The Agent Lifecycle: Ideate, Build, Govern

Deploying agents at scale requires a proven, repeatable approach with a strong level of governance. This includes version control of agent metadata and underlying action metadata, setting review cycles based on risk, and continuously monitoring agent performance. With the foundations in place, organizations can confidently navigate the three key phases of the AI agent lifecycle.

Phase I: Ideate

The initial phase is all about understanding, scoping, architecting, and designing AI agents that can deliver the most significant impact. This isn’t just about automating existing tasks; it’s about reimagining entire business processes.

• 1.1 Brainstorm Agent Ideas: Think beyond current limitations to envision an exceptional user experience for customers, partners, and employees. Consider how agents can empower even the most junior team member with the “smarts” of your best and brightest employee, or how they can coach your teams. This could involve internal applications like answering HR policy questions based on employee location or providing sales development representative (SDR) support for an account executive, or customer-facing roles such as handling product returns, exchanges, or warranty claims. AI can also analyze customer interaction data to proactively identify issues and suggest personalized solutions.
• 1.2 Configuration Mining Draws Operational Business Processes: Rapidly get an accurate perspective of the operational processes based on the configuration of the core systems. Use this as a basis to engage stakeholders who can add their expertise and perspective to build up a robust picture of the operations.     
• 1.3 AI Generates Process Diagrams: Alternatively, use AI to accelerate the capture of business processes that are locked up in users’ memories. Get them to talk through,  sketch out, find old documentation, or write down how the business operates. Use that as input so that AI can generate the first draft. Then collaborate to refine the diagrams.
• 1.2 AgentFinder Analyzing Operational Business Processes: Leverage tools like Elements.cloud’s Agent Finder to analyze any documented processes and automatically identify potential use cases for AI agents. This provides reasoning and confidence levels for its recommendations
• 1.3 Evaluate and Categorize Agent Ideas: Use AI to assess the brainstormed ideas and categorize them based on their nature: pure automation, AI-powered workflows, or conversational AI. This helps prioritize and select the easiest and simplest use cases for initial implementation, which builds confidence and momentum.
• 1.4 Architect Agent: Agent vs. Topic (JTBD): Define the scope of your agent. An “Agent” acts as a domain (e.g., “Customer Support” or “Employee Support”). Agents should initially be narrower in scope to facilitate experimentation, faster deployment, and simpler data governance. Within an Agent, “Topics” represent specific “Jobs To Be Done” (JTBD), such as “Answer a product query” or “Arrange the return of a purchased product”. Documenting this structure in an Agent Interaction Map (AIM) is key to planning and collaboration with stakeholders.
• 1.5 Process-Led Design – AID (Agent Instruction Diagram): Use a process-led approach to design the agent’s behavior. An Agent Instruction Diagram (AID), provides a visual way of understanding the scope of the JTBD. This collaborative diagram, which can be AI-created, helps clarify the steps the agent will take, identify handoffs to human intervention, and define guardrails. This explicit detail is crucial since AI agents lack human common sense and context.
• 1.6 Agents Review/Validate/Check: Utilize AI to review, validate, and check the design. This can include running consistency checks to ensure standards are followed and no conflicts or gaps are introduced, and even suggesting process streamlining.
• 1.7 Generate User Stories for Actions: From the detailed process design, generate user stories for the metadata (Flow, Apex, Prompt Template) that powers the “Actions” the agent uses to achieve its goal.
• 1.8 Generate Instructions and Test Utterances: AI can help write the specific instructions and guardrails for the agent, often consolidating them into a single instruction for more consistent results. Additionally, AI can generate test utterances for each process path, considering various perspectives that human testers might overlook, significantly saving time.

Phase II: Build

This phase focuses on the technical implementation and initial testing of the AI agent, moving from design to a deployed agent:

• 2.1 Create Agent: Set up the agent within the Agent Builder environment.
• 2.2 Create Topics: Define the specific Topic (JTBD) within the Agent, adding the scope and classification descriptions as defined in the Ideate phase.
• 2.3 Create metadata used by Actions: Build or modify the underlying metadata (Flows, Apex, Prompt Templates) that the agent’s actions will call. User stories generated previously guide this development. Existing metadata should be reused where possible, but clearly documented and understood to ensure predictable agent behavior.
• 2.4 Test Agent: Conduct thorough testing of the agent. This involves using the AI-generated test utterances and evaluating how the agent responds. Salesforce’s Test Center can automate general utterances, while AI-generated ones provide an end-to-end process-specific perspective.
• 2.5 Improve Agent: Based on testing feedback, iteratively refine the agent. This involves updating the AID to reflect necessary changes, regenerating instructions, and then re-testing. This iterative loop is crucial for optimizing agent behavior, but it also provides an audit trail and change log.
• 2.6 Deploy: Metadata DevOps, Sandbox to Production: Once testing is satisfactory, deploy the agent and its related metadata (Topics, Actions, Flows, Apex, etc.) from a sandbox to the production environment, following standard Salesforce DevOps practices. At the same time, version and sign off on the AID so that you have built-in governance.
• 2.7 Go-Live: The agent is now live and available for users.

Phase III: Govern

The final phase ensures the long-term effectiveness, reliability, and security of your AI agents. This is a continuous cycle of monitoring, evaluation, and improvement.

• 3.1 Observability of Agents: Implement robust monitoring to track the agent’s performance. Every conversation and action is logged in Data Cloud, providing granular data. This observability helps in understanding effectiveness, identifying areas for improvement, and evaluating ROI.
• 3.2 Setting Review Cycles for Agents Based on Risk: Establish regular review cycles for agents, with the frequency and depth of review determined by the agent’s impact and associated risks. This ensures ongoing compliance and performance optimization. Agents may need more oversight and governance than humans due to their lack of organizational context.
• 3.3 Version Control of Agent Metadata and Action Metadata: Maintain strict version control for all agent-related metadata, including the AID, Topics, Instructions, and underlying Actions (Flows, Apex). This historical record is vital for troubleshooting, understanding past behaviors, and ensuring accountability.
• 3.4 Metadata Dictionary and Dependencies: Utilize a comprehensive metadata dictionary to understand the relationships and dependencies between different metadata items, including the Agentforce and Data Cloud metadata. This is crucial because changing one Flow or Apex component could unintentionally impact an agent’s behavior, especially if it’s reused across multiple Topics.
• 3.5 Impact Analysis of Changes: Before making any changes to agent or action metadata, conduct a thorough impact analysis. This leverages the metadata dictionary and dependency mapping to predict how a change might affect agent behavior, preventing unintended consequences and costly rework.

Final Word

By embracing these comprehensive phases and establishing the necessary foundations, organizations can strategically implement AI agents, moving beyond simple automation to truly transform their business operations. The future winners are already piloting, experimenting, and learning, accelerating ahead of those who remain on the sidelines due to concerns about compliance, data quality, or risk.