Context
A US-based restaurant intelligence platform was running a 200-person manual research operation to compile competitive intelligence reports. Each report took approximately one month to complete, involved multiple handoffs, and had no consistent quality baseline. The client needed to collapse this workflow into an AI-driven pipeline without losing data quality or auditability — and with hard cost ceilings per pipeline run.
The Problem
- • 200-person workflow — manual research, data entry, and report compilation with no standardization
- • 1-month cycle time — too slow for competitive intelligence in a fast-moving market
- • No quality baseline — output quality varied by researcher, with no systematic validation
- • Unpredictable costs — manual workflows made cost-per-report impossible to forecast
Architecture Decision
Three-agent orchestrated pipeline with constrained identities, cost guardrails per agent and per pipeline run, and full audit observability. Each agent had a single responsibility with defined input/output contracts.
Scrapes, normalizes, and validates source data from multiple restaurant industry databases. Constrained to read-only access. Cost ceiling per run.
Processes normalized data into competitive intelligence insights. Applies classification models and trend detection. No external write access.
Compiles analysis into structured reports with confidence scores. Human review gate before client delivery. Full provenance chain from source to output.
Governance Controls
Lesson
Agent consolidation is not about replacing people with AI. It’s about replacing unstructured manual processes with governed automated pipelines. The 200-to-3 reduction worked because each agent had a constrained identity, a cost ceiling, and a quality gate. Without governance infrastructure, this would have been a 200-person workflow replaced by an unauditable black box. The plumbing made the difference.