Case Study · 01 — Prevalent AI
A zero-config data ingestion pipeline where a coordinated fleet of AI agents discovers, maps, validates, and commits third-party connector data directly into the Prevalent Knowledge Graph — replacing weeks of engineering work with an intent-driven, fully observable pipeline.
Studio replaces the engineering-heavy connector integration process with a coordinated fleet of AI agents — each named, observable, and scoped — that negotiate how external data maps into the Knowledge Graph schema in real time. Humans remain in the loop at defined ambiguity thresholds, not at every step.
Before Studio, adding a single connector required an engineer to manually analyze output schemas, author field-mapping scripts, and run multi-pass validations — a process taking 2–4 weeks per integration with high breakage risk.
The design challenge was not just to automate this, but to make the automation legible — so engineers could trust, audit, and override agent decisions without needing to re-do the work themselves.
Five specialized agents, each with a scoped role, named identity, and confidence scoring — designed to be observed, not trusted blindly.
Scans raw connector output to identify entity types, relationship candidates, and schema structure. First agent in every pipeline — generates the discovery manifest.
Proposes how discovered entities map to the existing Knowledge Graph ontology — matching types, properties, and relationship labels with a confidence score per mapping.
Builds field-level transformation rules — type coercions, normalizations, ID resolutions, and format conversions. Generates executable transform specs, not code.
Surfaces mapping disagreements between Architect and Weaver as Resolution Cards — showing both proposals, confidence levels, and reasoned recommendations for human review.
Runs integrity checks before any graph write — schema compliance, duplicate detection, relationship consistency, and graph impact analysis. Nothing commits without Steward's sign-off.
Each agent runs its own inference loop. Showing every intermediate thought would overwhelm users; hiding everything would destroy trust in the output.
Design ResponseAgents surface a single key signal per run — their top decision with confidence — behind a progressive disclosure layer. The full action log is always one click away, but never forced.
When Arbiter fires, it means two agents disagree. In most systems this is treated as a failure. In Studio, it means the data has genuine ambiguity that a human should weigh in on.
Design ResponseConflict Resolution Cards present both proposals side-by-side with confidence scores, agent reasoning, and downstream graph impact. The human chooses — not accepts a "best guess".
Interrupting too often defeats the purpose of agentic automation. Interrupting too rarely creates a false sense of safety and hidden errors in the graph.
Design ResponseAdmins configure a per-pipeline confidence threshold (default: 80%). Agents below this threshold pause and create a Review Item rather than proceeding. High-confidence runs commit automatically with a full audit trail.
Graph mutations are persistent. An agent making a wrong high-confidence mapping could silently corrupt relationships across thousands of connected entities.
Design ResponseEvery ingestion run is a versioned transaction. The graph maintains a full history of agent-authored commits. Rollback is a single button — not a database operation — and shows a diff of what will be undone before confirming.
Every entity in the Knowledge Graph carries a trust record — which connector sourced it, which agent mapped it, and what confidence tier it was committed at. This isn't metadata. It's a design primitive that shapes how analysts use the data.
Name every agent. Never call them "the system" or "AI".
When something is wrong, users need to know which agent was responsible. Named agents can be individually paused, re-run, or replaced — anonymous systems cannot. Identity creates accountability.
Confidence is always visible. No binary pass/fail states.
A mapping that passes at 65% confidence is not the same as one at 97%. Showing the number changes user behavior — they scrutinize the right things. Binary states create false security and overrides happen blindly.
Preview graph changes before any commit is allowed.
The Knowledge Graph is a shared resource. A single ingestion run might create, modify, or re-link thousands of entities. Users need to see the blast radius before they approve — not after.
Drift detection is ambient, not on-demand.
Connectors change schemas without warning. If we only check at ingestion time, silent drift corrupts the graph between runs. A background Drift Sentinel agent monitors continuously and surfaces changes before they cause damage.
Features we prototyped and chose to hold — not because they were impossible, but because shipping them without the right trust foundation would have undermined the system's core value.
Agents writing directly to the graph without human review. The speed gain wasn't worth the trust risk in early adoption — reversibility needs to be demonstrated before it can be removed.
Using mapping learnings from one customer's ingestion to bootstrap another's. Technically straightforward; legally and ethically complex. Tabled until data isolation guarantees were airtight.
Letting users write natural language instructions that modify agent behavior mid-run. Powerful but unpredictable. Held in favor of a structured intent-field with validated options.