Salesforce Agentforce and DataCloud are a hugely exciting step forward for any organization looking to enhance their customer interactions and operational efficiency. With the ability to create countless LLM-based agents that can tackle anything from customer queries to automated task execution, they’re among some of the most powerful tools at your disposal for simplifying complexity and driving business success.
But without rigorous testing, Salesforce’s AI agents can hit a number of hurdles once in deployment—regardless of how effective they first appear during the Build stage.
hat’s where the Agentforce Testing Center (AFTC) comes in. With features like synthetic interaction generation, plan tracing, and utterance analysis, AFTC lays the groundwork for testing autonomous AI agents at scale. It enables you to run hundreds of test scenarios in real-time, giving you a window into what you need to improve accuracy and squash bugs without disrupting the live production environment.
However, as powerful as AFTC is, it can still fall short of being a comprehensive solution for managing the development lifecycle of Agentforce agents.
To overcome these challenges, a complete end-to-end DevOps and testing framework is necessary to plan, build, test, and release fully-fledged Agentforce agents. With Copado, businesses can go beyond AFTC’s foundational capabilities to orchestrate their Agentforce implementations at scale—while ensuring compliance, quality, and continuous innovation.
At its launch in November 2024, Salesforce first billed Agentforce Testing Center as a means for software teams to test, deploy and monitor AI agents at scale. In many ways, it is certainly that. It provides a range of much-needed tooling for agent testing, including:
These features are critical for testing Agentforce Agents within sandboxes and ensuring that Agents perform as intended prior to deployment.
However, Agents are complex systems, and once operational, can face a large amount of real-time unpredictability and variation. They also rely on custom metadata such as Apex classes, Flows, and Lightning Web Components (LWCs) – each of which can cause complications amid real-time interactions at scale. Each of these components requires automated testing, versioning, and governance.
Although powerful, AFTC lacks metadata-aware testing tools, as well as end-to-end system testing to validate how agents cope with customer data in Salesforce Data Cloud. Tackling these obstacles is absolutely critical to ensure reliable, compliant, and scalable Agentforce deployments.
To successfully release a complete Agentforce Agent, organizations need more than just agent-specific testing tools. They need an end-to-end DevOps approach which ensures enterprise-grade reliability and speed in Agentforce.
However, traditional DevOps approaches often struggle to handle the unique challenges of AI agent development and testing. The sheer number of complex dependencies and configuration requirements, coupled with an overreliance on manual processes, means teams can face mounting challenges to scale AI implementations.
That’s why a DevOps orchestration platform is vital to ensuring that every component is rigorously validated, versioned, and deployed with precision. This includes everything from metadata and AI models to governance and compliance frameworks to ensure a seamless and reliable deployment.
As the industry’s first comprehensive DevOps solution for Agentforce, Copado can eliminate these challenges with automated deployments, configuration steps, and AI-powered testing.
Here’s just some of the ways Copado complements and extends AFTC’s capabilities:
By eliminating manual overhead and ensuring reliable, consistent agent delivery, Copado empowers teams to focus on building innovative solutions and make release days obsolete.
Thanks to features such as Plan Trace, auto-generated synthetic interactions, and AI-specific monitoring, it’s true that AFTC excels at AI-specific testing. It’s fantastic at validating an agent’s response accuracy and real-time performance in controlled environments.
However, Copado expands this scope to ensure that all aspects of your Agentforce Agent are validated for enterprise-grade reliability.
By testing the configuration of the Agent in real-world scenarios in a chat interface, you’re able to achieve comprehensive end-to-end testing that ensures all applications, integrations and processes work exactly as expected.
This not only enables you to greatly reduce errors and testing times, but also accelerate your Agent deployments—ensuring your agents perform consistently across every environment, every time.
Building and deploying Agentforce Agents is a challenge in itself. But you have to keep in mind that agents are long-term assets within your Salesforce ecosystem. They require continuous monitoring, updates, and testing to remain effective.
Firstly, Agents are based on LLMs, meaning they continuously evolve and learn from interactions with customers over time. Then, Salesforce is always changing, and so is your organisation. That means updates to LWCs or Flows could hinder an operational Agent’s functionality. That’s before any tweaks are made to regulations or your own policies.
All of these factors mean Agentforce Agents are, by their very nature, dynamic entities—and generate constant feedback loops with which you must contend.
Copado’s DevOps and testing solutions support these efforts by enabling:
By aligning every component of the development process, and ensuring your agents are secure, compliant, and scalable, your team can be empowered to achieve faster high-quality releases with minimal risk.
Salesforce Agentforce Testing Center provides powerful new tools to test autonomous AI agents, but it’s not the entire solution. To deliver fully operational Agentforce Agents, especially for small and medium teams, businesses need a comprehensive DevOps and testing framework like Copado. By combining the foundational capabilities of AFTC with Copado’s end-to-end orchestration, businesses can unlock the full potential of Agentforce in their Salesforce environments.
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