Behavioral Mining & Auto-Doc Gen for Gusto's Automation Roadmap
FullStory Strategic Solutions Team for Gusto · April 2026 Workforce Onsite
A sovereign architecture for closing the loop between specialist behavior and automation logic — leveraging Gusto's existing internal MCP and the Silver Schema sitting idle in Snowflake today.
Specialists bounce between Hippo, Panda, and the Wiki. Context switching is the primary source of inflated AHT in Carrier Submissions.
The 43-day variance in State Data Exchanges isn't random. It's a workflow pattern — and it's already captured in Fullstory's behavioral data. We don't need to go find it. We need to query it.
Gusto has already built an internal MCP — that's the foundation we're building on, not replacing. github.com/fullstorydev/lexicon is the reference MCP architecture: an open pattern that teaches developers how to expose Fullstory APIs as MCP tools. Separately, Fullstory's upcoming Analytics MCP will enable direct querying of analytic data living in Fullstory today — a complementary capability, not the same thing.
getSessionInsights, summarizeFriction — that Gusto's agents call when they need behavioral context.A library of AI Agent Skills — Markdown skill files loaded into AI coding assistants (Cursor, Claude Code, etc.) that make them experts in Fullstory implementation and semantic DOM decoration. When your developers use these skills, every interactive element in the app gets machine-readable semantic attributes.
data-component, data-id, data-section to every interactive element — web and mobile.FS.identify, custom events, page properties with proper schema discipline.Lexicon is a reference MCP architecture — an open pattern that teaches developers how to expose Fullstory's behavioral APIs as MCP tool definitions. Not a standalone server. A blueprint you implement in your own stack, connecting Gusto's existing MCP to Fullstory's behavioral data layer.
{
"name": "getSessionSummary",
"description": "Summarize a Fullstory session via AI profile",
"parameters": {
"session_id": "string",
"profile_id": "string"
}
}
The Silver Schema is the prerequisite — a low-lift ETL that lands Fullstory's behavioral events into Gusto's Snowflake environment. Once connected, 6 years of behavioral history is queryable at scale. Cortex is one path; Gusto's existing Cube semantic layer is another.
-- Ask Cortex why specialists are detouring to GA wiki SELECT SNOWFLAKE.CORTEX.COMPLETE( 'mixtral-8x7b', 'Analyze this session cohort. Why are specialists navigating to the GA SUI wiki 4x more often than the VT wiki? Identify the trigger pages.', { 'context_data': ( SELECT page_sequence, session_duration, carrier_id, state_code FROM fullstory.silver.sessions WHERE carrier_id = 'GA-SUI' AND session_date >= DATEADD('day', -30, CURRENT_DATE) ) } );
A design pattern — not a pre-built tool — for how Gusto could implement batch behavioral auditing against the Silver Schema without requiring a live agent session for every query.
# Run a waste audit for GA Carrier Submissions, last 30d python audit.py \ --carrier "GA-SUI" \ --date-range "2026-03-01:2026-03-31" \ --profile "waste-indicators-v2" \ --output ./reports/GA-SUI-march.md # Output structure: # ─ session_count: 1,847 # ─ waste_rate: 38.4% # ─ top_friction: "Hippo → Wiki redirect at Step 4" # ─ gumloop_candidates: 712 sessions
## GA-SUI Carrier Submissions — March 2026 ### Waste Indicators Detected 1. Specialists navigated to wiki at Panda step 4 in 38% of sessions — median +14m to session time. 2. Field error loop (EIN validation) triggered fallback in 22% of sessions. No automation present. 3. Context switches per session: avg 4.2 (target: 1)
Two roads to the same destination. The behavioral intelligence Gusto needs already exists — the only question is whether you build the wrapper or adopt the pattern.
The batch audit is the foundation. Real-time is the destination. As Gusto's MCP matures with FullStory tool integration, the same behavioral intelligence that powers nightly reports can power live agent decisions.
discoverGroups and automatically updates Gusto's internal automation queues — no human triage required.
The logic you build today is a permanent asset. Because the Silver Schema is the shared source of truth between Gusto's internal MCP and FullStory's Enterprise MCP, the patterns implemented today are 100% compatible with Enterprise MCP features tomorrow.
Four concrete actions that move from conversation to committed prototype by end of Day 2. Each one is independently valuable — any single step creates compounding return.
Stand up the low-lift ETL that lands Fullstory behavioral events into Gusto's Snowflake environment. Minimal engineering work. Once live, 6 years of behavioral history is immediately queryable.
Engineering review of github.com/fullstorydev/fs-skills and lexicon. Identify the 3 tool definitions most relevant to Carrier Submissions.
Run the first natural language query against the GA-SUI cohort — via Cortex or Gusto's Cube semantic layer. Validate output structure. Confirm behavioral signal quality for Gumloop consumption.
Scope the batch audit design pattern against the March Carrier Submissions cohort. Define waste indicators with PI leads. Identify the top 3 Gumloop automation candidates by session volume.
Lane Greer · Sr. Manager, Strategic Solutions Specialists
lane@fullstory.com
The behavioral data is already captured. The Silver Schema is ready to share. The Lexicon pattern is documented and open. The only remaining step is the connection.
github.com/fullstorydev/fs-skills · github.com/fullstorydev/lexicon
Engineering defense points for teams evaluating whether to build vs. adopt.