Development Lifecycle
Free Salesforce Agentforce Specialist practice — 6 questions on Development Lifecycle, with explanations. No sign-up. Full 12-question mixed test →
A development team is building an Agentforce agent that integrates with multiple Salesforce orgs across different release cycles. The agent uses custom Apex actions that depend on a managed package. During UAT in a sandbox, the team discovers that API version mismatches are causing unexpected behavior when the agent calls deprecated methods. What is the BEST approach to ensure stable agent behavior acrossorg updates?
Pinning API versions in action metadata ensures consistent behavior regardless of org API updates. The package.xml dependency lock prevents automatic upgrades that could introduce breaking changes. This is the standard Salesforce practice for managing cross-org compatibility in managed solutions.
An Agentforce agent deployment failed in production with the error 'Agent Plan validation failed: Required data source not available.' The same agent deployed successfully to three staging sandboxes. The deployment uses a change set that includes the agent definition, all custom objects, and Apex classes. What is the MOST likely cause of this deployment failure?
The error specifically mentions 'data source not available' and agents often reference Data Cloud sources in their plans. Data Cloud configurations and connections are org-specific and not included in change sets or metadata deployments. This is a common production deployment gotcha because Data Cloud sources must be manually configured in each org.
A developer needs to test an Agentforce agent that uses Einstein Trust Layer to mask PII data before sending prompts to the LLM. The agent is configured with data masking policies for email addresses and phone numbers. In the Developer sandbox, test results show unmasked PII appearing in agent responses. Production org shows correct masking behavior. What should the developer verify FIRST?
Einstein Trust Layer is a feature that must be explicitly enabled per org and doesn't automatically deploy with agent metadata. Masking policies are configuration that must be separately deployed. This is the first troubleshooting step because the feature might not be active in the sandbox at all, which would explain the complete absence of masking.
A company is implementing CI/CD for Agentforce agents using Salesforce CLI and GitHub Actions. The pipeline deploys agents to integration, staging, and production orgs. After deployment, automated tests verify agent responses but frequently fail in staging with 'Agent response timeout' errors despite identical configuration across all orgs. The staging org has 10x more data volume than integration. Which optimization will MOST effectively resolve the staging test failures?
Agent response timeouts with high data volumes indicate the grounding service is processing too much data to retrieve relevant context. Indexed fields and selectivity filters directly address the root cause by making data retrieval more efficient. This is the performance optimization pattern Salesforce recommends for agents in data-heavy orgs.
An Agentforce agent uses a custom Apex action that queries external REST APIs. During load testing, the action intermittently fails with 'Apex CPU time limit exceeded' in production but never in sandbox environments. The action has @future annotation and processes up to 200 API responses per invocation. Sandbox testing showed consistent 8-second execution times. What is the correct solution?
Queueable provides higher governor limits than @future (60 seconds CPU time vs 60 seconds for synchronous, but better monitoring and chaining). Chunking 200 responses into 50-per-job batches directly addresses the CPU limit issue. This is the standard pattern for processing large volumes in Apex while staying within governor limits.
A development team maintains separate Git branches for three Agentforce agents that share common Apex actions and prompt templates. When merging feature branches, conflicts frequently occur in the agent metadata XML files even when developers worked on different agents. The team uses source format and deploys with SFDX. What is the BEST practice to minimize merge conflicts while maintaining proper version control?
SFDX source format decomposes metadata into granular files (separate files per component), which significantly reduces merge conflicts compared to monolithic XML. Branch-per-agent with shared action library allows parallel development while isolating agent-specific changes. This is the recommended Salesforce DX pattern for multi-component projects.
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