What is an embedded agent?
An embedded agent is a technical module that structures the connectivity of embedded and IoT systems. It interacts with system applications to guarantee not only connectivity, but also security, updates, and system monitoring.
Embedded agent vs. integrated module: two distinct architectures
Embedded connectivity can be approached through two paradigms:
- Integrated module: Directly embedded within the business application, it handles connectivity as just another feature, often with minimal isolation. This model is common in microcontroller-based systems, where resources are limited and integration is tight.
- Embedded agent: As described earlier, this is a standalone application dedicated to connectivity. It interacts with business applications via well-defined interfaces. This model promotes modularity, separation of concerns, and robustness.
The choice between these models depends on system complexity, security requirements, maintainability, and scalability. Let’s see when it may be beneficial to go for an embedded agent rather than an integrated module.
Why choose an embedded agent?
Embedded agents offer several key advantages.
1. Connection stability
Connectivity isn’t just about linking to the cloud; it must be resilient. In case of network loss (e.g., 5G outage), the agent should manage reconnection, maintain sessions, and ensure continuity. This is vital for mobile or unstable environments.
2. Secure provisioning
Securing the connection involves a provisioning process using certificates, keys, or passwords. The agent encapsulates this complexity, offering a simplified interface to business applications that don’t need to handle these mechanisms directly.
3. System status reporting
The agent can report technical data such as:
- System version
- CPU load
- RAM usage
These optional metrics are valuable for diagnostics, monitoring, and cybersecurity, helping detect anomalies like memory leaks or abnormal behavior.
4. Isolation and cybersecurity
As the first point of contact with the cloud, the agent must be isolated from the rest of the system, often via containerization. This prevents a compromised agent from jeopardizing the entire embedded system.
5. Simpler application development
The agent acts as an abstraction layer. Embedded software developers can focus on their core features without worrying about connectivity details. They send data, and the agent ensures secure, reliable transmission.
6. Update management
The agent can handle the full update workflow:
- Detect current version
- Conditional download
- Validation
- Installation
- Status reporting
This process is critical for long-term security and performance of connected devices. We’ll dive deeper into this topic later in this article.
Device < > cloud communication modes supported by embedded agents
An embedded agent should support three types of communication:
1. Telemetry
- Regular, historical data (e.g., temperature, available RAM)
- Used for monitoring, analytics, AI
- Stored hot or cold depending on needs
2. Configuration
- Bidirectional communication (device shadow)
- Allows setting target values and confirming device compliance
- Example: set a temperature threshold, receive confirmation
3. Command (RPC)
- Synchronous communication
- Client sends a command, device responds immediately
- Example: turn on a light, start SSH service
These three communication modes are complementary and must be integrated into the agent to cover all business and technical use cases.
Focus: update management
Software updates are one of the most emblematic use cases demonstrating the value of an embedded agent.
They involve all the functional blocks discussed: connectivity, security, configuration, telemetry, and bidirectional communication. This critical process deserves special attention from the design phase.
A complete and structured workflow
Software updates go far beyond simple file downloads. They require a structured workflow with interdependent steps:
- Current version detection
The agent identifies the current firmware, application, or module version and reports it to the cloud via telemetry or configuration. - Target version definition
The central platform specifies the desired version, transmitted to the agent via configuration—often using a device shadow or digital twin model. - Conditional download
Before downloading, the agent checks whether the update is accepted by the system. No need to consume bandwidth or resources if the device rejects the update. This step optimizes flows and prevents errors. - Controlled installation
Once downloaded, the agent notifies the embedded system to enter installation mode. Success or failure must be tracked and reported to the cloud. - Status reporting to the cloud
At each workflow step, the agent updates the central platform: download in progress, installation success, failure, possible rollback. This enables fleet-wide supervision and proactive issue management.
Implementation based on robust connectivity
This process relies on secure, stable connectivity. The agent must maintain a session even during network outages or device mobility. It must handle reconnections, interruptions, and session recovery without data loss.
Security is central. Initial provisioning using certificates or keys ensures only authorized updates are applied. The agent safeguards the trust chain by verifying file integrity, validating signatures, and isolating critical processes.
A valuable abstraction for business app development
For embedded engineers, updates are often complex and risky. The embedded agent offloads this complexity to a specialized, standardized, and tested layer. Developers no longer manage download details, validation, or failure recovery; they simply specify a target version and let the agent orchestrate the process.
How to develop an embedded agent? 3 best practices
Designing an embedded agent goes beyond writing code to connect to the cloud. It involves technological, architectural, and methodological choices that impact long-term robustness, security, and maintainability.
Technology choices
Technology selection should be guided by:
- Language fit for environment
Choose a programming language suited to the target system.
For example, on a Linux platform with a Python-savvy team, Python is a valid choice—even if C or Rust are traditionally used in embedded systems. The goal is to maximize productivity while respecting technical constraints. - Internal skill alignment
Don’t choose tech based solely on market trends or theoretical performance. It must reflect the team’s actual skills. Poorly mastered tech can slow progress, introduce bugs, and complicate maintenance. - Right cloud tools and architectures
The target cloud (AWS, Azure, GCP, etc.) heavily influences agent-platform interaction. Anticipate provisioning mechanisms, supported protocols, communication models (device shadow, RPC), and security constraints. These choices shape the architecture for years to come.
These decisions must be made carefully. These platforms aren’t meant for temporary deployment, they’re built to last at least for 10 years, especially in industrial or agricultural sectors.
Quality and testing
Agent development quality relies on rigorous testing and validation:
- Follow common development best practices
Implement code reviews, unit tests, and continuous integration. These help catch errors early and ensure system stability. - Set up a connectivity test bench
Test scenarios like disconnection, reconnection, session recovery, and data continuity.
For example, if a vehicle loses connection while moving, the agent must handle it without data loss or state corruption. - Continuous system monitoring
Testing shouldn’t stop at development. Integrate it into the product lifecycle with embedded monitoring and diagnostics tools. This helps detect anomalies in production and anticipate failures.
Connectivity might seem like a small module, but it actually has a huge impact on your project. So, neglecting this layer can weaken your entire system.
Distinguish business and technical data
Another best practice is to clearly distinguish data types handled by the agent:
- Business data
Information directly related to device function (e.g., measured temperature, mowing height, energy production). This data will be used by business apps for control, analysis, or optimization. - Technical data
Information about the embedded system’s state (e.g., OS version, CPU load, available memory). This data is useful for monitoring, telemetry, anomaly detection, or update planning.
Though both data types often use the same channels (e.g., MQTT, HTTPS), their usage and management interfaces differ. Separating them in the software architecture improves data flow organization, readability, and security.
An embedded agent isn’t just a technical module, it’s a strategic building block that structures connectivity, security, updates, and monitoring in embedded systems.
Because its implementation is not always straightforward, we’ve built a ready-to-use connectivity agent specifically for embedded Linux systems. Just reach out to know more.
This article has been written by Pierre Gal, Head of Software development at The Embedded Kit.
