Agentic experience design is the practice of mapping how an AI agent will interact with your CRM data, your existing workflows, and your sales team's behavior — before any code is written or any configuration is deployed.
Most mid-market SaaS teams skip this step. They move directly from 'we should deploy an AI agent for lead routing' to building the agent. The gap between those two steps is where most agentic AI projects produce expensive, visible failures.
Why Prototyping Agentic Workflows Saves More Time Than It Costs
A prototype of an agentic workflow does not require any AI tooling. It requires:
- A clear definition of what decision the agent will make
- A map of which Salesforce fields the agent will read to make that decision
- A test of whether those fields are currently populated with accurate, consistent data
- A simulation of what the agent will do when fields are missing, inconsistent, or outside expected ranges
Running this exercise manually — before any build — typically surfaces three to five data or process gaps that would have caused the agent to produce incorrect outputs in production. Identifying those gaps in a prototype phase costs hours. Identifying them after deployment costs adoption credibility and pipeline.
The Five Prototype Questions for Any Agentic Salesforce Workflow
1. Which Salesforce object and which specific fields will the agent read?
Be precise. Not 'lead data' but 'Lead Status, Lead Source, Annual Revenue (custom), and Phone.' Query those exact fields across 100 recent records. What percentage are populated? What percentage are consistent with your expected format?
2. What decision or action will the agent take, and what is the decision logic?
Map the logic explicitly: if Lead Source = Paid Search AND Annual Revenue > 10M AND Lead Status = MQL, then assign to Territory A queue. Test this logic against your actual data distribution. How many records meet all three criteria? How many meet two out of three? What happens in edge cases?
3. What happens when the agent encounters missing or invalid input data?
Define the fallback explicitly before build. If Annual Revenue is blank, does the agent route to a default queue, hold the lead for manual review, or fire an alert to RevOps? The fallback logic determines whether the agent produces clean exceptions or silent failures.
4. How will the agent's decision be visible to the sales team?
If the agent routes a lead but the rep can't see why — which field values triggered the assignment — adoption fails. Design the visibility layer before the agent: a custom field that logs the routing reason, a Chatter notification, or a task description that explains the assignment rationale.
5. What baseline will you measure the agent's performance against?
Define the metric the agent is meant to improve — speed-to-lead, stage conversion rate, forecast accuracy — and measure its current value before go-live. You cannot evaluate an agent without a baseline.
These five questions can be answered in a two-day working session with your RevOps team and your Salesforce partner. If your current Salesforce org doesn't surface the data quality information needed to answer them, a Revenue Leak Audit is the right starting point.
Prototype before you build — and audit before you prototype.
TeraQuint helps mid-market SaaS teams design agentic Salesforce workflows that survive first contact with real production data.
Design Your Agentic WorkflowSudhanshu Gupta | Former Salesforce Technical Consultant | TeraQuint INC