of-the-art agentic AI systems efficiently and at scale across a "Cloud Anywhere" landscape.

In conclusion, the "Open Core and Managed Edges" strategy, powered by technologies like Kubernetes (platform), Dapr (application runtime/abstraction), Helm (packaging), Argo CD (deployment), and open-source Observability tools, provides a powerful blueprint for building state-of-the-art agentic AI systems. It allows you to focus your unique AI expertise on the flexible, open core while leveraging the scalability, reliability, and reduced operational cost of best-of-breed managed services for infrastructure needs, connected seamlessly via Dapr's abstraction layer. This hybrid approach balances control and customization for your core innovation with practicality and efficiency for supporting services.
